Time-optimal and path-optimal motion planning for selective broccoli harvesting manipulator

Abstract

Due to the asynchronous maturation characteristics of broccoli, manual harvesting in multiple batches encounters labor shortages. Consequently, there is an urgent requirement for the development of a selective broccoli harvesting robot. One of the crucial technologies in the broccoli selective harvesting robot is the motion planning algorithm, which quickly generates safe and feasible paths to avoid collisions, ensuring efficient completion of the harvesting task. However, existing motion planning algorithms struggle to balance planning speed, path quality, and success rate simultaneously. The desired motion planning algorithm for the robotic arm should enable continuous, smooth, safe, and fast movements, resembling the motion of a human arm, as it travels between the cutting and collection points, thereby enhancing the efficiency of broccoli harvesting. To address this issue, we proposed flexible edge checking and uniform sampling strategies to enhance the sampling and search processes, thereby expediting planning speed. Additionally, a Markov optimiser was incorporated to optimise path length and smoothness. Our proposed algorithm is named Time-Optimal and Path-Optimal (TOPO), which maintains and enhances the convergence properties and asymptotic optimality of Batch Informed Trees (BIT*), enabling the manipulator to exhibit smooth, efficient, and adaptive trajectories. To verify the effectiveness of our proposed algorithm, we conducted multi-dimensional collaborative simulation experiments and prototype verification experiments. The results demonstrate that compared with Rapidly-exploring Random Tree Star (RRT*), Rapidly-exploring Random Tree Connect (RRT-Connect), Fast Marching Tree Star (FMT*), Adaptively Informed Trees (AIT*), Advanced BIT*(ABIT*) and BIT* algorithms, the TOPO algorithm can reduce path planning time by 50%-73%, shorten path length by 4%-11%, minimise operating time by 9%–32%, and improve planning success rate to 97%. It can reduce the operating time of harvesting a single broccoli to within 11 s, thus enabling the selective harvesting robot to perform tasks more efficiently.