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  • 1.
    Adaldo, Antonio
    et al.
    Department of Automatic Control, School of Electrical Engineering, KTH Royal Institute of Technology.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Dimarogonas, Dimos V.
    Department of Automatic Control, School of Electrical Engineering, KTH Royal Institute of Technology.
    Johansson, Karl H.
    Department of Automatic Control, School of Electrical Engineering, KTH Royal Institute of Technology.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Cooperative coverage for surveillance of 3D structures2017In: IEEE International Conference on Intelligent Robots and Systems, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1838-1845, article id 8205999Conference paper (Refereed)
    Abstract [en]

    In this article, we propose a planning algorithm for coverage of complex structures with a network of robotic sensing agents, with multi-robot surveillance missions as our main motivating application. The sensors are deployed to monitor the external surface of a 3D structure. The algorithm controls the motion of each sensor so that a measure of the collective coverage attained by the network is nondecreasing, while the sensors converge to an equilibrium configuration. A modified version of the algorithm is also provided to introduce collision avoidance properties. The effectiveness of the algorithm is demonstrated in a simulation and validated experimentally by executing the planned paths on an aerial robot.

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  • 2.
    Carlbaum, Erik
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Koval, Anton
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Towards Robust Localization Deep Feature Extraction by CNN2020In: Proceedings: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2020, p. 807-812Conference paper (Refereed)
    Abstract [en]

    Robust localization is a fundamental capability to increase the autonomy levels of robotic platforms. A core processing step in vision based odometry methods is the extraction and tracking of distinctive features in the image frame. Nevertheless, when deploying robots in challenging environments like underground tunnels, the sensor measurements are noisy with lack of information due to low light conditions, introducing a bottleneck for feature detection methods. This paper proposes a deep classifier Convolutional Neural Network (CNN) architecture to retain detailed and noise tolerant feature maps from RBG images, establishing a novel feature tracking scheme in the context of localization. The proposed method is feeding the RGB image into the AlexNet or VGG-16 network and extracts a feature map at a specific layer. This feature map consists of feature points which are then paired between frames resulting in a discrete vector field of feature change. Finally, the proposed method is evaluated with RGB camera footage of the Micro Aerial Vehicle (MAV) flights in dark underground mines and the performance is compared with existing feature extraction methods, while the noise is added to the images.

  • 3.
    Eleftheroglou, Nick
    et al.
    Faculty of Aerospace Engineering, TU Delft, the Netherlands.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Loutas, Theodoros
    Department of Mechanical Engineering & Aeronautics, University of Patras, Greece.
    Karvelis, Petros
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, George
    Department of Mechanical Engineering & Aeronautics, University of Patras, Greece.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Zarouchas, Dimitrios
    Faculty of Aerospace Engineering, TU Delft, the Netherlands.
    Intelligent data-driven prognostic methodologies for the real-time remaining useful life until the end-of-discharge estimation of the Lithium-Polymer batteries of unmanned aerial vehicles with uncertainty quantification2019In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 254, article id 113677Article in journal (Refereed)
    Abstract [en]

    In this paper, the discharge voltage is utilized as a critical indicator towards the probabilistic estimation of the Remaining Useful Life until the End-of-Discharge of the Lithium-Polymer batteries of unmanned aerial vehicles. Several discharge voltage histories obtained during actual flights constitute the in-house developed training dataset. Three data-driven prognostic methodologies are presented based on state-of-the-art as well as innovative mathematical models i.e. Gradient Boosted Trees, Bayesian Neural Networks and Non-Homogeneous Hidden Semi Markov Models. The training and testing process of all models is described in detail. Remaining Useful Life prognostics in unseen data are obtained from all three methodologies. Beyond the mean estimates, the uncertainty associated with the point predictions is quantified and upper/lower confidence bounds are also provided. The Remaining Useful Life prognostics during six random flights starting from fully charged batteries are presented, discussed and the pros and cons of each methodology are highlighted. Several special metrics are utilized to assess the performance of the prognostic algorithms and conclusions are drawn regarding their prognostic capabilities and potential.

  • 4.
    Eleftheroglou, Nick
    et al.
    Faculty of Aerospace Engineering, Delft University of Technology, The Netherlands.
    Zarouchas, Dimitrios
    Faculty of Aerospace Engineering, Delft University of Technology, The Netherlands.
    Loutas, Theodoros
    Department of Mechanical Engineering & Aeronautics, University of Patras, Greece.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, George
    Department of Mechanical Engineering & Aeronautics, University of Patras, Greece.
    Karvelis, Petros
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Benedictus, Rinze
    Faculty of Aerospace Engineering, Delft University of Technology, The Netherlands.
    Real time Diagnostics and Prognostics of UAV Lithium-Polymer Batteries2019In: Proceedings of the Annual Conference of the Prognostics and Health Management Society 2019 / [ed] N. Scott Clements, Prognostics and Health Management Society , 2019Conference paper (Other academic)
    Abstract [en]

    This paper examines diagnostics and prognostics of Lithium-Polymer (Li-Po) batteries for unmanned aerial vehicles (UAVs). Several discharge voltage histories obtained during actual indoor flights constitute the training data for a data-driven approach, utilizing the Non-Homogenous Hidden Semi Markov model (NHHSMM). NHHSMM is a suitable candidate as it has a rich mathematical structure, which is capable of describing the discharge process of Li-Po batteries and providing diagnostic and prognostic measures. Diagnostics and prognostics in unseen data are obtained and compared with the actual remaining flight time in order to validate the effectiveness of the selected model.

  • 5.
    Fresk, Emil
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Halén, Erik
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Reduced complexity calibration of MEMS IMUs2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1316-1320, article id 7984300Conference paper (Refereed)
    Abstract [en]

    In this article a reduced complexity calibration method for Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Units (IMUs) will be presented, which does not need the rotating reference tables, commonly used in the gyroscope calibration. As it will be presented, in the proposed novel scheme fixed angle rotations have been utilized to observe the integral of the gyroscope signals to find the corresponding sensitivity, axis misalignment and acceleration sensitivity matrices. This appraoch has the significant merit of high norm accuracy, easiness of use, low cost and simplicity in construction, thus allowing anyone with a basic electronics knowledge to calibrate an IMU.

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  • 6.
    Jafari, Hedyeh
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On the Fear of Falling Detection by Moving Horizon Estimation2020In: 21th IFAC World Congress / [ed] Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, Elsevier, 2020, p. 16512-16517Conference paper (Refereed)
    Abstract [en]

    Fear of falling (FoF) is a major health problem, especially in elders, which can lead to falls, injury, loss of independence, and premature needs of nursing and assistance. However, most of the studies have focused on the psychological aspect of the FoF and there is a significant lack of technological assistance and methodology to detect and eliminate the effects of this fear on maintaining balance. In this article, we propose a novel method to detect the FoF as a quantitative signal. In our proposed novel approach, fear is considered as an internal disturbance inside a Central Nervous System (CNS) that can affect the generated output torque to each joint of the psychical body. By assuming the human body in a quiet stance, as an inverted pendulum model, this disturbance signal is estimated by Moving Horizon Estimation (MHE). For this purpose, the body kinetics and kinematics measurements of forty-five subjects during upright stance trails, as well as the psychological FoF falls efficacy test, were collected and utilized for the estimation and validation of the results. The experimental results show that the subjects with FoF present a higher variation in the estimated signal. This method can sufficiently detect the FoF by the posturographic and motion data, which can be utilized on the future assistive devices for prevention and treatment of the FoF and falls.

