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  • 1.
    Bai, Yifan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karlsson, Samuel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Cluster-based Multi-Robot Task Assignment, Planning, and Control2024In: Article in journal (Other academic)
  • 2.
    Bai, Yifan
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karlsson, Samuel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Multi-Robot Task Allocation Framework with Integrated Risk-Aware 3D Path Planning2022In: 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, 2022, p. 481-486Conference paper (Refereed)
    Abstract [en]

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

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  • 3.
    Dahlquist, Niklas
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Saradagi, Akshit
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Reactive Multi-agent Coordination using Auction-based Task Allocation and Behavior Trees2023In: 2023 IEEE Conference on Control Technology and Applications (CCTA), IEEE, 2023, p. 829-834Conference paper (Refereed)
  • 4.
    Karlsson, Samuel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Ensuring Robot-Human Safety for the BD Spot Using Active Visual Tracking and NMPC With Velocity Obstacles2022In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 100224-100233Article in journal (Refereed)
    Abstract [en]

    When humans and robots operate in and occupy the same local space, proximity detection and proactive collision avoidance is of high importance. As legged robots, such as the Boston Dynamics (BD) Spot, start to appear in real-world application environments, ensuring safe robot-human interactions while operating in full autonomy mode becomes a critical gate-keeping technology for trust in robotic workers. Towards that problem, this article proposes a track-and-avoid architecture for legged robots that combines a visual object detection and estimation pipeline with a Nonlinear Model Predictive Controller (NMPC) based on the Optimization Engine, capable of generating trajectories that satisfy the avoidance and tracking problems in real-time operations where the computation time never exceeded 40 ms. The system is experimentally evaluated using the BD Spot, in a custom sensor and computational suite, and in fully autonomous operational conditions, for the robot-human safety scenario of quickly moving noncooperative obstacles. The results demonstrate the efficacy of the scheme in multiple scenarios where the maximum safety distance violation was only 9 cm for an obstacle moving at 2.5 m/s while affected by both state estimation and object detection uncertainty and noise.

  • 5.
    Kottayam Viswanathan, Vignesh
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Satpute, Sumeet Gajanan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Towards Visual Inspection of Distributed and Irregular Structures: A Unified Autonomy Approach2023In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 109, no 2, article id 32Article in journal (Refereed)
    Abstract [en]

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

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  • 6.
    Kottayam Viswanathan, Vignesh
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Satpute, Sumeet Gajanan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Experimental Evaluation of a Geometry-Aware Aerial Visual Inspection Framework in a Constrained Environment2022In: 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, 2022, p. 468-474Conference paper (Refereed)
    Abstract [en]

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

  • 7.
    Kottayam Viswanathan, Vignesh
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Satpute, Sumeet
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    First-look enabled Autonomous Aerial Visual Inspection of Geometrically Fractured Objects in Constrained Environments2022In: 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE), IEEE, 2022, p. 295-300Conference paper (Refereed)
    Abstract [en]

    In this article we propose a novel offline, model-based aerial visual inspection scheme for geometrically fractured large objects, based on fully autonomous Unmanned Aerial Ve-hicles (UAV s), while specifically targeting the case of constrained environments. The proposed framework enables a safe and collision free inspection mission, while guaranteeing a complete visual inspection of the object of interest. The proposed framework employs a novel First - Look approach to generate viewpoints satisfying specific photogrammetric requirements, as well as spatial constraints that are inherently applied by the UAV's state constraints. As it will be presented, i) the First - Look approach allows the UAV to first orient it's view vector towards the nearest available point detected by kd - tree based Nearest Neighbour search on the object, from it's current position, and ii) in the sequel, based on the orientation of the left vector of the camera and the overlap distance, the next viewpoint is projected. This approach is repeated throughout the whole inspection procedure, while the established framework has also the merit to ensure that the inspection path adapts to the shape of the object, which is highly advantageous for the cases of geometrically fractured objects. Multiple realistic and physics based simulation results are presented that prove the efficacy of the proposed scheme.

