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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.
    Bai, Yifan
    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.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Depth-First-Conflict-Based Search in Non-Holonomic Heterogeneous Robot ScenariosManuscript (preprint) (Other academic)
  • 3.
    Bai, Yifan
    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.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    SA-reCBS: Multi-robot task assignment with integrated reactive path generation2023In: 22nd IFAC World Congress: Yokohama, Japan, July 9-14, 2023, Proceedings / [ed] Hideaki Ishii; Yoshio Ebihara; Jun-ichi Imura; Masaki Yamakita, Elsevier, 2023, p. 7032-7037Conference paper (Refereed)
    Abstract [en]

    In this paper, we study the multi-robot task assignment and path-finding problem (MRTAPF), where a number of robots are required to visit all given tasks while avoiding collisions with each other. We propose a novel two-layer algorithm SA-reCBS that cascades the simulated annealing algorithm and conflict-based search to solve this problem. Compared to other approaches in the field of MRTAPF, the advantage of SA-reCBS is that without requiring a pre-bundle of tasks to groups with the same number of groups as the number of robots, it enables a part of robots needed to visit all tasks in collision-free paths. We test the algorithm in various simulation instances and compare it with state-of-the-art algorithms. The result shows that SA-reCBS has a better performance with a higher success rate, less computational time, and better objective values.

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  • 4.
    Bai, Yifan
    et al.
    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.
    Karlsson, Samuel
    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.
    Cluster-based Multi-Robot Task Assignment, Planning, and Control2024In: Article in journal (Other academic)
  • 5.
    Bai, Yifan
    et al.
    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.
    Karlsson, Samuel
    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.
    Multi-Robot Task Allocation Framework with Integrated Risk-Aware 3D Path Planning2022In: 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, 2022, p. 481-486Conference paper (Refereed)
    Abstract [en]

    This article presents an overall system architecture for multi-robot coordination in a known environment. The proposed framework is structured around a task allocation mechanism that performs unlabeled multi-robot path assignment informed by 3D path planning, while using a nonlinear model predictive control(NMPC) for each unmanned aerial vehicle (UAV) to navigate along its assigned path. More specifically, at first a risk aware 3D path planner D∗+ is applied to calculate cost between each UAV agent and each target point. Then the cost matrix related to the computed trajectories to each goal is fed into the Hungarian Algorithm that solves the assignment problem and generates the minimum total cost. NMPC is implemented to control the UAV while satisfying path following and input constraints. We evaluate the proposed architecture in Gazebo simulation framework and the result indicates UAVs are capable of approaching their assigned target whilst avoiding collisions.

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  • 6.
    Banerjee, Avijit
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Satpute, Sumeet
    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.
    Bodin, Per
    OHB Sweden AB, Sollentuna, Stockholm, Sweden.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On the Design, Modeling and Experimental Verification of a Floating Satellite Platform2022In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 7, no 2, p. 1364-1371Article in journal (Refereed)
    Abstract [en]

    In this letter, a floating robotic emulation platform is presented with an autonomous maneuverability for a virtual demonstration of a satellite motion. Such a robotic platform design is characterized by its friction-less, levitating, yet planar motion over a hyper-smooth surface. The design of the robotic platform, integrated with the sensor and actuator units, is briefly described, including the related component specification along with the mathematical model, describing its dynamic motion. Additionally, the article establishes a nonlinear optimal control architecture consisting of a unified model predictive approach for the overall manoeuvre tracking. The efficacy of the proposed modeling and control scheme is demonstrated in multiple experimental studies, where it is depicted that the proposed controller has the potential to address a precise point-to-point manoeuvre with terminal objectives, as well as an excellent path following capability. The proposed design is validated with extensive experimental studies, and it is supported with related results.

  • 7.
    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.

