摘要
针对应用双向快速扩展随机树(Bi-RRT)算法进行复杂环境路径规划时,存在采样效率低、冗余节点以及无法实时避障等问题提出一种将改进Bi-RRT与改进动态窗口法(DWA)融合的算法。算法采用双向自适应扩展策略,加快收敛速度以及提高采样效率。引入基于A-算法的节点最优选择及路径优化策略,提高目标导向性和缩短路径长度。改进DWA算法中的评价函数,把全局偏航角与全局路径距离加入评估函数,对轨迹方向的评价更加多样化,实现机器人高效动态避障。融合改进Bi-RRT与改进DWA算法,在Matlab与ROS平台进行实验仿真,结果表明,所提融合算法规划路径短、效率高且能有效避开未知障碍物。
In complex environments,the bidirectional fast expanding random tree(Bi-RRT)algorithm,exists problems of low sampling efficiency,redundant nodes and inability in avoiding obstacles,hence,an algorithm based on the fusion of improved Bi-RRT and improved dynamic window method(DWA)is proposed.Firstly,the improved Bi-RRT algorithm adopts the strategy of bidirectional adaptive expansion,which accelerates the convergence speed and improves the sampling efficiency,and introduces the node optimal selection and path optimization strategy based on the A~*algorithm to improve the goal orientation and shorten the path length.Then,the evaluation function in the DWA algorithm is improved,and the global yaw angle and global path distance are added to the evaluation function,so as to diversify the evaluation of the trajectory direction and realize the efficient dynamic obstacle avoidance of the robot.Finally,the improved Bi-RRT and improved DWA algorithm are fused,based on MATLAB and ROS platform,the fused algorithm is simulated to verify that the proposed algorithm has a short planning path,high efficiency and can effectively avoid unknown obstacles.
作者
刘越
王天笑
柴秋月
刘芳
LIU Yue;WANG Tianxiao;CHAI Qiuyue;LIU Fang(College of Electrical and Electronic Engineering,Changchun University of Technology,Changchun 130012,China)
出处
《实验室研究与探索》
CAS
北大核心
2024年第5期77-83,共7页
Research and Exploration In Laboratory
基金
吉林省科技发展计划项目(20220204090YY)
吉林省长春市科技发展计划项目(23ZCX04)。
