SWC机器人高效信息增强RRT*运动规划

Efficient information enhanced RRT* motion planning for SWC robots

摘要:

自重构波状爬行(self-reconfiguration wave-like crawling,SWC)机器人具有特殊的串/并联连接状态,其在运动规划过程中更需要生成连续可行的轨迹。传统运动规划算法存在效率低下、生成路径不符合运动学约束的问题。本文提出了一种改进的信息增强快速探索随机树运动规划方法。首先以RRT-Connect(rapidly-exploring random tree connect)算法为基础,生成初始可行路径,构建椭圆状态空间采样域,实现随机树的快速生长。其次,基于最小化的加加速度目标函数和Hessian矩阵优化多项式轨迹,生成符合SWC机器人运动学特性的平滑轨迹。最后,基于不同的障碍物场景进行路径规划仿真,以验证优化后算法的效果。仿真结果表明,相较于传统算法,所提方法在多种障碍物环境中可显著提升路径规划效率,缩短全局采样时间和规划路径长度,并可有效地避免SWC机器人运动过程中的急加速转弯,消除路径中的尖锐转折,更符合实际作业需求。

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.