未知环境的四足爬壁机器人足端轨迹双向蚁群规划方法
Bidirectional Ant Colony Planning Method for Foot End Trajectories of Quadruped Wall Climbing Robots in Unknown Environments
王坚 1贺建军2
作者信息
- 1. 辽宁对外经贸学院,辽宁大连 116000
- 2. 大连民族大学,辽宁大连 116000
- 折叠
摘要
针对未知环境中的障碍物导致四足爬壁机器人爬壁作业的运动协调角度变化程度较大,加大了单向蚁群规划算法下爬壁机器人轨迹规划执行难度,设计一种双向蚁群规划方法,并应用到未知环境的四足爬壁机器人足端轨迹规划中.基于四足爬壁机器人结构,结合运动协调角度获取更新后的信息素浓度.通过构建正向蚂蚁群体和反向蚂蚁群体两个蚂蚁群体,分别从起点和终点开始搜索最优路径,并通过信息素浓度更新和转移概率计算不断更新路径.根据轨迹选择概率并添加启发式函数促使两个蚂蚁群体路径交叉,形成一条完整的足端轨迹,实现四足爬壁机器人足端轨迹双向蚁群规划.经过实验证明,所提方法能有效地实现四足爬壁机器人在未知环境中足端轨迹规划,规划结果具有较高的可行性和优越性,迭代收敛速度快.
Abstract
A bidirectional ant colony planning method is designed to address the significant changes in the motion coordination angle of a quadruped wall climbing robot during wall climbing operations caused by obstacles in an unknown environment,which increases the difficulty of executing trajectory planning for wall climbing robots using one-way ant colony planning algorithms.This method is applied to the foot end trajectory planning of quadruped wall climbing robots in unknown environments.Based on the structure of the quadruped wall climbing robot,the updated pheromone concentration is obtained by combining the motion coordination angle.By constructing positive ant colony and reverse ant colony,the optimal path is searched from the starting point and the end point,and the path is constantly updated through pheromone concentration update and transfer probability calculation.Based on the trajectory selection probability and adding a heuristic function,two ant colony paths intersect to form a complete foot trajectory,to achieve bidirectional ant colony planning for the foot trajectory of a quadruped wall climbing robot.The experimental results have shown that the proposed method can effectively achieve foot trajectory planning for quadruped wall climbing robots in unknown environments.The planning results have high feasibility and superiority,and the iteration convergence speed is fast.
关键词
爬壁机器人/轨迹规划/双向蚁群搜索/路径规划/足端轨迹Key words
wall climbing robot/trajectory planning/bidirectional ant colony search/path planning/foot trajectory引用本文复制引用
基金项目
辽宁省自然科学基金(2020-MS-134)
出版年
2024
NETL
NSTL
万方数据