Abstract:The realization of autonomous and safe operation for orchard mobile robots relies critically on efficient path planning technology. To address the prevalent challenges of low planning efficiency, excessive path turning points, and poor smoothness in existing path planning methods within complex orchard environments, an autonomous path planning method was proposed, integrating an adaptive A* algorithm with trajectory optimization, improving the autonomous navigation and operation performance of robots. Firstly, an orchard grid map model was constructed as the foundation for global planning. Secondly, a real-time optimization mechanism combining cost-weighted and centerline offset functions enhanced the adaptive A* algorithm, and a dynamic five-neighborhood search strategy was introduced for comprehensive global path searching. Subsequently, third-order Bézier curves were applied for adaptive path smoothing, generating a curvature-continuous navigation trajectory that met the operational requirements of orchard robots. Simulation and field experiments conducted in representative orchard environments demonstrated that compared with an improved A* algorithm, the proposed method significantly reduced the average path planning time by 23.8% (17.15ms) and 23.1% (16.09ms) in obstacle-free and obstacle-present scenarios, respectively, while achieving a reduction in path average curvature by 10.7% (0.011m-1) and 15.8% (0.028m-1). Field tests further validated the reductions in average planning time by 26.4% (19.01ms) and 27.4% (21.28ms), along with decreases in path average curvature by 7.3% (0.009m-1) and 8.7% (0.013m-1) under the respective scenarios. The proposed method significantly enhanced path planning efficiency and smoothness, effectively meeting the practical operational demands of orchard robots and demonstrating strong potential for practical application.
