Efficient information enhanced RRT* motion planning for SWC robots

Abstract:

Self-reconfigurable wave-like crawling (SWC) robots, characterized by their unique serial/parallel connection states, imposes stringent requirements for generating continuous and feasible trajectories during motion planning. Conventional motion planning algorithms suffer from inefficiency and fail to satisfy kinematic constraints. To address these limitations, this paper presents an enhanced motion planning method termed as informed optimal rapid-exploration random tree. First, the rapidly-exploring random tree connect (RRT-Connect) algorithm is employed to generate an initial feasible path and construct an elliptical state-space sampling domain, facilitating rapid expansion of the random tree. Besides, polynomial trajectories are optimized based on a minimized jerk objective function and Hessian matrix to generate smooth motion profiles that conform to the kinematic characteristics of the SWC robot. Finally, simulations conducted in diverse obstacle scenarios validate the effectiveness of the optimized algorithm. The results demonstrate that, compared to traditional algorithms, the proposed method significantly enhances path planning efficiency across various obstacle environments, reducing global sampling time and shortening planned path length. Furthermore, it effectively mitigates abrupt acceleration and sharp turns during the robot’s movement, yielding paths that are better suited for practical operational applications.