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  • 7.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Fresk, Emil
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Towards Visual Inspection of Wind Turbines: A Case of Visual Data Acquisition using Autonomous Aerial Robots2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 181650-181661Article in journal (Refereed)
    Abstract [en]

    This article presents a novel framework for acquiring visual data around 3D infrastructures, by establishing a team of fully autonomous Micro Aerial Vehicles (MAVs) with robust localization, planning and perception capabilities. The proposed aerial system reaches high level of autonomy on a large scale, while pushing to the boundaries the real life deployment of aerial robotics. In the presented approach, the MAVs deployed around the structure rely only on their onboard computer and sensory systems. The developed framework envisions a modular system, combining open research challenges in the fields of localization, path planning and mapping, with an overall capability for a fast on site deployment and a reduced execution time that can repeatably perform the mission according to the operator needs. The architecture of the established system includes: 1) a geometry-based path planner for coverage of complex structures by multiple MAVs, 2) an accurate yet flexible localization component, which provides an accurate pose estimation for the MAVs by utilizing an Ultra Wideband fused inertial estimation scheme, and 3) visual data post-processing scheme for the 3D model building. The performance of the proposed framework has been experimentally demonstrated in multiple realistic outdoor field trials, all focusing on the challenging structure of a wind turbine as the main test case. The successful experimental results, depict the merits of the proposed autonomous navigation system as the enabling technology towards aerial robotic inspectors.

  • 8.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karvelis, Petros
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, 91109.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Vision-driven NMPC for Autonomous Aerial Navigation in Subterranean EnvironmentsManuscript (preprint) (Other academic)
    Abstract [en]

    This work establishes a novel robocentric Non-linear Model Predictive Control (NMPC) framework for fast fully autonomous navigation of quadrotors in featureless dark tunnel environments. Additionally, this work leverages the processing of a single camera to generate direction commands along the tunnel axis, while regulating the platform's altitude.  The extracted visual dynamics are coupled in the sequel with the NMPC problem,  structured around the Proximal Averaged Newton-type method for Optimal Control (PANOC), which is a fast numerical optimization method that is not sensitive to ill conditioning and is suitable for embedded NMPC implementations. Multiple fully realistic simulation results demonstrate the effectiveness of the proposed method in challenging environments.

  • 9.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karvelis, Petros
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, 91109.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments2020In: 21th IFAC World Congress / [ed] Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, Elsevier, 2020, p. 9288-9294Conference paper (Refereed)
    Abstract [en]

    This work establishes a novel robocentric Non-linear Model Predictive Control (NMPC) framework for fast fully autonomous navigation of quadrotors in featureless dark tunnel environments. Additionally, this work leverages the processing of a single camera to generate direction commands along the tunnel axis, while regulating the platform’s altitude. The extracted visual dynamics are coupled in the sequel with the NMPC problem, structured around the Proximal Averaged Newton-type method for Optimal Control (PANOC), which is a fast numerical optimization method that is not sensitive to ill conditioning and is suitable for embedded NMPC implementations. Multiple fully realistic simulation results demonstrate the effectiveness of the proposed method in challenging environments.

  • 10.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karvelis, Petros S.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, 91109.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Towards Autonomous Aerial Scouting Using Multi-Rotors in Subterranean Tunnel Navigation2021In: IEEE Access, E-ISSN 2169-3536, Vol. 9, p. 66477-66485Article in journal (Refereed)
    Abstract [en]

    This work establishes a robocentric framework around a non-linear Model Predictive Control (NMPC) for autonomous navigation of quadrotors in tunnel-like environments. The proposed framework enables obstacle free navigation capabilities for resource constraint platforms in areas with critical challenges including darkness, textureless surfaces as well as areas with self-similar geometries, without any prior knowledge. The core contribution of the proposed framework stems from the merging of perception dynamics in a model-based optimization approach, aligning the vehicles heading to the tunnels’ open space expressed in the x axis coordinate in the image frame of the most distant area. Moreover, the aerial vehicle is considered as a free-flying object that plans its actions using egocentric onboard sensors. The proposed method can be deployed in both fully illuminated indoor corridors or featureless dark tunnels, leveraging visual processing from either RGB-D or monocular sensors for generating direction commands to keep flying in the proper direction. Multiple experimental field trials demonstrate the effectiveness of the proposed method in challenging environments.

  • 11.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Castaño, Miguel
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Karvelis, Petros
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Where to look: a collection of methods for MAV heading correction in underground tunnels2020In: IET Image Processing, ISSN 1751-9659, E-ISSN 1751-9667, Vol. 14, no 10, p. 2020-2027Article in journal (Refereed)
    Abstract [en]

    Degraded Subterranean environments are an attractive case for miniature aerial vehicles, since there is a constant need to increase the safety operations in underground mines. The starting point for integrating aerial vehicles in the mining process is the capability to reliably navigate along tunnels. Inspired by recent advancements, this paper presents a collection of different, experimentally verified, methods tackling the problem of MAVs heading regulation while navigating in dark and textureless tunnel areas. More specifically, four different methods are presented in this work with the common goal to identify open space in the tunnel and align the MAV heading using either visual sensor in methods a) single image depth estimation, b) darkness contour detection, c) Convolutional Neural Network (CNN) regression and 2D Lidar sensor in method d) range geometry. For the works a)-c) the dark scene in the middle of the tunnel is considered as open space and is processed and converted to yaw rate command, while d) examines the geometry of the range measurements to calculate the yaw rate command. Experimental results from real underground tunnel demonstrate the performance of the methods in the field, while setting the ground for further developments in the aerial robotics community.

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  • 12.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Fresk, Emil
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Aerial imaging and reconstruction of infrastructures by UAVs2020In: Imaging and Sensing for Unmanned Aircraft Systems Volume 2: Deployment and Applications, Institution of Engineering and Technology , 2020, p. 157-176Chapter in book (Other academic)
    Abstract [en]

    This chapter presents a compilation of experimental field trials aiming vision-based reconstruction of large-scale infrastructures using micro aerial vehicles (MAVs). The main focus of this study is on the sensor selection, the data-set generation and on the computer vision algorithms for generating three-dimensional (3D) models. In general, MAVs are distinguished for their ability to fly at various speeds, to stabilise their position and to perform manoeuvres close to large-scale infrastructures. The aforementioned merits constitute aerial robots a highly paced evolving robotic platform for infrastructure inspection and maintenance tasks. Different MAV solutions with task-oriented sensory modalities can be developed to address unique tasks, such as 3D modelling of infrastructures. In this chapter, aerial agents navigate around/along different environments, while collecting visual data for post-processing using structure from motion (SfM) and multi-view stereo (MVS) techniques to generate 3D models [1, 2]. The proposed framework has been successfully experimentally demonstrated in real indoor, outdoor and subterranean environments.

