Motion Planning for Heterogeneous Unmanned Systems under Partial Observation from UAV

Chen, Ci; Wan, Yuanfang; Li, Baowei; Wang, Chen; Xie, Guangming; Jiang, Huanyu

doi:10.1109/iros45743.2020.9341326

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…In some approaches, the path planning was performed directly on the image data without map construction, with calculations being done either on the UAV [14] or in the cloud [15]. In [16], the authors proposed to solve the path planning as a Partially Observable Markov Decision Process (POMDP) without global map construction and evaluated their approach in simulations. In [17], the UAV sent only target positions to the UGV, which performed motion planning on its own sensory data.…”

Section: B Related Workmentioning

confidence: 99%

Cooperative Navigation and Guidance of a Micro-Scale Aerial Vehicle by an Accompanying UAV using 3D LiDAR Relative Localization

Pritzl

Vrba

Štěpán

et al. 2022

2022 International Conference on Unmanned Aircraft Systems (ICUAS)

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Section: B Related Workmentioning

confidence: 99%

Cooperative Navigation and Guidance of a Micro-Scale Aerial Vehicle by an Accompanying UAV using 3D LiDAR Relative Localization

Pritzl

Vrba

Štěpán

et al. 2022

2022 International Conference on Unmanned Aircraft Systems (ICUAS)

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“…By sharing their capacities, such systems aim to gain in flexibility, efficiency and robustness and tend to achieve total autonomy in dynamic environments. Some frameworks for cooperative air and ground vehicles have been mostly used for collaborative path-planning and obstacle avoidance [1]- [2], target tracking [3], exploration and mapping missions [4] and surveillance applications [5].…”

Section: Introductionmentioning

confidence: 99%

Cooperative architecture using air and ground vehicles for the search and recognition of targets

Theunissen,

Pousseur,

Castillo

et al. 2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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A cooperative navigation architecture for the search and recognition of targets using aerial and ground vehicles is proposed in this paper. The architecture allows to manage aerial and ground vehicles to autonomously perform different tasks in an independent or cooperative way. For our application, two main tasks are conceived; aerial monitoring of a surface to search for targets, and target ground recognition. In the target aerial detection task, the aerial drone tracks autonomously a trajectory, computed to cover all the surface to monitoring, to search for targets using vision algorithms. Once one of them is detected its relative position is sent to the cooperative architecture. After the aerial drone has covered the entire area, the architecture computes and assigns to each ground vehicle the closest target found. Then, each ground vehicle navigates autonomously avoiding obstacles (if presents) to its assigned target. For verifying the success of the mission, the aerial vehicle flies following the dynamic center of mass of the ground vehicles. Real-time experiments are carried out to validate the proposed architecture. Main results, depicted in some graphs, corroborate the good performance in closed loop.

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“…A 1.8-kg helicopter equipped with two counterrotating, concentric rotors, Ingenuity is capable of autonomous flights and will inform the design of future extraplanetary drones. Similarly, Chen et al(71) presented a motion planning strategy for an (Earth-based) system comprising both drones and ground vehicles that treats the planner as a partially observable Markov decision process. Booth et al(72) combined ground-based, mobile recharging robots with drones, specifically focusing on multiple drones searching for a target, with the ground-based charging robots constrained by a road network.…”

mentioning

confidence: 99%

Design and Control of Drones

Mueller

Lee

D’Andrea

2022

Annu. Rev. Control Robot. Auton. Syst.

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The design and control of drones remain areas of active research, and here we review recent progress in this field. In this article, we discuss the design objectives and related physical scaling laws, focusing on energy consumption, agility and speed, and survivability and robustness. We divide the control of such vehicles into low-level stabilization and higher-level planning such as motion planning, and we argue that a highly relevant problem is the integration of sensing with control and planning. Lastly, we describe some vehicle morphologies and the trade-offs that they represent. We specifically compare multicopters with winged designs and consider the effects of multivehicle teams. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 5 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Motion Planning for Heterogeneous Unmanned Systems under Partial Observation from UAV

Cited by 5 publications

References 11 publications

Cooperative Navigation and Guidance of a Micro-Scale Aerial Vehicle by an Accompanying UAV using 3D LiDAR Relative Localization

Cooperative Navigation and Guidance of a Micro-Scale Aerial Vehicle by an Accompanying UAV using 3D LiDAR Relative Localization

Cooperative architecture using air and ground vehicles for the search and recognition of targets

Design and Control of Drones

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