融合改进RRT和DWA算法的移动机器人路径规划

Mobile Robot Path Planning on Fusion of Improve RRT and DWA Algorithm

针对传统局部路径规划存在非全局最优、易陷入困境、导航效率低等问题,这里提出了一种将改进RRT(RapidlyExploring Random Trees)算法和动态窗口法融合的算法。首先优化基本RRT算法的采样策略,使用三次贝塞尔曲线平滑所生成的全局路径。然后改进DWA(Dynamic Window Approach)算法的轨迹评价函数,构造路径最优的目标函数,以保证路径规划最优,从而提高移动机器人的避障性能。最后使用ROS(Robot Operating System)平台进行仿真验证,实验结果表明,与A^(*)-DWA和Dijkstra-DWA相比,所提出算法在复杂环境下路径长度更短、路径质量更优,行进时间明显减少,移动机器人的平均速度较A^(*)-DWA算法提高了约16.8%,证明了算法的有效性和实用性。

Aiming at the problems of non-global optimization,low obstacle avoidance capability and navigation efficiency in traditional local path planning algorithms,a fusion algorithm combining the improved RRT(Rapid Exploring Random)and DWA(Dynamic Window Approach)algorithm is proposed.After optimizing the sampling strategies in RRT algorithm and using cubic-Bezier curve to smooth the global path,on basis of improving the evaluation function in DWA algorithm,we construct the objective function for path planning optimization and obstacle avoidance capability.The experimental results on ROS(Robot Operating System)platform demonstrate that the proposed algorithm achieves better performance of shorter path length,better path quality and less traveling time in complex environments,compared with A^(*)-DWA and Dijkstra DWA algorithm.The average speed of the mobile robot is increased by about 16.8%compared with A^(*)-DWA,which proves the effectiveness and practicability of the proposed algorithm.