To improve the planning efficiency of the Hybrid A-Star algorithm in facility-based agriculture environments, we propose a novel method that integrates directional guidance and segment-based heuristic weighting. Jump point search (JPS) is first employed to generate a reference path, from which key jump points and their heading angles are extracted to guide node expansion. The path is then segmented into straight and turning segments, each assigned dynamic heuristic weights to form a segment-wise heuristic sequence. Simulation results show that, while maintaining comparable path lengths, the proposed method significantly improves computational efficiency, reduces node expansion, and enhances overall path smoothness. These results highlight the proposed method’s effectiveness for path planning in autonomous mobile platforms operating within agricultural facilities.

A novel heuristic search algorithm, the Acoustic Diffraction Search Algorithm (ADSA), is proposed to address the global path planning problem for orchard transport robots. By simulating the diffraction characteristics of acoustic waves around obstacles, ADSA constructs a search method centered on an acoustic-wave-based search strategy, a diffraction node identification rule, and sound pressure attenuation estimation at diffraction nodes. Path-planning simulations were conducted on random grid maps of different scales and obstacle densities, and ADSA was compared with the A* algorithm, the Dijkstra algorithm, and the Jump Point Search (JPS) algorithm. The simulation results indicate that ADSA generated relatively short planned paths while maintaining relatively high search efficiency in the test scenarios. Only on the large-scale map with an obstacle density of 30% was the path generated by ADSA 0.24% longer than that generated by the Dijkstra algorithm, while its pathfinding time was only 0.18% of that of the Dijkstra algorithm. Path-planning experiments conducted on maps of the two test field sites showed that ADSA generally generated paths with shorter path lengths and smaller cumulative turning angles. Specifically, ADSA reduced the average path length by 3.95% compared with the Dijkstra algorithm and by 6.30% compared with the A* and JPS algorithms. The results show that ADSA achieved favorable overall performance in terms of path length, cumulative turning angle, and search efficiency, providing a new search approach for the global path planning of orchard transport robots in known environments.