  • 13.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Fresk, Emil
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Cooperative UAVs as a Tool for Aerial Inspection of Large Scale Aging Infrastructure2018In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Piscataway, NJ: IEEE, 2018, p. 5040-5040Conference paper (Refereed)
    Abstract [en]

    This work presents an aerial tool towards the autonomous cooperative coverage and inspection of a large scale 3D infrastructure using multiple Unmanned Aerial Vehicles (UAVs). In the presented approach the UAVs are relying only on their onboard computer and sensory system, deployed for inspection of the 3D structure. In this application each agent covers a different part of the scene autonomously, while avoiding collisions. The autonomous navigation of each platform on the designed path is enabled by the localization system that fuses Ultra Wideband with inertial measurements through an Error- State Kalman Filter. The visual information collected from the aerial team is collaboratively processed to create the 3D model. The performance of the overall setup has been experimentally evaluated in realistic wind turbine inspection experiments, providing dense 3D reconstruction of the inspected structures.

  • 14.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Towards Autonomous Surveying of Underground Mine using MAVs2019Conference paper (Refereed)
    Abstract [en]

    Micro Aerial Vehicles (MAVs) are platforms that received great attention during the last decade. Recently, the mining industry has been considering the usage of aerial autonomous platforms in their processes. This article initially investigates potential application scenarios for this technology in mining. Moreover, one of the main tasks refer to surveillance and maintenance of infrastructure assets. Employing these robots for underground surveillance processes of areas like shafts, tunnels or large voids after blasting, requires among others the development of elaborate navigation modules. This paper proposes a method to assist the navigation capabilities of MAVs in challenging mine environments, like tunnels and vertical shafts. The proposed method considers the use of Potential Fields method, tailored to implement a sense-and-avoid system using a minimal ultrasound-based sensory system. Simulation results demonstrate the effectiveness of the proposed strategy.

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  • 15.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Dynamic visual sensing based on MPC controlled UAVs2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1201-1206, article id 7984281Conference paper (Refereed)
    Abstract [en]

    This article considers the establishment of a dynamic visual sensor from monocular cameras to enable a reconfigurable environmental perception. The cameras are mounted on Micro Aerial Vehicles (MAV) which are coordinated by a Model Predictive Control (MPC) scheme to retain overlapping field of views and form a global sensor with varying baseline. The specific merits of the proposed scheme are: a) the ability to form a configurable stereo rig, according to the application needs, and b) the simple design, the reduction of the payload and the corresponding cost. Moreover, the proposed configurable sensor provides a glpobal 3D reconstruction of the surrounding area, based on a modified Structure from Motion approach. The efficiency of the suggested flexible visual sensor is demonstrated in simulation results that highlight the novel concept of cooperative flying cameras and their 3D reconstruction capabilities

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  • 16.
    Karlsson, Samuel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Monocular Vision-based Obstacle Avoidance Scheme for Micro Aerial Vehicle Navigation2021In: 2021 The International Conference on Unmanned Aircraft Systems (ICUAS’21), IEEE, 2021, p. 1321-1327Conference paper (Refereed)
    Abstract [en]

    One of the challenges in deploying Micro Aerial Vehicless (MAVs) in unknown environments is the need of securing for collision-free paths with static and dynamic obstacles. This article proposes a monocular vision-based reactive planner for MAVs obstacle avoidance. The avoidance scheme is structured around a Convolution Neural Network (CNN) for object detection and classification (You Only Lock Once (YOLO)), used to identify the bounding box of the objects of interest in the image plane. Moreover, the YOLO is combined with a Kalman filter to robustify the object tracking, in case of losing the boundary boxes, by estimating their position and providing a fixed rate estimation. Since MAVs are fast and agile platforms, the object tracking should be performed in real-time for the collision avoidance. By processing the information of the bounding boxes with the image field of view and applying trigonometry operations, the pixel coordinates of the object are translated to heading commands, which results to a collision free maneuver. The efficacy of the proposed scheme has been extensively evaluated in the Gazebo simulation environment, as well as in experimental evaluations with a MAV equipped with a monocular camera.

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  • 17.
    Karvelis, Petros
    et al.
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus, Arta, Greece.
    Röijezon, Ulrik
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Faleij, Ragnar
    Luleå University of Technology, Department of Health Sciences, Health and Rehabilitation.
    Georgoulas, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A Laser Dot Tracking Method for the Assessment of Sensorimotor Function of the Hand2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway. NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 217-222, article id 7984121Conference paper (Refereed)
    Abstract [en]

    Assessment of sensorimotor function is crucial during the rehabilitation process of various physical disorders, including impairments of the hand. While moment performance can be accurately assessed in movement science laboratories involving highly specialized personnel and facilities there is a lack of feasible objective methods for the general clinic. This paper describes a novel approach to sensorimotor assessment using an intuitive test and a specifically tailored image processing pipeline for the quantification of the test. More specifically the test relies on the patient being instructed on following a zig-zag pattern using a handled laser pointer. The movement of the pointer is tracked using image processing algorithm capable of automating the whole procedure. The method has potential for feasible objective clinical assessment of the hand and other body parts

  • 18.
    Kominiak, Dariusz
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    MAV Development Towards Navigation in Unknown and Dark Mining Tunnels2020In: 2020 28th Mediterranean Conference on Control and Automation (MED), IEEE, 2020, p. 1015-1020Conference paper (Refereed)
    Abstract [en]

    The Mining industry considers the deployment of Micro Aerial Vehicles (MAVs) for autonomous inspection of tunnels and shafts to increase safety and productivity. However, mines are challenging and harsh environments that have a direct effect on the degradation of high-end and expensive utilized components over time. Inspired by this effect, this article presents a low cost and modular platform for designing a fully autonomous navigating MAVs without requiring any prior information from the surrounding environment. The design of the proposed aerial vehicle can be considered as a consumable platform that can be instantly replaced in case of damage or defect, thus comes into agreement with the vision of mining companies for utilizing stable aerial robots with reasonable cost. In the proposed design, the operator has access to all on-board data, thus increasing the overall customization of the design and the execution of the mine inspection mission. The MAVs platform has a software core based on Robot Operating System (ROS) operating on an Aaeon UP-Board, while it is equipped with a sensor suite to accomplish the autonomous navigation equally reliable when compared to high-end and expensive platforms.

  • 19.
    Kottayam Viswanathan, Vignesh
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Aerial infrastructures inspection2023In: Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications / [ed] George Nikolakopoulos, Sina Sharif Mansouri, Christoforos Kanellakis, Elsevier, 2023, p. 175-211Chapter in book (Other academic)
    Abstract [en]

    This chapter presents the application of autonomous Aerial Robotic Workers towards performing a visual inspection of 3D infrastructures by utilizing single and multiple Aerial Robotic Workers (ARWs). To address this problem, the developed framework combines the fundamental tasks of path planning, localization, and mapping, which are the essential components for autonomous robotic navigation systems. In the presented approach, the Unmanned Aerial Workers (ARWs) deployed for inspecting the structure rely only on their onboard computer and sensory system. Initially, the problem of path planner is discussed and mathematically formulated, leading to the development of a geometry-based approach for coverage of complex structures. The navigation of the platform is performed through the localization component, which provides accurate pose estimation for the vehicle using a visual-inertial estimation scheme. During the coverage mission, the agents collect image data for post-processing and mapping using Visual SLAM and Structure from Motion techniques. The performance of the proposed framework has been experimentally evaluated in multiple indoor and realistic outdoor infrastructure inspection experiments, depicting the merits of the autonomous navigation system (path planning and localization) and 3D model building of the inspected object and infrastructure.