  • 8.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    On the utilization of Nonlinear MPC for Unmanned Aerial Vehicle Path Planning2021Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This compilation thesis presents an overarching framework on the utilization of nonlinear model predictive control(NMPC) for various applications in the context of Unmanned Aerial Vehicle (UAV) path planning and collision avoidance. Fast and novel optimization algorithms allow for NMPC formulations with high runtime requirement, as those posed by controlling UAVs, to also have sufficiently large prediction horizons as to in an efficient manner integrate collision avoidance in the form of set-exclusion constraints that constrain the available position-space of the robot. This allows for an elegant merging of set-point reference tracking with the collision avoidance problem, all integrated in the control layer of the UAV.

    The works included in this thesis presents the UAV modeling, cost functions, constraint definitions, as well as the utilized optimization framework. Additional contributions include the use case on multi-agent systems, how to classify and predict trajectories of moving (dynamic) obstacles, as well as obstacle prioritization when an aerial agent is in the precense of more obstacles, or other aerial agents, than can reasonably be defined in the NMPC formulation. For the cases of dynamic obstacles and for multi-agent distributed collision avoidance this thesis offers extensive experimental validation of the overall NMPC framework. These works push the limits of the State-of-the-Art regarding real-time real-life implementations of NMPC-based collision avoidance.

    The works also include a novel RRT-based exploration framework that combines path planning with exploration behavior. Here, a multi-path RRT * planner plans paths to multiple pseudo-random goals based on a sensor model and evaluates them based on the potential information gain, distance travelled, and the optimimal actuation along the paths.The actuation is solved for as as the solutions to a NMPC problem, implying that the nonlinear actuator-based and dynamically constrained UAV model is considered as part of the combined exploration plus path planning problem. To the authors best knowledge, this is the first time the optimal actuation has been considered in such a planning problem.

    For all of these applications, the utilized optimization framework is the Optimization Engine: a code-generation framework that generates a custom Rust-based solver from a specified model, cost function, and constraints. The Optimization Engine solves general nonlinear and nonconvex optimization problems, and in this thesis we offer extensive experimental validation of the utilized Proximal-Averaged Newton-type method for Optimal Control (PANOC) algorithm as well as both the integrated Penalty Method and Augmented Lagrangian Method for handling the nonlinear nonconvex constraints that result from collision avoidance problems.

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  • 9.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Reactive Navigation Methods for Autonomous Robots: Safety, Coordination, and Field Deployment2023Doctoral thesis, monograph (Other academic)
    Abstract [en]

    In the new era of intelligent systems, small-scale electronics, and advanced sensors, the application use-cases and operational capabilities of autonomous robots are exponentially increasing. Through their ability to execute complex tasks while relying only on onboard sensors and computation, autonomous field robots are showing promising results in inspection of infrastructure, search-and-rescue or surveillance, maintenance tasks, or in general operations in areas that prove to be dangerous or hazardous for human operators to enter, while also often increasing the efficiency of such tasks. But, to enable robots to autonomously execute their missions the demands on onboard intelligence is increasing rapidly as well. As robot operations move into complex and dynamic environments, into mixed-traffic or multi-robot operational scenarios, or into missions that demand the exploration and navigation of completely unknown areas, a new paradigm of autonomous robot navigation and collision avoidance algorithms need to be developed as well. Towards achieving the vision of autonomous robots performing such tasks for the good of society, this new paradigm of navigation capabilities must first be extended to operate outside of simulation environments, and then to operations in realistic field conditions with all the challenges that comes with that. 

    This thesis presents the development of a series of navigation methods for autonomous robots, with a specific focus on Unmanned Aerial Vehicles (UAVs). The vision of this thesis is to further the application areas of completely autonomous robotic platforms by extending their navigation capabilities: towards avoiding obstacles in their environment both static and dynamic, towards the critical perception-actuation link for reactive navigation, towards exploring and planning dynamic paths through previously unknown areas, and towards the coordination and safety in multi-agent robotic systems. This thesis also has a significant focus in the area of field robotics, meaning the ability to robustify and extend the robots onboard intelligence to handle the harsh conditions of real operations. This thesis will specifically investigate the application of autonomous UAVs in search-and-rescue tasks in subterranean environments, as well as a variety of inspection tasks in underground mines. In these environments the robots must operate completely autonomously without any assisting communication, computation, or perception infrastructure. In all of these areas a special focus has been placed on the real-life experimental validation of results and the required research to reach the readiness stage of such demonstrations, serving as the main motivator for the works presented in this manuscript. 

