基于云模型的农业移动机器人人机合作路径规划

【目的】实现农业移动机器人在复杂动态的农业环境中实时准确地无碰撞行驶。【方法】基于云模型的不确定性在线推理方法,提出一种基于云模型的动态引导A*(CDGA*)算法进行人机合作路径规划,将人的专业知识和喜好等引入DGA*优化中,实现机器人更快速的路径规划。利用Matlab软件对CDGA*算法与DGA*算法进行仿真对比分析。【结果】静态路径规划中,DGA*算法与CDGA*算法的close的点数分别为158和96,人员规划时间分别为8.8和4.0 s,规划总时间分别为15.6和8.9 s;动态路径规划中,DGA*算法与CDGA*算法的人员规划时间分别为12.5和5.8 s,规划总时间分别为23.3和14.6 s。【结论】提出的CDGA*算法能够大大减少产生的节点数,缩短规划时间,提高搜索效率。

Human-machine cooperative path planning of an agricultural mobile robot based on a cloud model

Objective To make an agricultural robot accurately find a path without collision in complex and dynamic environment in real time.Method Using online uncertainty reasoning based on a cloud model, a dynamic guidance A^* algorithm based on the cloud model (CDGA^*) was proposed to realize human-machine cooperative path planning. Human''s expertise and preferences were incorporated into the DGA^* optimization process to implement a faster path planning. Matlab software was used to simulate and analyze the CDGA^* and DGA^* algorithms.Result In static path planning, the numbers of close points of the DGA^* and CDGA^* algorithms were 158 and 96, human planning time was 8.8 and 4.0 s, the total planning time was 15.6 and 8.9 s, respectively. In dynamic path planning, human planning time of the DGA^* and CDGA^* algorithms was 12.5 and 5.8 s, the total planning time was 23.3 and 14.6 s, respectively.Conclusion The proposed CDGA^* algorithm can largely decrease the number of nodes, reduce computation time and improve planning efficiency.