  • 20.
    Koval, Anton
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karlsson, Samuel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Tevetzidis, Ilias
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Haluska, Jakub
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, 91109, United States of America.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Dataset collection from a SubT environment2022In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 155, article id 104168Article in journal (Refereed)
    Abstract [en]

    This article presents a dataset collected from the subterranean (SubT) environment with a current state-of-the-art sensors required for autonomous navigation. The dataset includes sensor measurements collected with RGB, RGB-D, event-based and thermal cameras, 2D and 3D lidars, inertial measurement unit (IMU), and ultra wideband (UWB) positioning systems which are mounted on the mobile robot. The overall sensor setup will be referred further in the article as a data collection platform. The dataset contains synchronized raw data measurements from all the sensors in the robot operating system (ROS) message format and video feeds collected with action and 360 cameras. A detailed description of the sensors embedded into the data collection platform and a data collection process are introduced. The collected dataset is aimed for evaluating navigation, localization and mapping algorithms in SubT environments. This article is accompanied with the public release of all collected datasets from the SubT environment. Link: Dataset (C) 2022 The Author(s). Published by Elsevier B.V.

  • 21.
    Koval, Anton
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Machine learning for ARWs2023In: Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications / [ed] George Nikolakopoulos, Sina Sharif Mansouri, Christoforos Kanellakis, Elsevier, 2023, p. 159-174Chapter in book (Other academic)
    Abstract [en]

    Navigation in underground mine environments is a challenging area for the aerial robotic workers. Mines usually have complex geometries, including multiple crossings with different tunnels. Moreover, improving the safety of mines requires drones to be able to detect human workers. Thus, in this Chapter, we introduce frameworks for junction and human detection in the underground mine environments.

  • 22.
    Koval, Anton
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Aerial Thermal Image based Convolutional Neural Networks for Human Detection in SubT Environments2021In: 2021 The International Conference on Unmanned Aircraft Systems (ICUAS’21), IEEE, 2021, p. 536-541Conference paper (Refereed)
    Abstract [en]

    This article proposes a novel strategy for detecting humans in harsh Sub-terranean (SubT) environments, with a thermal camera mounted on an aerial platform, based on the AlexNet Convolutional Neural Network (CNN). A transfer learning framework will be utilized for detecting the humans, where the aerial thermal images are fed to the trained network, which binary classifies them image content into two categories: a) human, and b) no human. Moreover, the AlexNet based framework is compared with two related popular CNN approaches as the GoogleNet and the Inception3Net. The efficacy of the proposed scheme has been experimentally evaluated through multiple data-sets, collected from a FLIR thermal camera during flights on an underground mining environment, fully demonstrating the performance and merits of the proposed module.

  • 23.
    Koval, Anton
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Multi-Agent Collaborative Path Planning Based on Staying Alive Policy2020In: Robotics, E-ISSN 2218-6581, Vol. 9, no 4, article id 101Article in journal (Refereed)
    Abstract [en]

    Modern mobile robots tend to be used in numerous exploration and search and rescue applications. Essentially they are coordinated by human operators and collaborate with inspection or rescue teams. Over the time, robots became more advanced and capable for various autonomous collaborative scenarios. Recent advances in the field of collaborative exploration and coverage provide different approaches to solve this objective. Thus scope of this article is to present a novel collaborative approach for multi-agent coordination in exploration and coverage of unknown complex indoor environments. Fundamentally, the task of collaborative exploration can be divided into two core components. The principal one is a sensor based exploration scheme that aims to guarantee complete area exploration and coverage. The second core component proposed is a staying alive policy that takes under consideration the battery charge level limitation of the agents. From this perspective the path planner assigns feasible tasks to each of the agents, including the capability of providing reachable, collision free paths. The overall efficacy of the proposed approach was extensively evaluated by multiple simulation results in a complex unknown environments.

  • 24.
    Koval, Anton
    et al.
    Department of Automation and Computer Integrated Technologies, Zhytomyr State Technological University, Ukraine.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Online Multi-Agent Based Cooperative Exploration and Coverage in Complex Environment2019In: 2019 18th European Control Conference (ECC), IEEE, 2019, p. 3964-3969Conference paper (Refereed)
    Abstract [en]

    In this article, an online collaborative exploration and coverage method is proposed for the unknown complex environment with multiple agents. The exploration and coverage is based on Boustrophedon motion, while the detection conditions for backtracking points have been modified based on mission requirements, the battery charge level of each agent is considered to reduce agent loss, and collision free paths are generated. The proposed method is evaluated in simulation, where complex environment with multiple branches is explored by multiple agents.

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  • 25.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, Pasadena, CA, 91109, USA.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    COMPRA: A COMPact Reactive Autonomy Framework for Subterranean MAV Based Search-And-Rescue Operations2022In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 105, no 3, article id 49Article in journal (Refereed)
    Abstract [en]

    This work establishes COMPRA, a compact and reactive autonomy framework for fast deployment of Micro Aerial Vehicles (MAVs) in subterranean Search-and- Rescue (SAR) missions. A COMPRA-enabled MAV is able to autonomously explore previously unknown areas while specific mission criteria are considered e.g. an object of interest is identified and localized, the remaining useful battery life, the overall desired exploration mission duration. The proposed architecture follows a low-complexity algorithmic design to facilitate fully on-board computations, including nonlinear control, state-estimation, navigation, exploration behavior and object localization capabilities. The framework is mainly structured around a reactive local avoidance planner, based on enhanced Potential Field concepts and using instantaneous 3D pointclouds, as well as a computationally efficient heading regulation technique, based on depth images from an instantaneous camera stream. Those techniques decouple the collision-free path generation from the dependency of a global map and are capable of handling imprecise localization occasions. Field experimental verification of the overall architecture is performed in relevant unknown Global Positioning System (GPS)-denied environments.

  • 26.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 USA.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nonlinear MPC for Collision Avoidance and Control of UAVs With Dynamic Obstacles2020In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 5, no 4, p. 6001-6008Article in journal (Refereed)
    Abstract [en]

    This article proposes a Novel Nonlinear Model Predictive Control (NMPC) for navigation and obstacle avoidance of an Unmanned Aerial Vehicle (UAV). The proposed NMPC formulation allows for a fully parametric obstacle trajectory, while in this article we apply a classification scheme to differentiate between different kinds of trajectories to predict futureobstacle positions. The trajectory calculation is done from an initial condition, and fed to the NMPC as an additional input.The solver used is the nonlinear, non-convex solver Proximal Averaged Newton for Optimal Control (PANOC) and its as-sociated software OpEn (Optimization Engine), in which weapply a penalty method to properly consider the obstacles and other constraints during navigation. The proposed NMPC scheme allows for real-time solutions using a sampling time of 50 ms and a two second prediction of both the obstacle trajectory and the NMPC problem, which implies that the scheme can be considered as a local path-planner. This paper will present the NMPC cost function and constraint formulation, as well as the methodology of dealing with the dynamic obstacles. We include multiple laboratory experiments to demonstrate the efficacy ofthe proposed control architecture, and to show that the proposed method delivers fast and computationally stable solutions to the dynamic obstacle avoidance scenarios.