  • 8.
    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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  • 9.
    Gasparetto, Tommaso
    et al.
    University of Padova, Italy.
    Banerjee, Avijit
    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.
    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.
    Design of Docking Mechanism for Refueling Free-flying 2D Planar Robot2021In: AIRPHARO 2021: The 1st AIRPHARO Workshop on Aerial Robotic Systems Physically Interacting with the Environment, IEEE, 2021, article id Tu1A.1Conference paper (Refereed)
    Abstract [en]

    Free-flying robots are considered a valuable and emerging tool to support astronauts in their daily tasks in space facilities. This work presents the design and development of a free-flying robot as well as a self-contained mechanism that allows its docking for storage and tank refuelling. More specifically, this study presents a floating robotic emulation platform for a simulated demonstration of satellite mobility in orbit. Friction-less, levitating, yet flat motion across a hyper-smooth surface characterizes the robotic platform design. Moreover, the docking mechanism has been designed and developed for the free-flying robot to automate the docking and refuelling processes. The mechanism is divided into two main components, one fixed and one placed on the robot, where the major merit of the proposed system is that it addresses both the tank connection subsystem for the refuelling as well as the subsystem for the dock and repel phases. The former is enabled through the use of an actuated coupling support structure between the air tank and the external outlet, while the latter is enabled with the use of an electromagnetic connection support structure. Finally, preliminary hardware developments have been performed for the proposed robotic systems, demonstrating it's usefulness and effectiveness.

  • 10.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On Visual Perception for an Aerial Robotic Worker2017Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Micro Aerial Vehicles and especially multi rotors are gaining more and more attention for accomplishing complex tasks, considering their simple mechanical design and their versatile movement. MAVs are ideal candidates to perform tasks autonomously, to work safely in close proximity and in collaboration with humans, and to operate safely and effectively in natural human environments, like infrastructure inspection-maintenance, underground mine operations and surveillance missions. Adopting this vision, this thesis contributes in the aerial platform ecosystem that can be summarized by the term Aerial Robotic Worker (ARW). An ARW is characterized, among others, by its advanced capabilities on environmental perception and 3D reconstruction and active aerial manipulation.Using cameras for localization, mapping of an ARW as well as guidance on aerial manipulation is appealing mainly because of the small size and cost of such sensors. Nevertheless, visualinformation provided from the cameras is enormous, posing significant challenges in real-time data processing, while meeting the constraints of these platforms. Additionally, another challenge on visual perception considers the usage of multiple agents that collaboratively perceive their surroundings forming an aerial sensor. This thesis also investigates the applicability of visual SLAM algorithms in uncontrolled and cluttered environments. Furthermore, work will be presented on visual guidance for an aerial manipulator, which is challenging regarding the object detection, tracking and the platform approaching strategies. The first contribution will be the establishment of a flexible virtual stereo rig consisted of MPC controlled MAVs. The advantage of this approach is the varying baseline sensor that is composed from independently moving cameras, adjusting the depth perception accordingly. This method is able to provide the 3D reconstruction of the environment in a sparse pointcloud. The second contribution of this this thesis will examine the single agents in two different scenarios. Initially, experimental trials of commonly used visual sensors in hard and challenging environments will be presented in real scale underground ore mine to evaluate the localization and mapping performance of such technology for potential usage in UAVs. Secondly, theoretical work will be performed regarding attitude regulation of a hexacopter for stable hovering based on visual localization. In this work the time delays induced from the processing should be compensated with a switching control scheme which is able to maintain the stability of the platform. Finally, a third contribution of this thesis will be vision for aerial manipulation. The developed system includes a stereo camera that is attached on the end-effector of the aerial manipulator and is used to provide robust target detection and tracking. The visual feedback is processed to co-localize the aerial agent with the target and generate a waypoint that allows to approach the target.

