摘要
为实现在人难以适应的环境中进行资源勘探和采样任务,对采样机器人车体进行全局路径规划与局部路径规划,对由人驾驶汽车的采样方式进行辅助与补充。采用A*算法和Dijkstra算法分别进行全局路径规划,并用前向实验的方法对规划出的路径进行优化,弥补了基于栅格的路径规划过程的缺陷;采用基于车体转向速度随状态机改变的避障路径规划方法进行局部路径规划。实验结果表明,全局路径规划算法计算得到的路径经过优化后长度更短,局部路径规划方法能够适应实际避障工况。
In order to realize resource exploration and sampling tasks in an environment that is difficult for humans to adapt to,global path planning and local path planning are carried out for the sampling robot body,and the sampling method of human-driven cars is assisted and supplemented.The global path planning part adopts the A*algorithm and Dijkstra algorithm optimized by the forward experiment method for planning,which makes up for the defects of the grid-based path planning process;Obstacle avoidance path planning method.The experimental results show that the path calculated by the global path planning algorithm is shorter after optimization,and the local path planning method can adapt to the actual obstacle avoidance conditions.
作者
陈思成
纪玉杰
路达
Chen Si-cheng;Ji Yu-jie;Lu Da(Shenyang Ligong University,Shenyang 110159,China;Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang 110016,China)
出处
《内燃机与配件》
2022年第22期96-98,共3页
Internal Combustion Engine & Parts
关键词
采样机器人
路径规划
前向实验法
车体避障
Sampling robot
Path planning
Forward experiment method
Vehicle obstacle avoidance
作者简介
陈思成(1998-)男,汉族,辽宁省沈阳市人,硕士研究生,研究方向:现代机电装备及自动化技术。
