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.