A heuristic tomato-bunch harvest manipulator path planning method based on a 3D-CNN-based position posture map and rapidly-exploring random tree

Abstract

The environment of tomato bunches is complex, and the fruit volume is relatively large, so manipulator picking-harvest motion planning should consider not only how to pick, but also how to avoid obstacles after picking tomato bunches and harvesting them from the complex environment, which puts forward strict requirements for picking-harvest motion performance. However, existing sampling-based algorithms still have problems of blind expansion and low efficiency. To solve these problems, a heuristic tomato-bunch harvest manipulator path planning method based on a 3D-CNN (convolution neural network)-based position posture map (PPM) and rapidly-exploring random tree (RRT) is proposed in this paper. This method comprehensively considers the sampling effectiveness of the whole process of picking and harvesting, search speed, path cost and manipulability of the manipulator, combining a sampling-based path planning algorithm and deep learning framework. The method utilizes CNN and FCN(Fully Connected Neural Networks) for generating a heuristic cost map where each voxel has a cost-to-go value toward the target and an optimal target posture value to guide the expansion tree. In addition, the sampling space of obstacle avoidance planning is reduced by a spatial partition method, and the difficulty caused by the increase in the volume of the end-effector clamping fruit is solved by prioritizing the planning of the fruit harvest path and then planning the picking path. On the basis of the above research, a large number of harvest experiments have verified the effectiveness of the proposed algorithm. Research and experimental results show that, based on the PPM-RRT algorithm, the picking-harvest time of a single tomato bunch is 13 s, and the success rate is close to 100%. Compared with state-of-the-art sampling algorithms, the planning time is reduced by more than 70%, the path length is reduced by more than 24%, and the manipulability of the manipulator is increased by 38.9%. The proposed path planning method is effective in complex environments.