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  • 1.
    Kottayam Viswanathan, Vignesh
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Aerial infrastructures inspection2023Ingår i: Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications / [ed] George Nikolakopoulos, Sina Sharif Mansouri, Christoforos Kanellakis, Elsevier, 2023, s. 175-211Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    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.

  • 2.
    Nikolakopoulos, George
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina SharifLuleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system. Autonomous Driving Lab in Scania Group, Stockholm, Sweden.Kanellakis, ChristoforosLuleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications2023Samlingsverk (redaktörskap) (Övrigt vetenskapligt)
  • 3.
    Lindqvist, Björn
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kottayam Viswanathan, Vignesh
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    ARW deployment for subterranean environments2023Ingår i: Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications / [ed] George Nikolakopoulos, Sina Sharif Mansouri, Christoforos Kanellakis, Elsevier, 2023, s. 213-243Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    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.

  • 4.
    Karlsson, Samuel
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Koval, Anton
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    D+: A risk aware platform agnostic heterogeneous path planner2023Ingår i: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 215, artikel-id 119408Artikel, forskningsöversikt (Refereegranskat)
    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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  • 5.
    Koval, Anton
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Machine learning for ARWs2023Ingår i: Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications / [ed] George Nikolakopoulos, Sina Sharif Mansouri, Christoforos Kanellakis, Elsevier, 2023, s. 159-174Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    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.

  • 6.
    Saucedo, Mario Alberto Valdes
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    MSL3D: Pointcloud-based muck pile Segmentation and Localization in Unknown SubT Environments2023Ingår i: 2023 31st Mediterranean Conference on Control and Automation, MED 2023, Institute of Electrical and Electronics Engineers Inc. , 2023, s. 269-274Konferensbidrag (Refereegranskat)
    Abstract [en]

    This article presents MSL3D, a novel framework for pointcloud-based muck pile Segmentation and Localization in unknown Sub-Terranean (Sub-T) environments. The proposed framework is capable of progressively segmenting the muck piles and extracting their location in a global constructed point cloud map, using the autonomy sensor payload of mining or robotic platforms. MSL3D is structured in a two layer novel architecture that relies on the geometric properties of muck piles in underground tunnels, where the first layer extracts a local Volume Of Interest (VOI) proposal area out of the registered point cloud and the second layer is refining the muck pile extraction of each VOI proposal in the global optimized point cloud map. The first layer of MSL3D is extracting local VOIs bounded in the look-ahead surroundings of the platform. More specifically, the ceiling, left and right walls as well as the ground are continuously segmented using progessive RANSAC, searching for inclination in the segmented ground area to keep as the next-best local VOI. Once a local VOI is extracted, it is transmitted to the second layer, where it is converted to the world frame coordinates. In the sequel, a morphological filter is applied, in order to segment ground and nonground points, followed by RANSAC once again to extract the remaining points corresponding to the right and left walls. In this approach, Euclidean clustering is utilized to keep the cluster with the majority of points, which is assumed to belong to the muck pile. The efficacy of the proposed novel scheme was successfully and experimentally validated in real and large scale SubT environments by utilizing a custom-made UAV.

  • 7.
    Karlsson, Samuel
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Perception driven guidance modules for aerial manipulation2023Ingår i: Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications / [ed] George Nikolakopoulos, Sina Sharif Mansouri, Christoforos Kanellakis, Elsevier, 2023, s. 141-157Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    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.

  • 8.
    Patel, Akash
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Lindqvist, Björn
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, 91109.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    REF: A Rapid Exploration Framework for Deploying Autonomous MAVs in Unknown Environments2023Ingår i: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 108, artikel-id 35Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Exploration and mapping of unknown environments is a fundamental task in applications for autonomous robots. In this article, we present a complete framework for deploying Micro Aerial Vehicles (MAVs) in autonomous exploration missions in unknown subterranean areas. The main motive of exploration algorithms is to depict the next best frontier for the MAV such that new ground can be covered in a fast, safe yet efficient manner. The proposed framework uses a novel frontier selection method that also contributes to the safe navigation of autonomous MAVs in obstructed areas such as subterranean caves, mines, and urban areas. The framework presented in this work bifurcates the exploration problem in local and global exploration. The proposed exploration framework is also adaptable according to computational resources available onboard the MAV which means the trade-off between the speed of exploration and the quality of the map can be made. Such capability allows the proposed framework to be deployed in subterranean exploration and mapping as well as in fast search and rescue scenarios. The performance of the proposed framework is evaluated in detailed simulation studies with comparisons made against a high-level exploration-planning framework developed for the DARPA Sub-T challenge as it will be presented in this article.