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  • 11.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Perception Aware Guidance Framework for Micro Aerial Vehicles2020Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Micro Aerial Vehicles (MAVs) are platforms that have received significant research resources within robotics community, since they are characterized by simple mechanical design and versatile movement. These platforms possess capabilities that are suitable for complex task execution, in situations which are impossible or dangerous for the human operator to perform, as well as to reduce the operating costs and increase the overall efficiency of the operation. Until now they have been integrated in the photography-filming industry, but more and more efforts are directed towards remote reconnaissance and inspection applications. Moreover, instead of carrying only sensors these platforms could be endowed with lightweight dexterous robotic arms expanding their operational workspace allowing active interaction with the environment, capabilities that can be vital for applications like payload transportation and infrastructure maintenance. The main objective of this thesis is to establish the concept of the resource-constraint aerial robotic scout and present perception aware frameworks for guidance of the platform and the aerial manipulator as part of the enabling technology towards fully autonomous capabilities. The majority of the works has been developed aiming the application scenario of the MAV deployments in subterranean environments for search and rescue missions, infrastructure inspection and other tasks. A key factor when deploying aerial platforms in dark and cluttered underground tunnels in the lack of illumination which degrades the performance of the visual sensor. It is essential for the inspection or reconnaissance task to get visual feedback from the robot and therefore, this thesis evaluates methods for low light image enhancement in real environments and with datasets collected from flying vehicles, while proposes a preprocessing methodology of the visual dataset for enhancing the 3D mapping of the area. Another capability required when deploying the platforms is the navigation along the tunnel. This thesis establishes 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. Finally, combining the agility of MAVs with the dexterity of robotic arms leads to a new era of Aerial Robotic Workers (ARWs) with advanced capabilities, suitable for complex task execution. This technology has the potential to revolutionize infrastructure maintenance tasks. The development of efficient and reliable perception modules to guide the aerial platform at the desired target areas and perform the respective manipulation tasks is, among others, an essential step towards the envisioned goal. Thus, the aim of this work is the establishment of a visual guidance system to assist the aerial platform before applying any physical interaction. The proposed system is structured around a robust object tracker and is characterized by stereo vision capabilities for target position extraction, towards an autonomous aerial robotic worker.

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  • 12.
    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.

  • 13.
    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.

  • 14.
    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.

  • 15.
    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.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Image Enhancing in Poorly Illuminated Subterranean Environments for MAV Applications: A Comparison Study2019In: 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. 511-520Conference paper (Refereed)
    Abstract [en]

    This work focuses on a comprehensive study and evaluation of existing low-level vision techniques for low light image enhancement, targeting applications in subterranean environments. More specifically, an emerging effort is currently pursuing the deployment of Micro Aerial Vehicles in subterranean environments for search and rescue missions, infrastructure inspection and other tasks. A major part of the autonomy of these vehicles, as well as the feedback to the operator, has been based on the processing of the information provided from onboard visual sensors. Nevertheless, subterranean environments are characterized by a low natural illumination that directly affects the performance of the utilized visual algorithms. In this article, an novel extensive comparison study is presented among five State-of the-Art low light image enhancement algorithms for evaluating their performance and identifying further developments needed. The evaluation has been performed from datasets collected in real underground tunnel environments with challenging conditions from the onboard sensor of a MAV. 

  • 16.
    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.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On Image based Enhancement for 3D Dense Reconstruction of Low Light Aerial Visual Inspected Environments2019In: Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 2 / [ed] Kohei Arai, Supriya Kapoor, Springer, 2019, p. 265-279Conference paper (Refereed)
    Abstract [en]

    Micro Aerial Vehicles (MAV)s have been distinguished, in the last decade, for their potential to inspect infrastructures in an active manner and provide critical information to the asset owners. Inspired by this trend, the mining industry is lately focusing to incorporate MAVs in their production cycles. Towards this direction, this article proposes a novel method to enhance 3D reconstruction of low-light environments, like underground tunnels, by using image processing. More specifically, the main idea is to enhance the low light resolution of the collected images, captured onboard an aerial platform, before inserting them to the reconstruction pipeline. The proposed method is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm that limits the noise, while amplifies the contrast of the image. The overall efficiency and improvement achieved of the novel architecture has been extensively and successfully evaluated by utilizing data sets captured from real scale underground tunnels using a quadrotor.

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  • 17.
    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.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Open Space Attraction Based Navigation in Dark Tunnels for MAVs2019In: 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. 110-119Conference paper (Refereed)
    Abstract [en]

    This work establishes a novel framework for characterizing the open space of featureless dark tunnel environments for Micro Aerial Vehicles (MAVs) navigation tasks. The proposed method leverages the processing of a single camera to identify the deepest area in the scene in order to provide a collision free heading command for the MAV. In the sequel and inspired by haze removal approaches, the proposed novel idea is structured around a single image depth map estimation scheme, without metric depth measurements. The core contribution of the developed framework stems from the extraction of a 2D centroid in the image plane that characterizes the center of the tunnel’s darkest area, which is assumed to represent the open space, while the robustness of the proposed scheme is being examined under varying light/dusty conditions. Simulation and experimental results demonstrate the effectiveness of the proposed method in challenging underground tunnel environments.

