Tube-RRT*: Efficient Homotopic Path Planning for Swarm Robotics Passing-Through Large-Scale Obstacle Environments
<|reference_start|>Tube-RRT*: Efficient Homotopic Path Planning for Swarm Robotics Passing-Through Large-Scale Obstacle Environments: Recently, the concept of optimal virtual tube has emerged as a novel solution to the challenging task of navigating obstacle-dense environments for swarm robotics, offering a wide ranging of applications. However, it lacks an efficient homotopic path planning method in obstacle-dense environments. This paper introduces Tube-RRT*, an innovative homotopic path planning method that builds upon and improves the Rapidly-exploring Random Tree (RRT) algorithm. Tube-RRT* is specifically designed to generate homotopic paths for the trajectories in the virtual tube, strategically considering opening volume and tube length to mitigate swarm congestion and ensure agile navigation. Through comprehensive comparative simulations conducted within complex, large-scale obstacle environments, we demonstrate the effectiveness of Tube-RRT*.<|reference_end|>
@article{mao2024tube,
    title={Tube RRT*: Efficient Homotopic Path Planning for Swarm Robotics
  Passing-Through Large-Scale Obstacle Environments},
    author={Pengda Mao, Shuli Lv and Quan Quan},
    journal={arXiv preprint arXiv:2404.09200},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09200},
    primaryClass={cs.RO cs.SY eess.SY}
}