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  • 9.
    Patel, Akash
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Karlsson, Samuel
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Lindqvist, Björn
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Agha-Mohammadi, Ali-Akbar
    Jet Propulsion Laboratory, California Institute of Technology Pasadena, CA 91109, USA.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Towards energy efficient autonomous exploration of Mars lava tube with a Martian coaxial quadrotor2023Ingår i: Advances in Space Research, ISSN 0273-1177, E-ISSN 1879-1948, Vol. 71, nr 9, s. 3837-3854Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Mapping and exploration of a Martian terrain with an aerial vehicle has become an emerging research direction, since the successful flight demonstration of the Mars helicopter Ingenuity. Although the autonomy and navigation capability of the state of the art Mars helicopter has proven to be efficient in an open environment, the next area of interest for exploration on Mars are caves or ancient lava tube like environments, especially towards the never-ending search of life on other planets. This article presents an autonomous exploration mission based on a modified frontier approach along with a risk aware planning and integrated collision avoidance scheme with a special focus on energy aspects of a custom designed Mars Coaxial Quadrotor (MCQ) in a Martian simulated lava tube. One of the biggest novelties of the article stems from addressing the exploration capability, while rapidly exploring in local areas and intelligently global re-positioning of the MCQ when reaching dead ends in order to efficiently use the battery based consumed energy, while increasing the volume of the exploration. The proposed novel algorithm for the Martian exploration is able to select the next way point of interest, such that the MCQ keeps its heading towards the local exploration direction where it will find maximum information about the surroundings. The proposed three layer cost based global re-position point selection assists in rapidly redirecting the MCQ to previously partially seen areas that could lead to more unexplored part of the lava tube. The Martian fully simulated mission presented in this article takes into consideration the fidelity of physics of Mars condition in terms of thin atmosphere, low surface pressure and low gravity of the planet, while proves the efficiency of the proposed scheme in exploring an area that is particularly challenging due to the subterranean-like environment. The proposed exploration-planning framework is also validated in simulation by comparing it against the graph based exploration planner. Intensive simulations with true Mars conditions are carried out in order to validate and benchmark our approach in a utmost realistic Mars lava tube exploration scenario using a Mars Coaxial Quadrotor.

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  • 10.
    Patel, Akash
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Karlsson, Samuel
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Lindqvist, Björn
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Haluska, Jakub
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Towards Field Deployment of MAVs in Adaptive Exploration of GPS-denied Subterranean Environments2023Ingår i: Artikel i tidskrift (Övrigt vetenskapligt)
  • 11.
    Kottayam Viswanathan, Vignesh
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Lindqvist, Björn
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Satpute, Sumeet Gajanan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Towards Visual Inspection of Distributed and Irregular Structures: A Unified Autonomy Approach2023Ingår i: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 109, nr 2, artikel-id 32Artikel i tidskrift (Refereegranskat)
    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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  • 12.
    Patel, Akash
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Agha-Mohammadi, Ali-Akbar
    NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Traversability Aware Graph Based Subterranean Exploration with Unmanned Aerial Vehicles2023Konferensbidrag (Övrigt vetenskapligt)
  • 13.
    Lindqvist, Björn
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Haluska, Jakub
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    An Adaptive 3D Artificial Potential Field for Fail-safe UAV Navigation2022Ingår i: 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, 2022, s. 362-367Konferensbidrag (Refereegranskat)
    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.

  • 14.
    Lindqvist, Björn
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, Pasadena, CA, 91109, USA.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    COMPRA: A COMPact Reactive Autonomy Framework for Subterranean MAV Based Search-And-Rescue Operations2022Ingår i: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 105, nr 3, artikel-id 49Artikel i tidskrift (Refereegranskat)
    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.

  • 15.
    Koval, Anton
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Karlsson, Samuel
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Tevetzidis, Ilias
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Haluska, Jakub
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, 91109, United States of America.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Dataset collection from a SubT environment2022Ingår i: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 155, artikel-id 104168Artikel i tidskrift (Refereegranskat)
    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.

