改进D^(*)算法的未知场景机器人运动规划

Motion planning for robots in unknown scenes based on improved D^(*)algorithm

随着机器人技术不断发展,自主移动机器人的应用已拓展到复杂未知环境中。针对传统运动规划算法在复杂未知环境中存在搜索盲目、计算效率低、难获得安全轨迹等问题,本文提出了一种基于改进D^(*)算法的运动规划方法。其中,前端路径规划使用融合跳跃点搜索(JPS)思想的D^(*)算法,后端轨迹优化基于B样条构建二次规划问题。利用矩阵实验室(Matlab)与机器人操作系统(ROS)的仿真平台进行实验,结果表明,改进D^(*)算法在30×30的栅格地图中,相比传统D^(*)算法、FocussedD^(*)、有向D^(*)算法搜索耗时减少0.297、0.269、0.191s;动态障碍物存在时,可使移动机器人快速、安全运动至目标点。

As robotics continues to evolve,the applications of autonomous mobile robots have expanded into complex and unknown environments.However,traditional motion planning algorithms in this environment suffer from blind search,low computational efficiency,and difficulty in obtaining safe trajectories.In this paper,a motion planning method based on the improved D^(*)algorithm is proposed.The front-end path planning uses the D^(*)algorithm incorporating the idea of jump point search(JPS),and the back-end trajectory optimization is based on the B-spline to construct a quadratic planning problem.Simulation experiments are conducted using Matlab and ROS-Gazebo platforms.And the results show that the improved D^(*)algorithm takes 0.297,0.269,and 0.191 s less time to search in a 30×30 grid map compared to the traditional D^(*)algorithm,Focussed D^(*),and directed D^(*)algorithm,respectively;and the proposed method enables the mobile robot to move safely and fast to the target point in the presence of dynamic obstacles.