Workspace decomposition based path planning for fruit-picking robot in complex greenhouse environment

Abstract

The working environment of fruit-picking robots is very complicated. Generally, there are a large number of obstacles such as branches, immature crops, etc. Besides, the agricultural scene is not as static. For sampling-based methods such as Probabilistic RoadMap (PRM) and Rapidly-exploring Random Trees (RRT), they suffer from inferior path quality in obstacle condensed environments. This paper proposes a local search path planning method in the feasible region of fruit-picking robots based on discrete workspace guidance and configuration space exploration correction. It is used to plan the motion of fruit-picking robots for avoiding obstacles and grasping in dynamic and complex agricultural environments. First, the workspace is discretized, which makes the location information of obstacles computable. Then, a connected region path is found to guide the robot to search in the local configuration space. This greatly improves the continuity of the generated paths in the configuration space. The local search is further used to correct the weights of discrete regions of the workspace to find better connected paths with higher planning speed. The experimental results show that the quality of paths planned by proposed method is generally better than that of RRT and RRT-CONNECT, and the planning speed is faster than that of Task-Space RRT (TSRRT). Proposed algorithm enables real-world picking at 10.5 piece/s and a recovery rate of 80.0 %. This method significantly improves the path quality in dynamic and complex agricultural scenarios.