  • 16.
    Patel, Akash
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Banerjee, Avijit
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Lindqvist, Björn
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Design and Model Predictive Control of a Mars Coaxial Quadrotor2022Ingår i: 2022 IEEE Aerospace Conference (AERO), IEEE, 2022Konferensbidrag (Refereegranskat)
    Abstract [en]

    Mars has been a prime candidate for planetary explo-ration of the solar system because of the science discoveries that support chances of future habitation on this planet. The Mars exploration landers and rovers have laid the foundation of our understanding of the planet's atmosphere and terrain. However, the rovers have presented limitations in terms of their pace, travers ability, and exploration capabilities from the ground and thus, one of the main field of interest for future robotic mission to Mars is to enhance the autonomy of this exploration vehicles. Martian caves and lava tubes like terrains, which consists of uneven ground, poor visibility and confined space, makes it impossible for wheel based rovers to navigate through these areas. In order to address these limitations and advance the exploration capability in a Martian terrain, this article presents the design and control of a novel coaxial quadrotor Micro Aerial Vehicle (MAV). As it will be presented, the key contributions on the design and control architecture of the proposed Mars coaxial quadrotor, are introducing an alternative and more enhanced, from a control point of view concept, when compared in terms of autonomy to Ingenuity. Based on the presented design, the article will introduce the mathematical modelling and automatic control framework of the vehicle that will consist of a linearised model of a co-axial quadrotor and a corresponding Model Pre-dictive Controller (MPC) for the trajectory tracking. Among the many models, proposed for the aerial flight on Mars, a reliable control architecture lacks in the related state of the art. The MPC based closed loop responses of the proposed MAV will be verified in different conditions during the flight with additional disturbances, induced to replicate a real flight scenario. For the model validation purpose, the Mars coaxial quadrotor is sim-ulated inside a Martian environment with related atmospheric conditions in the Gazebo simulator, which will use the proposed MPC controller for following an a priory defined trajectory. In order to further validate the proposed control architecture and prove the efficacy of the suggested design, the introduced Mars coaxial quadrotor and the MPC scheme will be compared to a PID-type controller, similar to the Ingenuity helicopter's control architecture for the position and the heading.

  • 17.
    Koval, Anton
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Evaluation of Lidar-based 3D SLAM algorithms in SubT environment2022Ingår i: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 55, nr 38, s. 126-131Artikel i tidskrift (Refereegranskat)
    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.

  • 18.
    Kottayam Viswanathan, Vignesh
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Satpute, Sumeet Gajanan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Lindqvist, Björn
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Experimental Evaluation of a Geometry-Aware Aerial Visual Inspection Framework in a Constrained Environment2022Ingår i: 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, 2022, s. 468-474Konferensbidrag (Refereegranskat)
    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.

  • 19.
    Patel, Akash
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Lindqvist, Björn
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Fast Planner for MAV Navigation in Unknown Environments Based on Adaptive Search of Safe Look-Ahead Poses2022Ingår i: 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, 2022, s. 545-550Konferensbidrag (Refereegranskat)
    Abstract [en]

    Autonomous navigation capability is a crucial part for deploying robots in an unknown environment. In this article a reactive local planner for autonomous and safe navigation in subterranean environment is presented. The proposed planning framework navigates the MAV forward in a tunnel such that the MAV gains more information about the environment while avoiding obstacles. The proposed planning architecture works solely based on the information of local surrounding of the MAV thus, making navigation simple yet fast. One of the biggest novelties of the article comes from solving the combined problem of autonomous navigation and obstacle avoidance. The proposed algorithm for selecting the next way point of interest also accounts in the safety margin for traversing to such way point. The approach presented in this article is also different from classical map based global planning algorithms because it favours the next way point away from obstacles in way point selection process and thus providing a safe path for incremental forward navigation. The approach is validated by simulating a MAV equipped with the proposed reactive local planner in order for the MAV to navigate in a subterranean cave environment.