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  • 27.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Haluška, Jakub
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Reactive Navigation of an Unmanned Aerial Vehicle With Perception-Based Obstacle Avoidance Constraints2022In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 30, no 5, p. 1847-1862Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a reactive constrained navigation scheme, with embedded obstacles avoidance for an unmanned aerial vehicle (UAV), for enabling navigation in obstacle-dense environments. The proposed navigation architecture is based on a nonlinear model predictive controller (NMPC) and utilizes an onboard 2-D LiDAR to detect obstacles and translate online the key geometric information of the environment into parametric constraints for the NMPC that constrain the available position space for the UAV. This article focuses also on the real-world implementation and experimental validation of the proposed reactive navigation scheme, and it is applied in multiple challenging laboratory experiments, where we also conduct comparisons with relevant methods of reactive obstacle avoidance. The solver utilized in the proposed approach is the optimization engine (OpEn) and the proximal averaged Newton for optimal control (PANOC) algorithm, where a penalty method is applied to properly consider obstacles and input constraints during the navigation task. The proposed novel scheme allows for fast solutions while using limited onboard computational power, which is a required feature for the overall closed-loop performance of a UAV and is applied in multiple real-time scenarios. The combination of built-in obstacle avoidance and real-time applicability makes the proposed reactive constrained navigation scheme an elegant framework for UAVs that is able to perform fast nonlinear control, local path planning, and obstacle avoidance, all embedded in the control layer.

  • 28.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kottayam Viswanathan, Vignesh
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    ARW deployment for subterranean environments2023In: Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications / [ed] George Nikolakopoulos, Sina Sharif Mansouri, Christoforos Kanellakis, Elsevier, 2023, p. 213-243Chapter in book (Other academic)
    Abstract [en]

    This chapter will present the application of deployment of full autonomous Aerial Robotic Workers for inspection and exploration tasks in Subterranean environments. The framework shown will focus on the navigation, control, and perception capabilities of resource-constrained aerial platforms, contributing to the development of consumable scouting robotic platforms for real-life applications in extreme environments. In the approach, the aerial platform will be treated as a floating object, commanded by a velocity controller on the x-y axes, a height controller, as well as a heading correction module aligning the platform with the mining tunnel open space. Multiple experimentally verified methods regarding the heading correction module, for dark environments with limited texture, using either a visual camera or a 2D LiDAR presented in real mining environments are presented.

  • 29.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Collision Free Path Planning based on Local 2D Point-Clouds for MAV Navigation2020In: 2020 28th Mediterranean Conference on Control and Automation (MED), IEEE, 2020, p. 538-543Conference paper (Refereed)
    Abstract [en]

    The usage of Micro Aerial Vehicles (MAVs) in different applications is gaining attention, however one of the main challenges is to provide collision free paths, despite the uncertainties in localization, mapping, or path planning. This article proposes a novel collision-free path planner for MAV navigation in confined environments, while not being dependent on the information of the localization, only relying on 2D local point-cloud data. The proposed backup path planner generates velocity commands for a trajectory-following controller, while guaranteeing a safety distance from all points in the local-point-cloud. The proposed method considers the kinematics of the MAV and can be extended to any robotics application, such as ground vehicles. The proposed method is evaluated in a Gazebo simulation environment and successfully provides a collision-free navigation.

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  • 30.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Non-linear MPC based Navigation for Micro Aerial Vehicles in Constrained Environments2020In: European Control Conference 2020, IEEE, 2020, p. 837-842Conference paper (Refereed)
    Abstract [en]

    This article establishes a novel Non linear Model Predictive Control (MPC) scheme for the navigation of a MAV (Micro Aerial Vehicle) in constrained environments, such as narrow passages, multi-obstacle populated spaces and tight openings. The proposed NMPC optimization framework is based on the Proximal Averaged Newton type method for Optimal Control (PANOC) and has the merit to employ a penalty method for the proper consideration of the obstacles and other environmental constraints during the navigation. The proposed scheme has the ability to be a fast solution for the navigation of MAVs that can be directly applied online and thus it is creating a powerful navigation framework for demanding flights. For achieving such an agile and fast aerial navigation, the article will also present the proposed penalty creation methodology for dealing with the obstacle avoidance and the space constraint navigation. Finally, the efficacy of the proposed scheme will be demonstrated by multiple simulation results under constrained and demanding environments.

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  • 31.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Sopasakis, Pantelis
    School of Electronics, Electrical Engineering and Computer Science (EEECS), Queen’s University Belfast. Centre for Intelligent Autonomous Manufacturing Systems (i-AMS), United Kingdom.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Collision Avoidance for Multiple Micro Aerial Vehicles using Fast Centralized Nonlinear Model Predictive Control2020In: 21th IFAC World Congress / [ed] Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, Elsevier, 2020, p. 9303-9309Conference paper (Refereed)
    Abstract [en]

    This article proposes a novel control architecture using a centralized nonlinear model predictive control (CNMPC) scheme for controlling multiple micro aerial vehicles (MAVs). The control architecture uses an augmented state system to control multiple agents and performs both obstacle and collision avoidance. The optimization algorithm used is OpEn, based on the proximal averaged Newton type method for optimal control (PANOC) which provides fast convergence for non-convex optimization problems. The objective is to perform position reference tracking for each individual agent, while nonlinear constraints guarantee collision avoidance and smooth control signals. To produce a trajectory that satisfies all constraints a penalty method is applied to the nonlinear constraints. The efficacy of this proposed novel control scheme is successfully demonstrated through simulation results and comparisons, in terms of computation time and constraint violations, which are provided with respect to the number of agents.

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    lindqvist2020centralized
  • 32.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On Autonomous Deployment of Micro Aerial Field Vehicles2020Doctoral thesis, monograph (Other academic)
    Abstract [en]

    In this thesis, I am going to investigate the control, navigation and path planning frame-works forMicro Aerial Vehicles (MAVs), both mathematically and algorithmically. Inorder to deploy them in challenging real life applications and with a main focus on theunderground mine navigation and wind turbine inspection. As it will be presented, theobjective of the proposed modules is to provide to the robotics community a foundationon field robotics, which one can continue research on.Towards this envisioned aim, this thesis will establish the following main theoreticaland practical contributions: 1) the unifiedNonlinear Model Predictive Control (NMPC)framework for position-velocity reference tracking, while guaranteeing collision free pathsby considering obstacles. In this framework, theNMPCconsiders also the localizationuncertainties and tracks either position or velocity references, or none of them if neces-sary. The obstacles are extracted from 2D/3D point clouds and in the sequel are definedin local coordinates that result to their decoupling from localization uncertainties. Thus,the obstacle avoidance remains active and guarantees collision avoidance in all the cases.The proposed control architecture allows for a mission continuation by aMAVevenin the presence of a localization drift, providing recovery opportunities to the localiza-tion scheme in case of loop closure events or reaching feature-rich areas. 2) Introducinga baseline approach for the deployment of aerial underground scouts for subterraneanenvironments. The proposed framework focuses on the navigation, control and visioncapabilities of the aerial platforms with low-cost sensor suites, contributing significantlytowards real-life applications. The thesis proposes multiple methods for correcting theheading of theMAVtowards open spaces in featureless dark tunnel environments forfast fully autonomous navigation. 3) Developing a holistic approach to the problems of2D area coverage withMAVs for polygon areas, while considering the camera footprint.In the presented novel approach a 3Degree of Freedom (DoF)camera movement is con-sidered and the shortest path from the taking off to the landing station is generated,while covering the target area. 4) Finally, this thesis revisits theCollaborative CoveragePath Planning (C-CPP)problem for the inspection of complex infrastructures, and es-tablishes a theoretical framework, capable of offline providing a path for accomplishinga full coverage of the infrastructure with multipleMAVs.

