Tube RRT*: Efficient Homotopic Path Planning for Swarm Robotics Passing-Through Large-Scale Obstacle Environments

Recently, the concept of homotopic trajectory planning has emerged as a novel solution to navigation in large-scale obstacle environments for swarm robotics, offering a wide range of applications. However, it lacks an efficient homotopic path planning method in large-scale obstacle environments. This letter introduces Tube RRT*, an innovative homotopic path planning method that builds upon and improves the Rapidly-exploring Random Tree (RRT) algorithm. Tube RRT* is designed to efficiently generate homotopic paths belonging to the same homotopy class and simultaneously considers gap volume and path length to mitigate swarm congestion and enable agile navigation. Through comprehensive simulations and experiments, the effectiveness of Tube RRT* is validated.

I. Introduction

In Recent years, swarm robotics has emerged as a promising application in various fields, including search and rescue operations, environmental monitoring, agriculture, exploration, and logistics. A current focus is on determining the safe, reliable, and smooth movement of swarm robotics within large-scale obstacle environments. To address the challenge of swarms navigating large-scale obstacles, trajectory planning [1], control-based approaches [2], and virtual tube methods [3] have been explored. Trajectory planning ensures smooth movement but is computationally intensive in complex environments, while control-based methods are simpler and cost-efficient but lack predictive capabilities and smoothness. Combining their strengths, the optimal virtual tube method [4] enables efficient swarm navigation with low computation costs through centralized planning and distributed control.