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.

Regarding the path planning problem for the local leveling operation of the land leveler, this paper proposes a path planning method based on the improved dung beetle optimizer (IDBO) algorithm. Firstly, a comprehensive evaluation objective function was established for the local operation path planning of the land leveler, which included the path length, under-excavation amount, under-filling amount, as well as the total amount of excavated and filled soil. Then, IDBO algorithm was constructed, an initialization population strategy based on Fuch chaotic mapping and reverse learning strategy was designed, as well as an improved ball-rolling behavior that integrates the search strategy of the Aquila high soar with the vertical stoop from the Aquila optimizer algorithm. Test functions were used to verify the superiority of the IDBO algorithm compared to the dung beetle optimizer (DBO) algorithm, the particle swarm optimization (PSO) algorithm and the gray wolf optimizer (GWO) algorithm. Finally, taking the paddy fields in a real environment as the object, a hardware platform for data acquisition was constructed, and data collection, analysis, terrain modeling, and path planning experiments were carried out with paddy fields in the natural environment as the measured objects. The experimental results show that, for the primary optimization objective of load variation cost, as well as path length cost, compared with the other three algorithms (PSO, GWO, DBO), the IDBO algorithm achieved improvements of 7.0%, 12.3%, and 6.6% on Plot 1, 1.6%, 9.2%, and 1.6% on Plot 2, 4.0%, 6.1%, and 1.5% on Plot 3, and 3.3%, 24.1%, and 3.4% on Plot 4.