Year: 2025
Type: article
Abstract: To address the challenges of inefficient path planning, discontinuous trajectories, and inadequate safety margins in autonomous spraying vehicles for greenhouse environments, this paper proposes a hierarchical motion control architecture. At the global path planning level, the heuristic function of the A* algorithm was redesigned to integrate channel width constraints, thereby optimizing node expansion efficiency. A continuous reference path is subsequently generated using a third-order Bézier curve. For local path planning, a state-space sampling method was adopted, incorporating a multi-objective cost function that accounts for collision distance, curvature change rate, and path deviation, enabling the real-time computation of optimal obstacle-avoidance trajectories. At the control level, an adaptive look-ahead distance pure pursuit algorithm was designed for trajectory tracking. The proposed framework was validated through a Simulink-ROS co-simulation environment and deployed on a Huawei MDC300F computing platform for real-world vehicle tests under various operating conditions. Experimental results demonstrated that compared with the baseline methods, the proposed approach improved the planning efficiency by 38.7%, reduced node expansion by 16.93%, shortened the average path length by 6.3%, and decreased the path curvature variation by 65.3%. The algorithm effectively supports dynamic obstacle avoidance, multi-vehicle coordination, and following behaviors in diverse scenarios, offering a robust solution for automation in facility agriculture.
Source: Vehicles
Authors: Junchong Zhou, Yi Zheng, Xianghua Zheng, Kuan Peng
Institution: Chengdu Technological University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4415620357