  • 20.
    Lindqvist, Björn
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Karlsson, Samuel
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Koval, Anton
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Tevetzidis, Ilias
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Haluska, Jakub
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, 91109, United States of America.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Multimodality robotic systems: Integrated combined legged-aerial mobility for subterranean search-and-rescue2022Ingår i: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 154, artikel-id 104134Artikel i tidskrift (Refereegranskat)
    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.

  • 21.
    Bai, Yifan
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Lindqvist, Björn
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Karlsson, Samuel
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Multi-Robot Task Allocation Framework with Integrated Risk-Aware 3D Path Planning2022Ingår i: 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, 2022, s. 481-486Konferensbidrag (Refereegranskat)
    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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  • 22.
    Banerjee, Avijit
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Satpute, Sumeet
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Tevetzidis, Ilias
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Haluska, Jakub
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Bodin, Per
    OHB Sweden AB, Sollentuna, Stockholm, Sweden.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    On the Design, Modeling and Experimental Verification of a Floating Satellite Platform2022Ingår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 7, nr 2, s. 1364-1371Artikel i tidskrift (Refereegranskat)
    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.

  • 23.
    Patel, Akash
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Traversable Frontiers Based Autonomous Exploration Strategy for Deploying MAVs in Subterranean Environments2022Ingår i: 2022 Eigth Indian Control Conference (ICC): Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2022, s. 260-265Konferensbidrag (Refereegranskat)
  • 24.
    Koval, Anton
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Aerial Thermal Image based Convolutional Neural Networks for Human Detection in SubT Environments2021Ingår i: 2021 The International Conference on Unmanned Aircraft Systems (ICUAS’21), IEEE, 2021, s. 536-541Konferensbidrag (Refereegranskat)
    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.

  • 25.
    Gasparetto, Tommaso
    et al.
    University of Padova, Italy.
    Banerjee, Avijit
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Tevetzidis, Ilias
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Haluska, Jakub
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Design of Docking Mechanism for Refueling Free-flying 2D Planar Robot2021Ingår i: AIRPHARO 2021: The 1st AIRPHARO Workshop on Aerial Robotic Systems Physically Interacting with the Environment, IEEE, 2021, artikel-id Tu1A.1Konferensbidrag (Refereegranskat)
    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.

  • 26.
    Papadimitriou, Andreas
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Geometry Aware NMPC Scheme for Morphing Quadrotor Navigation in Restricted Entrances2021Ingår i: 2021 European Control Conference (ECC), IEEE, 2021, s. 1597-1603Konferensbidrag (Refereegranskat)
    Abstract [en]

    Geometry-morphing Micro Aerial Vehicles (MAVs) are gaining more attention lately, since, their ability to modify their geometric morphology in-flight increases their versatility while expanding their application range. In this novel research field, most of the works focus on the platform design and on the low-level control for maintaining stability after changing its shape. Nevertheless, another aspect of geometry morphing MAVs is the association of its morphology with respect to the shape and structure of the environment. In this article, we propose a novel Nonlinear Model Predictive Control (NMPC) structure that modifies the morphology of a quadrotor based on the environment entrances' geometrical shape. The proposed method considers restricted entrances as a constraint in the NMPC and modifies the arm configuration of the MAV to provide a collision-free path from the initial position to the desired goal while passing through the entrance. Multiple simulation results present the performance and efficiency of the proposed scheme in scenarios where the quadrotor is commanded to pass through narrow entrances.

  • 27.
    Palieri, Matteo
    et al.
    NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy.
    Morrell, Benjamin
    NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy.
    Thakur, Abhishek
    Aptiv, Troy, MI, USA.
    Ebadi, Kamak
    NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy.
    Nash, Jeremy
    NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy.
    Chatterjee, Arghya
    Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Carlone, Luca
    Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA.
    Guaragnella, Cataldo
    Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy.
    Agha-mohammadi, Ali-akbar
    NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy.
    LOCUS: A Multi-Sensor Lidar-Centric Solution for High-Precision Odometry and 3D Mapping in Real-Time2021Ingår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 6, nr 2, s. 421-428Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A reliable odometry source is a prerequisite to enable complex autonomy behaviour in next-generation robots operating in extreme environments. In this work, we present a high-precision lidar odometry system to achieve robust and real-time operation under challenging perceptual conditions. LOCUS (Lidar Odometry for Consistent operation in Uncertain Settings), provides an accurate multi-stage scan matching unit equipped with an health-aware sensor integration module for seamless fusion of additional sensing modalities. We evaluate the performance of the proposed system against state-of-the-art techniques in perceptually challenging environments, and demonstrate top-class localization accuracy along with substantial improvements in robustness to sensor failures. We then demonstrate real-time performance of LOCUS on various types of robotic mobility platforms involved in the autonomous exploration of the Satsop power plant in Elma, WA where the proposed system was a key element of the CoSTAR team's solution that won first place in the Urban Circuit of the DARPA Subterranean Challenge.