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  • 18.
    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.

  • 19.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kyritsis, George
    Electrical and Computer Engineering Department, University of Patras.
    Tsilomitrou, Ourania
    Electrical and Computer Engineering Department, University of Patras.
    Manesis, Stamatis
    Electrical and Computer Engineering Department, University of Patras.
    A low-cost stereoscopic µP-based vision system for industrial light objects grasping2015In: IEEE Mediterranean Conference on Control and Automation, Torremolinos, Spain, June 16-19, 2015 / [ed] V. Nunoz, Piscataway, NJ: IEEE Communications Society, 2015, p. 759-765, article id 7158837Conference paper (Refereed)
    Abstract [en]

    This article describes a vision-based manipulation process for real-time moving objects tracking and grasping, aiming at industrial manufacturing and assembling applications. The adoption of computer vision techniques for object recognition is implemented by means of a stereoscopic system using color based methods under OpenCV libraries. The visual software is directly coupled with the control software of the robotic arm Katana 6M90G manufactured by Neuronics AG, running under a Linux-based Operating System (OS) distribution Lubuntu, over a low-cost and powerful microprocessor Odroid U3. Experimental studies validate the effectiveness of the implementation, while remarking the advantageous effects of the 3D pose estimation process.

  • 20.
    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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  • 21.
    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.

  • 22.
    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.

  • 23.
    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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  • 24.
    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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  • 25.
    Kanellakis, Christoforos
    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.
    A robust reconfigurable control scheme against pose estimation induced time delays2016In: 2016 IEEE Conference on Control Applications (CCA): 19-22 Sept. 2016, Piscataway, NU: IEEE Communications Society, 2016, p. 581-586, article id 7587892Conference paper (Refereed)
    Abstract [en]

    Time delays are one of the most common problems when utilizing a visual sensor for pose estimation or navigation in aerial robotics. Such time delays can grow exponentially as a function of the scene's complexity and the size of the mapping during classical Simultaneous Localization and Mapping (SLAM) strategies. In this article, a robust reconfigurable control scheme against pose estimation induced time delays is presented. Initially, an experimental verification of the induced time delays via pose estimation is performed for the attitude problem of a hexacopter, while a switching time delay dependent modeling approach is formulated. In addition, a stability analysis algorithm is introduced in order to evaluate the maximum allowable time delays that the target system can handle for a given LQR controller. The varying nature of the time delays results in a switching system with the latency time to play the role of a switching rule, while simulation results are presented to outline the effects of the time-induced delays in hexarotor-based systems and finally evaluate the overall efficiency of the proposed control scheme.

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  • 26.
    Kanellakis, Christoforos
    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.
    Evaluation of Visual Localization Systems in Underground Mining2016In: 24th Mediterranean Conference on Control and Automation (MED): June 21-24, Athens, Greece, 2016, Piscataway, NJ: IEEE Communications Society, 2016, p. 539-544, article id 7535853Conference paper (Refereed)
    Abstract [en]

    In this article an evaluation of the current technology on visual localization systems for underground mining is presented. The proposed study is considered to be the first step among others towards enabling the vision of underground localization for Unmanned Micro Aerial Vehicles. Furthermore, the aim of this article, is to verify applicable and reliable low cost existing methods and technologies for the problem of UAV localization in underground, harsh mining environments and more specifically in one of the biggest mines in Europe, the iron ore mine of LKAB in Kiruna, Sweden. In the experimental trials, the sensors employed were a RGB-D camera, a Kinect 2 and a Playstation 3 Eye web camera used in two configurations, as a stereo rig and as a monocular visual sensor. The processing of the stored data from the experiments will provide an insight for the applicability of these sensors, while it will identify what further technological and research developments are required in order to develop affordable autonomous UAV solutions for improving the underground mining production processes.

