Decentralized Multi-Agent Trajectory Planning in Dynamic Environments with Spatiotemporal Occupancy Grid Maps

Conference Paper (2024)
Author(s)

Siyuan Wu (Student TU Delft)

G. Chen (TU Delft - Learning & Autonomous Control)

Moji Shi (Student TU Delft)

J. Alonso-Mora (TU Delft - Learning & Autonomous Control)

Research Group
Learning & Autonomous Control
DOI related publication
https://doi.org/10.1109/ICRA57147.2024.10610670
More Info
expand_more
Publication Year
2024
Language
English
Research Group
Learning & Autonomous Control
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-care Otherwise 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.@en
Pages (from-to)
7208-7214
ISBN (electronic)
979-8-3503-8457-4
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

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.

Files

Decentralized_Multi-Agent_Traj... (pdf)
(pdf | 5.63 Mb)
- Embargo expired in 08-02-2025
License info not available