  • 28.
    Karlsson, Samuel
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Monocular Vision-based Obstacle Avoidance Scheme for Micro Aerial Vehicle Navigation2021Ingår i: 2021 The International Conference on Unmanned Aircraft Systems (ICUAS’21), IEEE, 2021, s. 1321-1327Konferensbidrag (Refereegranskat)
    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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  • 29.
    Kanellakis, Christoforos
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Karvelis, Petros S.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, 91109.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Towards Autonomous Aerial Scouting Using Multi-Rotors in Subterranean Tunnel Navigation2021Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 9, s. 66477-66485Artikel i tidskrift (Refereegranskat)
    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.

  • 30.
    Mansouri, Sina Sharif
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Pourkamali-Anaraki, Farhad
    University of Massachusetts, Lowell, MA, USA.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Towards Robust and Efficient Plane Detection from 3D Point Cloud2021Ingår i: 2021 International Conference on Unmanned Aircraft Systems (ICUAS), Institute of Electrical and Electronics Engineers (IEEE) , 2021, s. 560-566Konferensbidrag (Refereegranskat)
    Abstract [en]

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

  • 31.
    Mansouri, Sina Sharif
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Lindqvist, Björn
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Pourkamali-Anaraki, Farhad
    Department of Computer Science, University of Massachusetts Lowell, Lowell, MA 01854 USA.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory, California Institute of Technology Pasadena, Pasadena, CA 91109 USA.
    Burdick, Joel
    Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, CA 91125 USA.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    A Unified NMPC Scheme for MAVs Navigation With 3D Collision Avoidance Under Position Uncertainty2020Ingår i: IEEE Robotics and Automation Letters, ISSN 2377-3766, E-ISSN 1949-3045, Vol. 5, nr 4, s. 5740-5747Artikel i tidskrift (Refereegranskat)
    Abstract [en]

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

  • 32.
    Kanellakis, Christoforos
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Fresk, Emil
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kominiak, Dariusz
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Aerial imaging and reconstruction of infrastructures by UAVs2020Ingår i: Imaging and Sensing for Unmanned Aircraft Systems Volume 2: Deployment and Applications, Institution of Engineering and Technology , 2020, s. 157-176Kapitel i bok, del av antologi (Övrigt vetenskapligt)
    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.

  • 33.
    Lindqvist, Björn
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Collision Free Path Planning based on Local 2D Point-Clouds for MAV Navigation2020Ingår i: 2020 28th Mediterranean Conference on Control and Automation (MED), IEEE, 2020, s. 538-543Konferensbidrag (Refereegranskat)
    Abstract [en]

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

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  • 34.
    Mansouri, Sina Sharif
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kominiak, Dariusz
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Deploying MAVs for autonomous navigation in dark underground mine environments2020Ingår i: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 126, artikel-id 103472Artikel i tidskrift (Refereegranskat)
    Abstract [en]

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

  • 35.
    Kanellakis, Christoforos
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Guidance for Autonomous Aerial Manipulator Using Stereo Vision2020Ingår i: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 100, nr 3-4, s. 1545-1557Artikel i tidskrift (Refereegranskat)
    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.

  • 36.
    Kominiak, Dariusz
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    MAV Development Towards Navigation in Unknown and Dark Mining Tunnels2020Ingår i: 2020 28th Mediterranean Conference on Control and Automation (MED), IEEE, 2020, s. 1015-1020Konferensbidrag (Refereegranskat)
    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.