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  • 27.
    Kanellakis, Christoforos
    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.
    Guidance for Autonomous Aerial Manipulator Using Stereo Vision2020In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 100, no 3-4, p. 1545-1557Article in journal (Refereed)
    Abstract [en]

    Combining the agility of Micro Aerial Vehicles (MAV) with the dexterity of robotic arms leads to a new era of Aerial Robotic Workers (ARW) targeting infrastructure inspection and maintenance tasks. Towards this vision, this work focuses on the autonomous guidance of the aerial end-effector to either reach or keep desired distance from areas/objects of interest. The proposed system: 1) is structured around a real-time object tracker, 2) employs stereo depth perception to extract the target location within the surrounding scene, and finally 3) generates feasible poses for both the arm and the MAV relative to the target. The performance of the proposed scheme is experimentally demonstrated in multiple scenarios of increasing complexity.

  • 28.
    Kanellakis, Christoforos
    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.
    Survey on Computer Vision for UAVs: Current Developments and Trends2017In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 87, no 1, p. 141-168Article in journal (Refereed)
    Abstract [en]

    During last decade the scientific research on Unmanned Aerial Vehicless (UAVs) increased spectacularly and led to the design of multiple types of aerial platforms. The major challenge today is the development of autonomously operating aerial agents capable of completing missions independently of human interaction. To this extent, visual sensing techniques have been integrated in the control pipeline of the UAVs in order to enhance their navigation and guidance skills. The aim of this article is to present a comprehensive literature review on vision based applications for UAVs focusing mainly on current developments and trends. These applications are sorted in different categories according to the research topics among various research groups. More specifically vision based position-attitude control, pose estimation and mapping, obstacle detection as well as target tracking are the identified components towards autonomous agents. Aerial platforms could reach greater level of autonomy by integrating all these technologies onboard. Additionally, throughout this article the concept of fusion multiple sensors is highlighted, while an overview on the challenges addressed and future trends in autonomous agent development will be also provided.

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  • 29.
    Kanellakis, Christoforos
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Terreran, Matteo
    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.
    On vision enabled aerial manipulation for multirotors2018In: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper (Refereed)
    Abstract [en]

    This article presents an integrated vision-based guiding system for aerial manipulation. More specifically, a 4 DoF planar dexterous manipulator, with a stereo camera attached on the end-effector, is endowed to a multirotor aerial platform enabling active manipulation capabilities. The proposed novel approach combines a visual processing scheme for object detection and tracking, as well as a manipulator positioning for allowing the aerial platform to approach the surface of interaction efficiently. In the developed scheme, the object detection is based on correlation filters to track the target robustly, while the depth information, from the stereo camera on board the manipulator, is used to extract the centroid of the manipulated object, compute its relative configuration with respect to the UAV and align the end-effector properly with the grasping point. The effectiveness of the proposed scheme is demonstrated in multiple experimental trials and simulations, highlighting it's applicability towards autonomous aerial manipulation.

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  • 30.
    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.
    Perception driven guidance modules for aerial manipulation2023In: Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications / [ed] George Nikolakopoulos, Sina Sharif Mansouri, Christoforos Kanellakis, Elsevier, 2023, p. 141-157Chapter in book (Other academic)
    Abstract [en]

    Micro Aerial Vehicles (MAVs) have showcased their potential for enabling the next generation of inspection technologies as a sensor payload carrier operating at various levels of autonomy. Combining their ability to navigate in unstructured areas with the dexterity of robotic arms leads to a new era of Aerial Robotic Workers (ARWs) able to interact with the environment and as such address infrastructure maintenance tasks. The development of perception capabilities that guide the aerial platform at the desired regions of interest and perform the respective manipulation tasks is, among others, a fundamental step towards the envisioned goal. Thus, the aim of this Chapter is to discuss visual guidance systems to assist the aerial platform before applying any physical interaction.