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  • 10.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Exploration-RRT: A multi-objective Path Planning and Exploration Framework for Unknown and Unstructured Environments2021In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2021, p. 3429-3435Conference paper (Refereed)
    Abstract [en]

    This article establishes the Exploration-RRT algorithm: A novel general-purpose combined exploration and path planning algorithm, based on a multi-goal Rapidly-Exploring Random Trees (RRT) framework. Exploration-RRT (ERRT) has been specifically designed for utilization in 3D exploration missions, with partially or completely unknown and unstructured environments. The novel proposed ERRT is based on a multi-objective optimization framework and it is able to take under consideration the potential information gain, the distance travelled, and the actuation costs, along trajectories to pseudo-random goals, generated from considering the on-board sensor model and the non-linear model of the utilized platform. In this article, the algorithmic pipeline of the ERRT will be established and the overall applicability and efficiency of the proposed scheme will be presented on an application with an Unmanned Aerial Vehicle (UAV) model, equipped with a 3D lidar, in a simulated operating environment, with the goal of exploring a completely unknown area as efficiently and quickly as possible.

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  • 11.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Haluska, Jakub
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    An Adaptive 3D Artificial Potential Field for Fail-safe UAV Navigation2022In: 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, 2022, p. 362-367Conference paper (Refereed)
    Abstract [en]

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

  • 12.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, Pasadena, CA, 91109, USA.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    COMPRA: A COMPact Reactive Autonomy Framework for Subterranean MAV Based Search-And-Rescue Operations2022In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 105, no 3, article id 49Article in journal (Refereed)
    Abstract [en]

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

  • 13.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karlsson, Samuel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Koval, Anton
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Tevetzidis, Ilias
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Haluska, Jakub
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, 91109, United States of America.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Multimodality robotic systems: Integrated combined legged-aerial mobility for subterranean search-and-rescue2022In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 154, article id 104134Article in journal (Refereed)
    Abstract [en]

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

  • 14.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kottayam Viswanathan, Vignesh
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karlsson, Samuel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Satpute, Sumeet Gajanan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Navigation for ARWs2023In: Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications / [ed] George Nikolakopoulos, Sina Sharif Mansouri, Christoforos Kanellakis, Elsevier, 2023, p. 79-108Chapter in book (Other academic)
    Abstract [en]

    This chapter presents an overview of various navigation schemes used for ARWs and their application areas. Navigation schemes, in general, answer the question of how to move from the current position to the desired and optimally plan the path between them, which is a necessary step for almost all applications for autonomous flight. This chapter will go over reactive navigation schemes, such as the potential fields and Model Predictive Control with integrated obstacle avoidance, as well as global path-planning methods, such as map-based iterative planners like D, and planning for complete coverage of infrastructure to perform a visual inspection.

  • 15.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109 USA.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nonlinear MPC for Collision Avoidance and Control of UAVs With Dynamic Obstacles2020In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 5, no 4, p. 6001-6008Article in journal (Refereed)
    Abstract [en]

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

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  • 16.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Haluška, Jakub
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Reactive Navigation of an Unmanned Aerial Vehicle With Perception-Based Obstacle Avoidance Constraints2022In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 30, no 5, p. 1847-1862Article in journal (Refereed)
    Abstract [en]

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

  • 17.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kottayam Viswanathan, Vignesh
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    ARW deployment for subterranean environments2023In: Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications / [ed] George Nikolakopoulos, Sina Sharif Mansouri, Christoforos Kanellakis, Elsevier, 2023, p. 213-243Chapter in book (Other academic)
    Abstract [en]

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

  • 18.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Collision Free Path Planning based on Local 2D Point-Clouds for MAV Navigation2020In: 2020 28th Mediterranean Conference on Control and Automation (MED), IEEE, 2020, p. 538-543Conference paper (Refereed)
    Abstract [en]

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

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  • 19.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Non-linear MPC based Navigation for Micro Aerial Vehicles in Constrained Environments2020In: European Control Conference 2020, IEEE, 2020, p. 837-842Conference paper (Refereed)
    Abstract [en]