  • 37.
    Mansouri, Sina Sharif
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Karvelis, Petros
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kominiak, Dariusz
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    MAV Navigation in Unknown Dark Underground Mines Using Deep Learning2020Ingår i: European Control Conference 2020, IEEE, 2020, s. 1943-1948Konferensbidrag (Refereegranskat)
    Abstract [en]

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

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  • 38.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Perception Aware Guidance Framework for Micro Aerial Vehicles2020Doktorsavhandling, monografi (Övrigt vetenskapligt)
    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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  • 39.
    Mansouri, Sina Sharif
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Fresk, Emil
    WideFind AB, Aurorum 1C, Luleå SE-97775, Sweden.
    Lindqvist, Björn
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kominiak, Dariusz
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Koval, Anton
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    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 tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Subterranean MAV Navigation based on Nonlinear MPC with Collision Avoidance Constraints2020Ingår i: 21th IFAC World Congress / [ed] Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, Elsevier, 2020, s. 9650-9657Konferensbidrag (Refereegranskat)
    Abstract [en]

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

  • 40.
    Carlbaum, Erik
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Koval, Anton
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Towards Robust Localization Deep Feature Extraction by CNN2020Ingår i: Proceedings: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2020, s. 807-812Konferensbidrag (Refereegranskat)
    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.

  • 41.
    Kanellakis, Christoforos
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Fresk, Emil
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kominiak, Dariusz
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Towards Visual Inspection of Wind Turbines: A Case of Visual Data Acquisition using Autonomous Aerial Robots2020Ingår i: IEEE Access, E-ISSN 2169-3536, Vol. 8, s. 181650-181661Artikel i tidskrift (Refereegranskat)
    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.

  • 42.
    Kanellakis, Christoforos
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Karvelis, Petros
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, 91109.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments2020Ingår i: 21th IFAC World Congress / [ed] Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, Elsevier, 2020, s. 9288-9294Konferensbidrag (Refereegranskat)
    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.

  • 43.
    Kanellakis, Christoforos
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Castaño, Miguel
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Karvelis, Petros
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kominiak, Dariusz
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Where to look: a collection of methods for MAV heading correction in underground tunnels2020Ingår i: IET Image Processing, ISSN 1751-9659, E-ISSN 1751-9667, Vol. 14, nr 10, s. 2020-2027Artikel i tidskrift (Refereegranskat)
    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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  • 44.
    Mansouri, Sina Sharif
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Castaño, Miguel
    Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Drift, underhåll och akustik.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Autonomous MAV Navigation in Underground Mines Using Darkness Contours Detection2019Ingår i: 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, s. 164-174Konferensbidrag (Refereegranskat)
    Abstract [en]

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

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  • 45.
    Kanellakis, Christoforos
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Karvelis, Petros
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Image Enhancing in Poorly Illuminated Subterranean Environments for MAV Applications: A Comparison Study2019Ingår i: 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, s. 511-520Konferensbidrag (Refereegranskat)
    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. 

  • 46.
    Kanellakis, Christoforos
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Karvelis, Petros
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    On Image based Enhancement for 3D Dense Reconstruction of Low Light Aerial Visual Inspected Environments2019Ingår i: Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 2 / [ed] Kohei Arai, Supriya Kapoor, Springer, 2019, s. 265-279Konferensbidrag (Refereegranskat)
    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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  • 47.
    Kanellakis, Christoforos
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Karvelis, Petros
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Open Space Attraction Based Navigation in Dark Tunnels for MAVs2019Ingår i: 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, s. 110-119Konferensbidrag (Refereegranskat)
    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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  • 48.
    Kanellakis, Christoforos
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Mansouri, Sina Sharif
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Georgoulas, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Towards Autonomous Surveying of Underground Mine using MAVs2019Konferensbidrag (Refereegranskat)
    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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  • 49.
    Mansouri, Sina Sharif
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Karvelis, Petros
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kominiak, Dariusz
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Vision-based MAV Navigation in Underground Mine Using Convolutional Neural Network2019Ingår i: IECON 2019: 45th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2019, s. 750-755Konferensbidrag (Refereegranskat)
    Abstract [en]

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

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  • 50.
    Mansouri, Sina Sharif
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Karvelis, Petros
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Kanellakis, Christoforos
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Koval, Anton
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Nikolakopoulos, George
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.
    Visual Subterranean Junction Recognition for MAVs based on Convolutional Neural Networks2019Ingår i: IECON 2019: 45th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2019, s. 192-197Konferensbidrag (Övrigt vetenskapligt)
    Abstract [en]

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

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