  • 31.
    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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  • 32.
    Karlsson, Samuel
    et al.
    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.
    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.
    D+: A risk aware platform agnostic heterogeneous path planner2023In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 215, article id 119408Article, review/survey (Refereed)
    Abstract [en]

    This article establishes the novel D+*, , a risk-aware and platform-agnostic heterogeneous global path planner for robotic navigation in complex environments. The proposed planner addresses a fundamental bottleneck of occupancy-based path planners related to their dependency on accurate and dense maps. More specifically, their performance is highly affected by poorly reconstructed or sparse areas (e.g. holes in the walls or ceilings) leading to faulty generated paths outside the physical boundaries of the 3-dimensional space. As it will be presented, D+* addresses this challenge with three novel contributions, integrated into one solution, namely: (a) the proximity risk, (b) the modeling of the unknown space, and (c) the map updates. By adding a risk layer to spaces that are closer to the occupied ones, some holes are filled, and thus the problematic short-cutting through them to the final goal is prevented. The novel established D+*  also provides safety marginals to the walls and other obstacles, a property that results in paths that do not cut the corners that could potentially disrupt the platform operation. D+*  has also the capability to model the unknown space as risk-free areas that should keep the paths inside, e.g in a tunnel environment, and thus heavily reducing the risk of larger shortcuts through openings in the walls. D+* is also introducing a dynamic map handling capability that continuously updates with the latest information acquired during the map building process, allowing the planner to use constant map growth and resolve cases of planning over outdated sparser map reconstructions. The proposed path planner is also capable to plan 2D and 3D paths by only changing the input map to a 2D or 3D map and it is independent of the dynamics of the robotic platform. The efficiency of the proposed scheme is experimentally evaluated in multiple real-life experiments where D+* is producing successfully proper planned paths, either in 2D in the use case of the Boston dynamics Spot robot or 3D paths in the case of an unmanned areal vehicle in varying and challenging scenarios.

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  • 33.
    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.

  • 34.
    Kottayam Viswanathan, Vignesh
    et al.
    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.
    Satpute, Sumeet Gajanan
    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.
    Towards Visual Inspection of Distributed and Irregular Structures: A Unified Autonomy Approach2023In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 109, no 2, article id 32Article in journal (Refereed)
    Abstract [en]

    This paper highlights the significance of maintaining and enhancing situational awareness in Urban Search and Rescue (USAR) missions. It focuses specifically on investigating the capabilities of Unmanned Aerial Vehicles (UAV) equipped with limited sensing capabilities and onboard computational resources to perform visual inspections of apriori unknown fractured and collapsed structures in unfamiliar environments. The proposed approach, referred to as First Look Inspect-Explore (FLIE), employs a flexible bifurcated behavior tree that leverages real-time RGB image and depth cloud data. By employing a recursive and reactive synthesis of safe view pose within the inspection module, FLIE incorporates a novel active visual guidance scheme for identifying previously inspected surfaces. Furthermore, the integration of a tiered hierarchical exploration module with the visual guidance system enables the UAV to navigate towards new and unexplored structures without relying on a map. This decoupling reduces memory overhead and computational effort by eliminating the need to plan based on an incrementally built, error-prone global map. The proposed autonomy is extensively evaluated through simulation and experimental verification under various scenarios and compared against state-of-art approaches, demonstrating its performance and effectiveness.

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  • 35.
    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.

  • 36.
    Kottayam Viswanathan, Vignesh
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Satpute, Sumeet Gajanan
    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.
    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.
    Experimental Evaluation of a Geometry-Aware Aerial Visual Inspection Framework in a Constrained Environment2022In: 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, 2022, p. 468-474Conference paper (Refereed)
    Abstract [en]

    This article aims to present an experimental evaluation of an offline, geometry-aware aerial visual inspection framework, specifically in constrained environment, established for geometrically fractured objects, by employing an autonomous unmanned aerial vehicle (UAV), equipped with on-board sensors. Based on a model-centric approach, the proposed inspection framework, generates inspection viewpoints around the geometrically fractured object, subject to the augmented static bounds to prevent collisions. The novel framework of visual inspection, presented in this article, aims to mitigate challenges arising due to the spatially-constrained environment, such as limited configuration space and collision with the object under inspection, by accounting for the geometrical information of the vehicle to be inspected. The efficacy of the proposed scheme is experimentally evaluated through large scale field trials with a mining machine.

