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作 者:张立彬[1] 林后凯 谭大鹏[1] ZHANG Libin;LIN Houkai;TAN Dapeng(College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China)
机构地区:[1]浙江工业大学机械工程学院,浙江杭州310014
出 处:《计算机集成制造系统》2022年第6期1638-1649,共12页Computer Integrated Manufacturing Systems
基 金:国家重点研发计划资助项目(2018YFB1309404);国家自然科学基金资助项目(52175124)。
摘 要:为了使移动机械臂在不同危险环境下用较短的搜索时间快速规划较优的避障路径,提出一种基于栅格空间的自适应目标偏向快速搜索随机树算法。结合快速搜索随机树算法渐进最优思想对目标偏向快速搜索随机树算法进行优化,使得搜索路径朝渐进最优解收敛。由于在高维度关节空间内查找相邻节点的计算量较大,通过栅格储存树节点并结合栅格快速查找相邻节点来提升算法效率。利用open表解决目标偏向采样策略引起的重复偏向问题。针对不同环境中偏向阈值难确定的问题提出自适应目标偏向法,结合open表节点的数量变化自适应控制偏向性生长来缩减无效扩展,降低搜索时间和路径代价。为进一步改善路径曲折和代价,采用可变间隔的贪心算法对已规划的路径进行快速优化。仿真实验将所提方法用于不同障碍物环境,结果显示改进算法可以有效缩减搜索时间和路径代价,提升规划稳定性。To make the manipulator quickly plans a better obstacle avoidance path under different risk assembly,a rapidly exploring random tree star algorithm for adaptive goal bias based on grid space(SAGB_RRT*)was proposed.The Goal Bias Rapidly exploring Random Tree algorithm(GB_RRT)was optimized according to Rapidly exploring Random Tree star(RRT*)asymptotic optimization to make the search path converge towards the optimal solution.As to the issue that developed GB_RRT*required a large amount of calculation to traverse and search adjacent nodes in the joint space,grids was adopted to quickly find adjacent nodes and storage tree node for speeding up the algorithm calculation.Openlist was utilized to address the problem of repeated bias caused by target bias sampling strategy.In view of the difficulty of determining the target bias threshold in different environments,an adaptive target bias method was proposed,which enabled the algorithm to change growth strategy in real time according to the openlist feedback and hence reduce the algorithm Invalid extension,search time and path cost.To further reduce the path twists and costs,the greedy algorithm with variable interval was used to quickly optimize the path planned by SAGB_RRT*within limited time.In the simulation experiment,the proposed method was applied to the path planning of manipulators in different complex environments.The experimental results showed that the proposed algorithm could effectively decrease the search time and path cost and improve the planning stability.
分 类 号:TP242[自动化与计算机技术—检测技术与自动化装置]
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