| 1. |
- Kanellakis, Christoforos, et al.
(författare)
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Towards Autonomous Aerial Scouting Using Multi-Rotors in Subterranean Tunnel Navigation
- 2021
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 66477-66485
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Kanellakis, Christoforos, et al.
(författare)
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Vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 3. |
- Kanellakis, Christoforos, et al.
(författare)
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Vision-driven NMPC for Autonomous Aerial Navigation in Subterranean Environments
- 2020
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Ingår i: 21th IFAC World Congress. - : Elsevier. ; , s. 9288-9294
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Konferensbidrag (refereegranskat)abstract
- 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.
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| 4. |
- Kottayam Viswanathan, Vignesh, et al.
(författare)
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Towards a Reduced Dependency Framework for Autonomous Unified Inspect-Explore Missions
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- The task of establishing and maintaining situational awareness in an unknown environment is a critical step to fulfil in a mission related to the field of rescue robotics. Predominantly, the problem of visual inspection of urban structures is dealt with view-planning being addressed by map-based approaches. In this article, we propose a novel approach towards effective use of Micro Aerial Vehicles (MAVs) for obtaining a 3-D shape of an unknown structure of objects utilizing a map-independent planning framework. The problem is undertaken via a bifurcated approach to address the task of executing a closer inspection of detected structures with a wider exploration strategy to identify and locate nearby structures, while being equipped with limited sensing capability. The proposed framework is evaluated experimentally in a controlled indoor environment in presence of a mock-up environment validating the efficacy of the proposed inspect-explore policy.
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| 5. |
- Koval, Anton, et al.
(författare)
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Dataset collection from a SubT environment
- 2022
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Ingår i: Robotics and Autonomous Systems. - : Elsevier. - 0921-8890 .- 1872-793X. ; 155
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Lindqvist, Björn, et al.
(författare)
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COMPRA: A COMPact Reactive Autonomy Framework for Subterranean MAV Based Search-And-Rescue Operations
- 2022
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Ingår i: Journal of Intelligent and Robotic Systems. - : Springer. - 0921-0296 .- 1573-0409. ; 105:3
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Lindqvist, Björn, et al.
(författare)
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Exploration-RRT: A multi-objective Path Planning and Exploration Framework for Unknown and Unstructured Environments
- 2021
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Ingår i: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - : IEEE. ; , s. 3429-3435
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Konferensbidrag (refereegranskat)abstract
- 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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| 8. |
- Lindqvist, Björn, et al.
(författare)
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Multimodality robotic systems: Integrated combined legged-aerial mobility for subterranean search-and-rescue
- 2022
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Ingår i: Robotics and Autonomous Systems. - : Elsevier. - 0921-8890 .- 1872-793X. ; 154
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 9. |
- Lindqvist, Björn, et al.
(författare)
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Nonlinear MPC for Collision Avoidance and Control of UAVs With Dynamic Obstacles
- 2020
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 5:4, s. 6001-6008
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 10. |
- Mansouri, Sina Sharif, et al.
(författare)
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A Unified NMPC Scheme for MAVs Navigation With 3D Collision Avoidance Under Position Uncertainty
- 2020
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 5:4, s. 5740-5747
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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