Optimal collaborative path planning for four-wheel steering picking robots focusing on improving efficiency at bends

Abstract

In the agricultural field, intelligent picking has great application potential, among which efficient path planning is the key to improving picking efficiency. Given the expansive area and intricate terrain commonly encountered in orchards, challenges such as the tipping over of picking robots at bends and road encroachment, which can damage orchard plants, are prevalent. This study proposes an improved Hybrid A* algorithm based on collaborative path planning for multiple-picking robots. This study focuses on four-wheel steering picking robots and introduces a three-stage path-planning approach based on numerical optimization to address the issue of road encroachment at bends. Furthermore, the efficiency of the algorithm is enhanced by the introduction of guidelines. The improved Hybrid A* algorithm is combined with a time window model to dynamically adjust the priority of picking robots based on the requirements of the picking task and the distance between the robot and the endpoint, thus realizing the collaborative planning of multiple robots and effectively improving the picking efficiency. Finally, the simulation experiment based on MATLAB shows that the algorithm can generate optimal paths for multiple picking robots in orchard environments, adapt swiftly to environmental changes, and exhibit minimal collisions with orchard plants (13.4%). This research not only optimizes the path planning of intelligent picking but also provides strong technical support for the efficient and safe operation of agricultural robots in complex environments.