步态数据挖掘在爬壁机器人路径规划中的应用

Application of Gait Data Mining in Path Planning of Wall Climbing Robot

为减少不确定因素对爬壁机器人工作进程的影响,保障其在面对固定障碍物或移动障碍物时也能规划出最优路线,提出了越障步态数据挖掘下实时路径规划方法。利用蚁群搜索可行路径,通过更新信息素并加入避障策略从蚁群搜索路径中择优出无障碍最优解,同时利用声呐实时探测移动障碍物,使机器人能够动态判断突发障碍并作出有效避让行为,最后利用贝塞尔曲线对规划路径平滑处理,使机器人移动行为更加灵敏减少耗能。实验结果表明,所提算法的规划路径更为平滑,折点较少,算法收敛稳定且迅速,方法有效可行。

In order to reduce the influence of uncertain factors on the working process of wall climbing robot and ensure that it can plan the optimal route when facing fixed obstacles or moving obstacles,a real-time path planning method based on obstacle surmounting gait data mining is proposed.Ant colony is used to search the feasible path,and the obstacle free optimal solution is selected from the ant colony search path by updating pheromone and adding obstacle avoidance strategy,At the same time,sonar is used to detect moving obstacles in real time,so that the robot can dynamically judge sudden obstacles and make effective avoidance behavior.Finally,Bessel curve is used to smooth the planned path,so as to make the robot's moving behavior more sensitive and reduce energy consumption.Experimental results show that the proposed algorithm has smoother planning path,fewer break points,stable and rapid convergence,and the method is effective and feasible.