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  • 33.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On Visual Area Coverage Using Micro Aerial Vehicles2018Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    The aim of this Licentiate is to advance the field of cooperative visual coverage path planners for multiple Micro Aerial Vehicles (MAVs), while aiming for their real life adoption towards the tasks of aerial infrastructure inspection. The fields that will be addressed are focusing in: a) the collaborative perception of the environment, b) the collaborative visual inspection, and c) the optimization of the aerial missions based on the remaining flying battery, camera constraints, coverage constraints and other real life mission induced constraints.

    Towards this envisioned aim, this Licentiate will present the following main theoretical contributions: a) centralized and distributed Model Predictive Control (MPC) schemes for the cooperative motion control of MAVs focusing in the establishing of a formation control architecture to enable a dynamic visual sensor from monocular cameras towards a reconfigurable environmental perception, b) revisiting the Cooperative Coverage Path Planning (C-CPP) problem for the inspection of complex infrastructures, c) developing a holistic approach to the problems of 2-D area coverage with MAVs for polygon areas, while considering the camera footprint, and d) designing of a scheme to estimate the Remaining Useful Life (RUL) of the battery during a flight mission, a fact that directly effects the flying capabilities of the MAVs. The theoretical contributions of this thesis have been extensively evaluated in simulation and real life large scale field trials, a direction that adds another contribution of the suggested framework towards the massive insertion of the aerial platforms as aerial tools in the close future.

    In the first part of this Licentiate, the vision, motivation, open challenges, contributions, and future works are discussed, while in the second part the full articles connected to the presented contributions in this Licentiate are presented in the annex.

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  • 34.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Castaño, Miguel
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Autonomous MAV Navigation in Underground Mines Using Darkness Contours Detection2019In: Computer Vision Systems: 12th International Conference, ICVS 2019 Thessaloniki, Greece, September 23–25, 2019 Proceedings / [ed] Dimitrios Tzovaras, Dimitrios Giakoumis, Markus Vincze, Antonis Argyros, Springer, 2019, p. 164-174Conference paper (Refereed)
    Abstract [en]

    This article considers a low-cost and light weight platform for the task of autonomous flying for inspection in underground mine tunnels. The main contribution of this paper is integrating simple, efficient and well-established methods in the computer vision community in a state of the art vision-based system for Micro Aerial Vehicle (MAV) navigation in dark tunnels. These methods include Otsu's threshold and Moore-Neighborhood object tracing. The vision system can detect the position of low-illuminated tunnels in image frame by exploiting the inherent darkness in the longitudinal direction. In the sequel, it is converted from the pixel coordinates to the heading rate command of the MAV for adjusting the heading towards the center of the tunnel. The efficacy of the proposed framework has been evaluated in multiple experimental field trials in an underground mine in Sweden, thus demonstrating the capability of low-cost and resource-constrained aerial vehicles to fly autonomously through tunnel confined spaces.

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  • 35.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On the covering of a polygonal region with fixed size rectangles with an application towards aerial inspection2017In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1316-1320, article id 7984300Conference paper (Refereed)
    Abstract [en]

    Unmanned Aerial Vehicles (UAVs) equipped with remote visual sensing can be used in wide range of applications. However, guaranteeing the full coverage of the area and translating this coverage in a path planning problem, it is a quite challenging task. Thus, in this article a well-known and well-investigated family of hard optimization problems, covering a polygonal region (target area) with fixed size rectangles (camera frustrum), is studied. The problem is formulated mathematically and solved using metaheuristic optimization algorithms. The proposed novel algorithmic scheme requires an a priori 2D model of the target area, while it tries to maximize the coverage with a minimum number of fixed size rectangles. Finally, multiple simulation results are presented that prove the efficacy of the proposed scheme

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  • 36.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Jafari, Hedyeh
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    External Force Estimation based on Nonlinear Moving Horizon Estimation for MAV Navigation2020In: European Control Conference 2020, IEEE, 2020, p. 1312-1317Conference paper (Refereed)
    Abstract [en]

    Deployment of the Micro Aerial Vehicle (MAV) in real-life applications poses multiple challenges, specially when there are external forces such as wind gust or interaction to the environment. Thus, this article proposes a novel Nonlinear Moving Horizon Estimation (NMHE) for estimating the external forces without adding extra sensor to the MAV or using external sensor for force measurements. The proposed method uses only the dynamic model, while avoiding the need to know aerodynamic models or other parameters of the MAV. The NMHE problem is solved using Proximal Averaged Newton-type method for Optimal Control (PANOC), which is a fast numerical optimization, completely matrix-free, not sensitive to ill conditioning, and involves only simple algebraic operations. The solver has the ability to provide the estimation of the states and external forces, such as wind at a rate of 5 ms, which can be used online for the MAV controller. The proposed method is evaluated in a closed loop simulation environment with position controller in present of measurement noise and varying external forces, while it results to the fast convergent of the state and external force estimations.

  • 37.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Fresk, Emil
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Cooperative coverage path planning for visual inspection2018In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 74, p. 118-131Article in journal (Refereed)
    Abstract [en]

    This article addresses the inspection problem of a complex 3D infrastructure using multiple Unmanned Aerial Vehicles (UAVs). The main novelty of the proposed scheme stems from the establishment of a theoretical framework capable of providing a path for accomplishing a full coverage of the infrastructure, without any further simplifications (number of considered representation points), by slicing it by horizontal planes to identify branches and assign specific areas to each agent as a solution to an overall optimization problem. Furthermore, the image streams collected during the coverage task are post-processed using Structure from Motion, stereo SLAM and mesh reconstruction algorithms, while the resulting 3D mesh can be used for further visual inspection purposes. The performance of the proposed Collaborative-Coverage Path Planning (C-CPP) has been experimentally evaluated in multiple indoor and realistic outdoor infrastructure inspection experiments and as such it is also contributing significantly towards real life applications for UAVs.