  • 37.
    Koval, Anton
    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.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Evaluation of Lidar-based 3D SLAM algorithms in SubT environment2022In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 55, no 38, p. 126-131Article in journal (Refereed)
    Abstract [en]

    Autonomous navigation of robots in harsh and GPS denied subterranean (SubT) environments with lack of natural or poor illumination is a challenging task that fosters the development of algorithms for pose estimation and mapping. Inspired by the need for real-life deployment of autonomous robots in such environments, this article presents an experimental comparative study of 3D SLAM algorithms. The study focuses on state-of-the-art Lidar SLAM algorithms with open-source implementation that are i) lidar-only like BLAM, LOAM, A-LOAM, ISC-LOAM and hdl graph slam, or ii) lidar-inertial like LeGO-LOAM, Cartographer, LIO-mapping and LIO-SAM. The evaluation of the methods is performed based on a dataset collected from the Boston Dynamics Spot robot equipped with 3D lidar Velodyne Puck Lite and IMU Vectornav VN-100, during a mission in an underground tunnel. In the evaluation process poses and 3D tunnel reconstructions from SLAM algorithms are compared against each other to find methods with most solid performance in terms of pose accuracy and map quality.

  • 38.
    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.

  • 39.
    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.

  • 40.
    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.

  • 41.
    Lindqvist, Björn
    et al.
    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.
    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.
    An Adaptive 3D Artificial Potential Field for Fail-safe UAV Navigation2022In: 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, 2022, p. 362-367Conference paper (Refereed)
    Abstract [en]

    This article presents an obstacle avoidance framework for unmanned aerial vehicles (UAVs), with a focus on providing safe and stable local navigation in critical scenarios. The framework is based on enhanced artificial potential field (APF) concepts, and is paired with a nonlinear model predictive controller (NMPC) for complete local reactive navigation. This paper will consider a series of additions to the classical artificial potential field that addresses UAV-specific challenges, allows for smooth navigation in tightly constrained environments, and ensures safe human-robot interactions. The APF formulation is fundamentally based on using raw LiDAR pointcloud data as input to decouple the safe robot navigation problem from the reliance on any map or obstacle detection software, resulting in a very resilient and fail-safe framework that can be used a san additional safety layer for any 3D-LiDAR equipped UAV in any environment or mission scenario. We evaluate the scheme in both laboratory experiments and field trials, and also placea large emphasis on realistic scenarios for safe human-robot interactions.

  • 42.
    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.

  • 43.
    Lindqvist, Björn
    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.
    Koval, Anton
    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.
    Kanellakis, Christoforos
    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.
    Multimodality robotic systems: Integrated combined legged-aerial mobility for subterranean search-and-rescue2022In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 154, article id 104134Article in journal (Refereed)
    Abstract [en]

    This work presents a field-hardened autonomous multimodal legged-aerial robotic system for subterranean exploration, extending a legged robot to be the carrier of an aerial platform capable of a rapid deployment in search-and-rescue scenarios. The driving force for developing such robotic configurations are the requirements for large-scale and long-term missions, where the payload capacity and long battery life of the legged robot is combined and integrated with the agile motion of the aerial agent. The multimodal robot is structured around the quadruped Boston Dynamics Spot, enhanced with a custom configured autonomy sensor payload as well as a UAV carrier platform, while the aerial agent is a custom built quadcopter. This work presents the novel design and hardware implementation as well as the onboard sensor suites. Moreover it establishes the overall autonomy architecture in a unified supervision approach while respecting each locomotion modality, including guidance, navigation, perception, state estimation, and control capabilities with a focus on rapid deployment and efficient exploration. The robotic system complete architecture is evaluated in real subterranean tunnel areas, in multiple fully autonomous search-and-rescue missions with the goal of identifying and locating objects of interest within the subterranean environment.

  • 44.
    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.

  • 45.
    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.

    Download full text (pdf)
    fulltext
  • 46.
    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.

    Download full text (pdf)
    fulltext
  • 47.
    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.

    Download full text (pdf)
    fulltext
  • 48.
    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.

    Download full text (pdf)
    fulltext
  • 49.
    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.

  • 50.
    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.

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