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

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    fulltext
  • 20.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Mansouri, Sina Sharif
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Sopasakis, Pantelis
    School of Electronics, Electrical Engineering and Computer Science (EEECS), Queen’s University Belfast. Centre for Intelligent Autonomous Manufacturing Systems (i-AMS), United Kingdom.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Collision Avoidance for Multiple Micro Aerial Vehicles using Fast Centralized Nonlinear Model Predictive Control2020In: 21th IFAC World Congress / [ed] Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, Elsevier, 2020, p. 9303-9309Conference paper (Refereed)
    Abstract [en]

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

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    lindqvist2020centralized
  • 21.
    Lindqvist, Björn
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Sopasakis, Pantelis
    The School of Electronics, Electrical Engineering and Computer Science (EEECS), Queen’s University Belfast and Centre for Intelligent Autonomous Manufacturing Systems (i-AMS).
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A Scalable Distributed Collision Avoidance Scheme for Multi-agent UAV systems2021In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2021, p. 9212-9218Conference paper (Refereed)
    Abstract [en]

    In this article we propose a distributed collisionavoidance scheme for multi-agent unmanned aerial vehicles(UAVs) based on nonlinear model predictive control (NMPC),where other agents in the system are considered as dynamicobstacles with respect to the ego agent. Our control schemeoperates at a low level and commands roll, pitch and thrustsignals at a high frequency, each agent broadcasts its predictedtrajectory to the other ones, and we propose an obstacleprioritization scheme based on the shared trajectories to allowup-scaling of the system. The NMPC problem is solved usingan embedded solver generated by Optimization Engine (OpEn)where PANOC is combined with an augmented Lagrangianmethod to compute collision-free trajectories. We evaluate theproposed scheme in several challenging laboratory experimentsfor up to ten aerial agents, in dense aerial swarms.

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  • 22.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Fresk, Emil
    WideFind AB, Aurorum 1C, Luleå SE-97775, Sweden.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kominiak, Dariusz
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Koval, Anton
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Sopasakis, Pantelis
    School of Electronics, Electrical Engineering and Computer Science (EEECS), Queen’s University Belfast. Centre for Intelligent Autonomous Manufacturing Systems (i-AMS), United Kingdom.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Subterranean MAV Navigation based on Nonlinear MPC with Collision Avoidance Constraints2020In: 21th IFAC World Congress / [ed] Rolf Findeisen, Sandra Hirche, Klaus Janschek, Martin Mönnigmann, Elsevier, 2020, p. 9650-9657Conference paper (Refereed)
    Abstract [en]

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

  • 23.
    Mansouri, Sina Sharif
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Pourkamali-Anaraki, Farhad
    Department of Computer Science, University of Massachusetts Lowell, Lowell, MA 01854 USA.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory, California Institute of Technology Pasadena, Pasadena, CA 91109 USA.
    Burdick, Joel
    Division of Engineering and Applied Sciences, California Institute of Technology, Pasadena, CA 91125 USA.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    A Unified NMPC Scheme for MAVs Navigation With 3D Collision Avoidance Under Position Uncertainty2020In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 5, no 4, p. 5740-5747Article in journal (Refereed)
    Abstract [en]

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

  • 24.
    Patel, Akash
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Banerjee, Avijit
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Design and Model Predictive Control of a Mars Coaxial Quadrotor2022In: 2022 IEEE Aerospace Conference (AERO), IEEE, 2022Conference paper (Refereed)
    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.

  • 25.
    Patel, Akash
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Banerjee, Avijit
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Papadimitriou, Andreas
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Control of ARWs2023In: Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications / [ed] George Nikolakopoulos, Sina Sharif Mansouri, Christoforos Kanellakis, Elsevier, 2023, p. 49-65Chapter in book (Other academic)
    Abstract [en]

    This Chapter focuses on Aerial Robotic Workers (ARWs) various control methods to successfully track the desired states, waypoints, and trajectories. Additionally, this Chapter discusses the regulation from the motor commands level to the accurate tracking of waypoints in 3D space. Various model-based control frameworks are presented based on the modeled dynamics of the Modeling for ARWs (Chapter 3). Initially, a classical Proportional-Integral-Derivative (PID) control scheme is introduced, while in the sequel, a Linear Quadratic Regulator (LQR) and a Model Predictive Controller (MPC) are designed for the linearized dynamics of ARWs. In the sequel, a Nonlinear-MPC (NMPC) version of the simplified position control scheme is given. Finally, a switching MPC is presented for the attitude regulation of a reconfigurable ARW.