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  • 38.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Fresk, Emil
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Cooperative UAVs as a tool for Aerial Inspection of the Aging Infrastructure2017In: Field and Service Robotics: Results of the 11th International Conference / [ed] Marco Hutter, Roland Siegwart, Cham: Springer, 2017, p. 177-189Conference paper (Refereed)
    Abstract [en]

    This article presents an aerial tool towards the autonomous cooperative coverage and inspection of a 3D infrastructure using multiple Unmanned Aerial Vehicles (UAVs). In the presented approach the UAVs are relying only on their onboard computer and sensory system, deployed for inspection of the 3D structure. In this application each agent covers a different part of the scene autonomously, while avoiding collisions. The visual information collected from the aerial team is collaboratively processed to create the 3D model. The performance of the overall setup has been experimentally evaluated in a realistic outdoor infrastructure inspection experiments, providing sparse and dense 3D reconstruction of the inspected structures.

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  • 39.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Fresk, Emil
    WideFind AB, Aurorum 1C, Luleå SE-97775, Sweden.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Koval, Anton
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Sopasakis, Pantelis
    School of Electronics, Electrical Engineering and Computer Science (EEECS), Queen’s University Belfast. Centre for Intelligent Autonomous Manufacturing Systems (i-AMS), United Kingdom.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Subterranean MAV Navigation based on Nonlinear MPC with Collision Avoidance Constraints2020In: 21th IFAC World Congress / [ed] Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, Elsevier, 2020, p. 9650-9657Conference paper (Refereed)
    Abstract [en]

    Micro Aerial Vehicles (MAVs) navigation in subterranean environments is gaining attention in the field of aerial robotics, however there are still multiple challenges for collision free navigation in such harsh environments. This article proposes a novel baseline solution for collision free navigation with Nonlinear Model Predictive Control (NMPC). In the proposed method, the MAV is considered as a floating object, where the velocities on the x, y axes and the position on altitude are the references for the NMPC to navigate along the tunnel, while the NMPC avoids the collision by considering kinematics of the obstacles based on measurements from a 2D lidar. Moreover, a novel approach for correcting the heading of the MAV towards the center of the mine tunnel is proposed, while the efficacy of the suggested framework has been evaluated in multiple field trials in an underground mine in Sweden.

  • 40.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Towards MAV Navigation in Underground Mine Using Deep Learning2018In: IEEE ROBIO 2018, IEEE, 2018, p. 880-885Conference paper (Refereed)
    Abstract [en]

    The usage of Micro Aerial Vehicles (MAVs) is rapidly emerging in the mining industry to increase overall safety and productivity. However, the mine environment is especially challenging for the MAV's operation due to the lack of illumination, narrow passages, wind gusts, dust, and other factors that can affect the MAV's overall flying capability. This article presents a method to assist the navigation of MAVs by using a method from the field of Deep Learning (DL), while considering a low-cost platform without high-end sensor suits. The presented DL scheme can be further utilized as a supervised image classifier that has the ability to process the image frames from a single on-board camera and to provide mine tunnel wall collision prevention. The efficiency of the proposed scheme has been experimentally evaluated in two underground tunnel environments that were used for data collection, training, and corresponding testing under multiple flying scenarios with different cameras configurations and illuminations.

  • 41.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    2D visual area coverage and path planning coupled with camera footprints2018In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 75, p. 1-16Article in journal (Refereed)
    Abstract [en]

    Unmanned Aerial Vehicles (UAVs) equipped with visual sensors are widely used in area coverage missions. Guaranteeing full coverage coupled with camera footprint is one of the most challenging tasks, thus, in the presented novel approach a coverage path planner for the inspection of 2D areas is established, a 3 Degree of Freedom (DoF) camera movement is considered and the shortest path from the taking off to the landing station is generated, while covering the target area. The proposed scheme requires a priori information about the boundaries of the target area and generates the paths in an offline process. The efficacy and the overall performance of the proposed method has been experimentally evaluated in multiple indoor inspection experiments with convex and non convex areas. Furthermore, the image streams collected during the coverage tasks were post-processed using image stitching for obtaining a single overview of the covered scene.

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  • 42.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karvelis, Petros
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    MAV Navigation in Unknown Dark Underground Mines Using Deep Learning2020In: European Control Conference 2020, IEEE, 2020, p. 1943-1948Conference paper (Refereed)
    Abstract [en]

    This article proposes a Deep Learning (DL) method to enable fully autonomous flights for low-cost Micro Aerial Vehicles (MAVs) in unknown dark underground mine tunnels. This kind of environments pose multiple challenges including lack of illumination, narrow passages, wind gusts and dust. The proposed method does not require accurate pose estimation and considers the flying platform as a floating object. The Convolutional Neural Network (CNN) supervised image classifier method corrects the heading of the MAV towards the center of the mine tunnel by processing the image frames from a single on-board camera, while the platform navigates at constant altitude and desired velocity references. Moreover, the output of the CNN module can be used from the operator as means of collision prediction information. The efficiency of the proposed method has been successfully experimentally evaluated in multiple field trials in an underground mine in Sweden, demonstrating the capability of the proposed method in different areas and illumination levels.

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  • 43.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Deploying MAVs for autonomous navigation in dark underground mine environments2020In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 126, article id 103472Article in journal (Refereed)
    Abstract [en]

    Operating Micro Aerial Vehicles (MAVs) in subterranean environments is becoming more and more relevant in the field of aerial robotics. Despite the large spectrum of technological advances in the field, flying in such challenging environments is still an ongoing quest that requires the combination of multiple sensor modalities like visual/thermal cameras as well as 3D and 2D lidars. Nevertheless, there exist cases in subterranean environments where the aim is to deploy fast and lightweight aerial robots for area reckoning purposes after an event (e.g. blasting in production areas). This work proposes a novel baseline approach for the navigation of resource constrained robots, introducing the aerial underground scout, with the main goal to rapidly explore unknown areas and provide a feedback to the operator. The main proposed framework focuses on the navigation, control and vision capabilities of the aerial platforms with low-cost sensor suites, contributing significantly towards real-life applications. The merit of the proposed control architecture is that it considers the flying platform as a floating object, composing a velocity controller on the x, y axes and altitude control to navigate along the tunnel. Two novel approaches make up the cornerstone of the proposed contributions for the task of navigation: (1) a vector geometry method based on 2D lidar, and (2) a Deep Learning (DL) method through a classification process based on an on-board image stream, where both methods correct the heading towards the center of the mine tunnel. Finally, the framework has been evaluated in multiple field trials in an underground mine in Sweden.

  • 44.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Pourkamali-Anaraki, Farhad
    Department of Computer Science, University of Massachusetts Lowell, Lowell, MA 01854 USA.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory, California Institute of Technology Pasadena, Pasadena, CA 91109 USA.
    Burdick, Joel
    Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, CA 91125 USA.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A Unified NMPC Scheme for MAVs Navigation With 3D Collision Avoidance Under Position Uncertainty2020In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 5, no 4, p. 5740-5747Article in journal (Refereed)
    Abstract [en]

    This letter proposes a novel Nonlinear Model Predictive Control (NMPC) framework for Micro Aerial Vehicle (MAV) autonomous navigation in indoor enclosed environments. The introduced framework allows us to consider the nonlinear dynamics of MAVs, nonlinear geometric constraints, while guarantees real-time performance. Our first contribution is to reveal underlying planes within a 3D point cloud, obtained from a 3D lidar scanner, by designing an efficient subspace clustering method. The second contribution is to incorporate the extracted information into the nonlinear constraints of NMPC for avoiding collisions. Our third contribution focuses on making the controller robust by considering the uncertainty of localization in NMPC using Shannon's entropy to define the weights involved in the optimization process. This strategy enables us to track position or velocity references or none in the event of losing track of position or velocity estimations. As a result, the collision avoidance constraints are defined in the local coordinates of the MAV and it remains active and guarantees collision avoidance, despite localization uncertainties, e.g., position estimation drifts. The efficacy of the suggested framework has been evaluated using various simulations in the Gazebo environment.

