A RRT based path planning scheme for multi-DOF robots in unstructured environments

Path planning for robots in complex orchard environments is crucial to ensure efficient and safe completion of harvesting tasks. The use of flexible multi-degree-of-freedom (multi-DOF) robots further adds to the complexity of the task. Therefore, based on a rapidly exploring random tree (RRT) algorithm, a bidirectional path planning method adjusting the expansion direction and dynamic step (EDDS-bi-RRT) according to the scene information is proposed to improve the planning efficiency of multi-DOF robots in the configuration space (C-space). Two improvements are introduced to the algorithm: (1) The algorithm begins by designating the search's starting point as the tree's root node. Subsequently, it generates an extended tree by continuously adding leaf nodes through sampling. The expansion direction is set from the nearest node to the target point, and the cosine and sine factors are introduced to adjust the direction when the sampling point collides with obstacles, and the extended trees will be exchanged for growth from the target point to the nearest node accordingly. (2) The minimum distance between the robot links and obstacles is the maximum safe distance d with which the robot links can move in the workspace. Then, d is converted to the expansion step in the high-dimensional C-space by introducing the matrix operator norm. This way, the explore efficiency is improved significantly in complex environments while the computational time and the path length decrease. Simulations in different scenarios are performed based on a 17-DOF humanoid dual-arm apple harvesting robot developed in our lab, and the results show that the proposed algorithm presents a higher efficiency and shorter planning time and path length compared with previous similar algorithms. In addition, experiments in the lab orchard environments are also performed to indicate that the EDDS-bi-RRT algorithm can be applied to generate collision-free path points for multi-DOF robots in unstructured environments safely and effectively.