Efficient Global Trajectory Planning for Multi-robot System with Affinely Deformable Formation
Global trajectory planning is crucial for long-range formation navigation tasks of multi-robot systems in efficiency improvement and energy saving, whose main challenges are the joint space constraints of the whole team and the long-range deployment. To overcome the above difficulties, we reformulate the original problem into an affine formation planning problem in parameter space. Further, we propose a front-end & back-end framework for global trajectory planning of Multi-Robot Systems (MRS) with affinely deformable formation. For the front-end, an RL-steering affine formation RRT* method is designed to search a global formation-level trajectory in affine parameter space, combining the efficient BVP-solving capability of RL and the global guidance and generalizing ability of RRT*. For the back-end, we propose a formationlevel affine parameter trajectory optimization method to refine the front-end trajectory, and further transform it into peragent trajectories for execution. Extensive benchmarks and ablation experiments in simulation show the effectiveness of our framework for the global trajectory generation of a multiUAV system with affinely deformable formation. The appendix can be seen here<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup>.
Authors
- Rong Xiong (ORCID: https://orcid.org/0000-0001-9318-9014)
- Jun Wu (ORCID: https://orcid.org/0000-0002-1388-7451)
- Yuxiang Cui (ORCID: https://orcid.org/0000-0001-8976-8865)
- Dongkun Zhang (ORCID: https://orcid.org/0000-0002-0811-4964)
- Hao Sha (ORCID: https://orcid.org/0000-0003-3199-0732)
- Yue Wang (ORCID: https://orcid.org/0000-0003-0146-7262)
- Chaoqun Wang (ORCID: https://orcid.org/0000-0002-2127-4110)
- Wangtao Lu (ORCID: https://orcid.org/0009-0007-2652-4450)
Institutions
- Shandong University (CN)
- Zhejiang University of Technology (CN)
Publication Details
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1109/iros58592.2024.10802518
- Citations
- 1
- Primary Topic
- Robotic Path Planning Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.25
Funders
- Nature