  • 26.
    Patel, Akash
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karlsson, Samuel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Haluska, Jakub
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Towards Field Deployment of MAVs in Adaptive Exploration of GPS-denied Subterranean Environments2023In: Article in journal (Other academic)
  • 27.
    Patel, Akash
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karlsson, Samuel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Agha-Mohammadi, Ali-Akbar
    Jet Propulsion Laboratory, California Institute of Technology Pasadena, CA 91109, USA.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Towards energy efficient autonomous exploration of Mars lava tube with a Martian coaxial quadrotor2023In: Advances in Space Research, ISSN 0273-1177, E-ISSN 1879-1948, Vol. 71, no 9, p. 3837-3854Article in journal (Refereed)
    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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  • 28.
    Patel, Akash
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Karlsson, Samuel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Koval, Anton
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Exploration with ARWs2023In: Aerial Robotic Workers: Design, Modeling, Control, Vision, and Their Applications / [ed] George Nikolakopoulos, Sina Sharif Mansouri, Christoforos Kanellakis, Elsevier, 2023, p. 109-127Chapter in book (Other academic)
    Abstract [en]

    This chapter presents an overview of various exploration schemes with single and multi Aerial Robotic Workers (ARWs) and their applications in Search and Rescue, Environmental Monitoring, and planetary exploration missions, under the assumption that the map is partially known or completely unknown. The presented methods in the chapter are in line with the field deployment of the ARWs in subterranean and planetary exploration missions. The addressed questions will include the operating environment configuration and path planning methods for single and multi-robot exploration. The chapter will also briefly present two exploration strategies in terms of frontier and sampling-based exploration algorithms. More specifically, frontier-based and Rapidly Exploring Random Tree (RRT)-based exploration methodologies with results will be explained in detail.

  • 29.
    Patel, Akash
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Agha-mohammadi, Ali-akbar
    Jet Propulsion Laboratory California Institute of Technology Pasadena, CA, 91109.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    REF: A Rapid Exploration Framework for Deploying Autonomous MAVs in Unknown Environments2023In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 108, article id 35Article in journal (Refereed)
    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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  • 30.
    Patel, Akash
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Kanellakis, Christoforos
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Fast Planner for MAV Navigation in Unknown Environments Based on Adaptive Search of Safe Look-Ahead Poses2022In: 2022 30th Mediterranean Conference on Control and Automation (MED), IEEE, 2022, p. 545-550Conference paper (Refereed)
    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.

  • 31.
    Seisa, Achilleas Santi
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Bjorn
    Satpute, Sumeet Gajanan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    An Edge Architecture for Enabling Autonomous Aerial Navigation with Embedded Collision Avoidance Through Remote Nonlinear Model Predictive Control2024In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 188, article id 104849Article in journal (Other academic)
    Abstract [en]

    In this article, we present an edge-based architecture for enhancing the autonomous capabilities of resource-constrained aerial robots by enabling a remote nonlinear model predictive control scheme, which can be computationally heavy to run on the aerial robots' onboard processors. The nonlinear model predictive control is used to control the trajectory of an unmanned aerial vehicle while detecting, and preventing potential collisions. The proposed edge architecture enables trajectory recalculation for resource-constrained unmanned aerial vehicles in relatively real-time, which will allow them to have fully autonomous behaviors. The architecture is implemented with a remote Kubernetes cluster on the edge side, and it is evaluated on an unmanned aerial vehicle as our controllable robot, while the robotic operating system is used for managing the source codes, and overall communication. With the utilization of edge computing and the architecture presented in this work, we can overcome computational limitations, that resource-constrained robots have, and provide or improve features that are essential for autonomous missions. At the same time, we can minimize the relative travel time delays for time-critical missions over the edge, in comparison to the cloud. We investigate the validity of this hypothesis by evaluating the system's behavior through a series of experiments by utilizing either the unmanned aerial vehicle or the edge resources for the collision avoidance mission.

