Abstract
This paper proposes a decentralized trajectory planning framework for the collision avoidance problem of multiple micro aerial vehicles (MAVs) in environments with static and dynamic obstacles. The framework utilizes spatiotemporal occupancy grid maps (SOGM), which forecast the occupancy status of neighboring space in the near future, as the environment representation. Based on this representation, we extend the kinodynamic A∗ and the corridor-constrained trajectory optimization algorithms to efficiently tackle static and dynamic obstacles with arbitrary shapes. Collision avoidance between communicating robots is integrated by sharing planned trajectories and projecting them onto the SOGM. The simulation results show that our method achieves competitive performance against state-of-the-art methods in dynamic environments with different numbers and shapes of obstacles. Finally, the proposed method is validated in real experiments.
Original language | English |
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Title of host publication | Proceedings of the IEEE International Conference on Robotics and Automation, ICRA 2024 |
Publisher | IEEE |
Pages | 7208-7214 |
Number of pages | 7 |
ISBN (Electronic) | 979-8-3503-8457-4 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 IEEE International Conference on Robotics and Automation, ICRA 2024 - Yokohama, Japan Duration: 13 May 2024 → 17 May 2024 |
Publication series
Name | Proceedings - IEEE International Conference on Robotics and Automation |
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ISSN (Print) | 1050-4729 |
Conference
Conference | 2024 IEEE International Conference on Robotics and Automation, ICRA 2024 |
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Country/Territory | Japan |
City | Yokohama |
Period | 13/05/24 → 17/05/24 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.