Full-Coverage Motion Planning Method for Outdoor Mobile Spraying Robots Based on Hierarchical Optimization

Outdoor mobile spraying robots are increasingly being used for precision plant protection and vector-borne disease control. Coverage planning in unstructured environments remains difficult because spray models are computationally costly, chassis motion is subject to multiple constraints, and pan-tilt spraying must be coordinated with vehicle motion. This paper proposes a hierarchical planning method to address these challenges. The spray model uses a 2D fan-shaped representation for chassis planning and a 3D conical representation for pan-tilt control. The chassis planner then generates local paths, optimizes the operation sequence with a genetic algorithm and a Dijkstra distance matrix, and adjusts the resulting trajectory with Hybrid A*. An N-shaped scanning strategy is coupled with chassis pan-tilt coordination for target-surface coverage. In simulation, the simplified coverage models achieve Jaccard agreement above 90% with the corresponding Monte Carlo reference regions. Across three chassis scenarios, the complete framework attains 98.1% average binary effective-area coverage, an 18.9 percentage-point difference relative to conventional Hybrid A* in this benchmark. The three modeled pan-tilt surfaces reach 100% binary threshold coverage under the stated geometric model. These results indicate feasibility at the simulation level under the stated geometric and windless assumptions. They do not measure physical tracking accuracy, deposition uniformity, or outdoor field performance.