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  • 32.
    Seisa, Achilleas Santi
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Satpute, Sumeet Gajanan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    E-CNMPC: Edge-Based Centralized Nonlinear Model Predictive Control for Multiagent Robotic Systems2022In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 121590-121601Article in journal (Refereed)
    Abstract [en]

    With the wide deployment of autonomous multi-agent robotic systems, control solutions based on centralized algorithms have been developed. Even though these centralized algorithms can optimize the performance of the multi-agent robotic systems, they require a lot of computational effort, and a centralized unit to undertake the entire process. Yet, many robotic platforms like some ground robots and even more, aerial robots, do not have the computing capacity to execute this kind of frameworks on their onboard computers. While cloud computing has been used as a solution for offloading computationally demanding robotic applications, from the robots to the cloud servers, the latency they introduce to the system has made them unsuitable for time sensitive applications. To overcome these challenges, this article promotes an Edge computing-based Centralized Nonlinear Model Predictive Control (E-CNMPC) framework to control, and optimize, in swarm formation, the trajectory of multiple ground robotic agents, while taking under consideration potential collisions. The data processing procedure for the time critical application of controlling the robots in a centralized manner, is offloaded to the edge machine, thus the framework benefits from the provided edge resources, features, and centralized optimal performance, while the latency remains bounded in desired values. Besides, real experiments were conducted as a proof-of-concept of the proposed framework to evaluate the system’s performance and effectiveness.

  • 33.
    Seisa, Achilleas Santi
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Satpute, Sumeet Gajanan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    An Edge-Based Architecture for Offloading Model Predictive Control for UAVs2022In: Robotics, E-ISSN 2218-6581, Vol. 11, no 4, article id 80Article in journal (Refereed)
    Abstract [en]

    Thanks to the development of 5G networks, edge computing has gained popularity in several areas of technology in which the needs for high computational power and low time delays are essential. These requirements are indispensable in the field of robotics, especially when we are thinking in terms of real-time autonomous missions in mobile robots. Edge computing will provide the necessary resources in terms of computation and storage, while 5G technologies will provide minimal latency. High computational capacity is crucial in autonomous missions, especially for cases in which we are using computationally demanding high-level algorithms. In the case of Unmanned Aerial Vehicles (UAVs), the onboard processors usually have limited computational capabilities; therefore, it is necessary to offload some of these tasks to the cloud or edge, depending on the time criticality of the application. Especially in the case of UAVs, the requirement to have large payloads to cover the computational needs conflicts with other payload requirements, reducing the overall flying time and hindering autonomous operations from a regulatory perspective. In this article, we propose an edge-based architecture for autonomous UAV missions in which we offload the high-level control task of the UAV’s trajectory to the edge in order to take advantage of the available resources and push the Model Predictive Controller (MPC) to its limits. Additionally, we use Kubernetes to orchestrate our application, which runs on the edge and presents multiple experimental results that prove the efficacy of the proposed novel scheme.

  • 34.
    Seisa, Achilleas Santi
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Satpute, Sumeet
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Lindqvist, Björn
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nikolakopoulos, George
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    An Edge Architecture Oriented Model Predictive Control Scheme for an Autonomous UAV Mission2022In: 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE), IEEE, 2022, p. 1195-1201Conference paper (Refereed)
    Abstract [en]

    In this article the implementation of a controller and specifically of a Model Predictive Controller (MPC) on an Edge Computing device, for controlling the trajectory of an Unmanned Aerial Vehicle (UAV) model, is examined. MPC requires more computation power in comparison to other controllers, such as PID or LQR, since it use cost functions, optimization methods and iteratively predicts the output of the system and the control commands for some determined steps in the future (prediction horizon). Thus, the computation power required depends on the prediction horizon, the complexity of the cost functions and the optimization. The more steps determined for the horizon the more efficient the controller can be, but also more computation power is required. Since sometimes robots are not capable of managing all the computing process locally, it is important to offload some of the computing process from the robot to the cloud. But then some disadvantages may occur, such as latency and safety issues. Cloud computing may offer “infinity” computation power but the whole system suffers in latency. A solution to this is the use of Edge Computing, which will reduce time delays since the Edge device is much closer to the source of data. Moreover, by using the Edge we can offload the demanding controller from the UAV and set a longer prediction horizon and try to get a more efficient controller.

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