Time-Varying Multi-Goal Path Planning with Multi-Tree RRT* Algorithm for Quadruped Robots

Quadruped robots are being widely deployed in various scenarios with uneven terrains, such as rescue and supervision, due to their ability to climb obstacles and carry heavy loads. However, these robots struggle when faced with complex tasks that require reaching time-varying multiple target locations or landmarks. The visiting order of the landmarks and the total travel cost can significantly impact their overall work efficiency. The situation is worsened by limited on-board computing resources, restricted battery storage, and real-time computing demand for navigation systems. To address this issue, we propose a novel approach of multi-goal path planning specifically designed for quadruped robots. Our system extends the conventional Rapidly-Exploring Random Tree (RRT) algorithm to optimize the visiting order of multi-goal while taking into account the kinematics and safety-distance of quadruped robots. Simulation and experimental results demonstrate that our proposed multi-goal path planning system is more efficient than traditional methods in navigating complex tasks while reaching each sub-target position.