基于离散采样的多模态四足机器人路径规划

Path planning for multimodal quadruped robots based on discrete sampling

针对双向快速扩展随机树(RRT-Connect)在多模态四足机器人路径规划中存在不必要的跳跃和行走部分路径的地形起伏程度大及转向角度变化大的问题,提出了一种基于离散采样的解决方案。预处理路径去除不必要的跳跃,离散采样并动态规划获得粗解,使用B样条曲线拟合并二次规划得到最终路径。仿真结果表明,本文方法规划出的路径使得机器人对质心高度的调节平均减少了31.4%,途径地形的起伏程度减小13.4%,地形倾斜角度变化降低11.4%,转向角度变化减小62.7%,验证了本文方法的有效性。

Aiming at the challenges of unnecessary leap and significant undulations terrains with large steering angles in path planning of multimodal quadruped robots by Rapidly Exploring Random Tree algorithm,a path planning algorithm solution based on discrete sampling is proposed.The path is preprocessed to remove unnecessary leap and a solution set is obtained by discrete sampling and dynamic programming method.B-spline curves are used to define spline segments and quadratic programming method is used to optimize the final path.The simulation results show that paths planned by the proposed method exhibit an average reduction of 31.4%in the adjustment of robot's center of mass height,a 13.4%decrease in undulation of terrain,an 11.4%reduction in terrain slope angle and a 62.7%reduction in steering angle,which affirm the effectiveness of the proposed method.