  • 45.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Pourkamali-Anaraki, Farhad
    University of Massachusetts, Lowell, MA, USA.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Towards Robust and Efficient Plane Detection from 3D Point Cloud2021In: 2021 International Conference on Unmanned Aircraft Systems (ICUAS), Institute of Electrical and Electronics Engineers (IEEE) , 2021, p. 560-566Conference paper (Refereed)
    Abstract [en]

    This article proposes a robust and scalable clustering method for 3D point-cloud plane segmentation with applications in Micro Aerial Vehicles (MAVs), such as Simultaneous Localization and Mapping (SLAM), collision avoidance, and object detection. Our approach builds on the sparse subspace clustering framework, which seeks a collection of subspaces that fit the data. Since subspace clustering requires solving a global sparse representation problem and forming a similarity graph, its high computational complexity is known to be a significant drawback, and performance is sensitive to a few hyperparameters. To tackle these challenges, our method has two key ingredients. We use randomized sampling to accelerate subspace clustering by solving a reduced optimization problem. We also analyze the obtained segmentation for quality assurance and performing a post-processing process to resolve two forms of model mismatch. We present numerical experiments to demonstrate the benefits and merits of our method. © 2021 IEEE.

  • 46.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karvelis, Petros
    Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus, Arta, Greece.
    Georgoulas, Georgios
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Remaining Useful Battery Life Prediction for UAVs based on Machine Learning2017In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 50, no 1, p. 4727-4732Article in journal (Refereed)
    Abstract [en]

    Unmanned Aerial Vehicles are becoming part of many industrial applications. The advancements in battery technologies played a crucial part for this trend. However, no matter what the advancements are, all batteries have a fixed capacity and after some time drain out. In order to extend the flying time window, the prediction of the time that the battery will no longer be able to support a flying condition is crucial. This in fact can be cast as a standard Remaining Useful Life prognostic problem, similarly encountered in many fields. In this article, the problem of Remaining Useful Life estimation of a battery, under different flight conditions, is tackled using four machine learning techniques: a linear sparse model, a variant of support vector regression, a multilayer perceptron and an advanced tree based algorithm. The efficiency of the overall proposed machine learning techniques, in the field of batteries prognostics, is evaluated based on multiple experimental data from different flight conditions.

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  • 47.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karvelis, Petros
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Vision-based MAV Navigation in Underground Mine Using Convolutional Neural Network2019In: IECON 2019: 45th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2019, p. 750-755Conference paper (Refereed)
    Abstract [en]

    This article presents a Convolutional Neural Network (CNN) method to enable autonomous navigation of low-cost Micro Aerial Vehicle (MAV) platforms along dark underground mine environments. The proposed CNN component provides on-line heading rate commands for the MAV by utilising the image stream from the on-board camera, thus allowing the platform to follow a collision-free path along the tunnel axis. A novel part of the developed method consists of the generation of the data-set used for training the CNN. More specifically, inspired from single image haze removal algorithms, various image data-sets collected from real tunnel environments have been processed offline to provide an estimation of the depth information of the scene, where ground truth is not available. The calculated depth map is used to extract the open space in the tunnel, expressed through the area centroid and is finally provided in the training of the CNN. The method considers the MAV as a floating object, thus accurate pose estimation is not required. Finally, the capability of the proposed method has been successfully experimentally evaluated in field trials in an underground mine in Sweden.

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  • 48.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karvelis, Petros
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Koval, Anton
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Visual Subterranean Junction Recognition for MAVs based on Convolutional Neural Networks2019In: IECON 2019: 45th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2019, p. 192-197Conference paper (Other academic)
    Abstract [en]

    This article proposes a novel visual framework for detecting tunnel crossings/junctions in underground mine areas towards the autonomous navigation of Micro Aeril Vehicles (MAVs). Usually mine environments have complex geometries, including multiple crossings with different tunnels that challenge the autonomous planning of aerial robots. Towards the envisioned scenario of autonomous or semi-autonomous deployment of MAVs with limited Line-of-Sight in subterranean environments, the proposed module acknowledges the existence of junctions by providing crucial information to the autonomy and planning layers of the aerial vehicle. The capability for a junction detection is necessary in the majority of mission scenarios, including unknown area exploration, known area inspection and robot homing missions. The proposed novel method has the ability to feed the image stream from the vehicles’ on-board forward facing camera in a Convolutional Neural Network (CNN) classification architecture, expressed in four categories: 1) left junction, 2) right junction, 3) left & right junction, and 4) no junction in the local vicinity of the vehicle. The core contribution stems for the incorporation of AlexNet in a transfer learning scheme for detecting multiple branches in a subterranean environment. The validity of the proposed method has been validated through multiple data-sets collected from real underground environments, demonstrating the performance and merits of the proposed module.

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  • 49.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Gustafsson, Thomas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Distributed Model Predictive Control for Unmanned Aerial Vehicles2015In: 2015 Workshop on Research, Education and Development of Unmanned Aerial Systems: RED-UAS 2015, Cancun, Mexico, 23 - 25 November 2015, Picataway, NJ: IEEE Communications Society, 2015, p. 152-161, article id 7441002Conference paper (Refereed)
    Abstract [en]

    In this article a distributed model pre- dictive control scheme, for the cooperative motion control of Unmanned Aerial Vehicles (UAVs) is be- ing presented. The UAVs are modeled by a 6-DOF nonlinear kinematic model. Two different control ar- chitectures: a centralized and a distributed MPC, are studied and evaluated in simulation experiments. In the centralized approach, one central MPC controller is responsible for the movement coordination of all the UAVs, while in the distributed approach each aerial vehicle plans only for its own actions, while the objective function is coupled with the behavior of the rest of the team members and the constraints are decoupled. In this approach, each agent only shares the future position of itself with the other agents to avoid collisions. For reducing the computation time and complexity, only one step ahead prediction in the corresponding MPC schemes have been considered without a loss of generality. Finally, the efficiency of the overall suggested decentralized MPC scheme, as well as it comparison with the centralized approach, is being evaluated through the utilization of multiple simulation scenarios.

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  • 50.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Pourkamali-Anaraki, Farhad
    Department of Computer Science, University of Massachusetts Lowell, MA, USA.
    Castano, Miguel
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, 91109.
    Burdick, Joel
    Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, California, USA.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud2020In: 2020 28th Mediterranean Conference on Control and Automation (MED), IEEE, 2020, p. 802-807Conference paper (Refereed)
    Abstract [en]

    This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The  framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology. 

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