Path planning method for motor grader based on a variable-scale distribution strategy and improved ant colony optimization algorithms

To address the limitations of traditional traversal-based land leveling operations—such as a lack of overall coordination and planning, resulting in low operational efficiency, excessive path planning data, and frequent full or empty loads of the leveling blade—this study proposed a farmland leveling path planning method based on the improved ant colony optimization algorithms incorporating a variable-scale stepwise planning strategy. Based on an elevation difference model of the farmland terrain, the improved ant colony optimization algorithms were used for path planning. At the large-scale regional level, the method involved partitioning the farmland area according to the cut/fill soil volume derived from grid cell data, with each region serving as a node. Key components included: parameter setting for pheromone evaporation, construction of heuristic functions and pheromone structures, and an adaptive adjustment mechanizm for corresponding weight factors. A path evaluation standard was also designed, using total path length and the overall load rate of the leveling blade as the optimality criteria, to achieve intelligent planning of soil redistribution paths across regions. At the small-scale grid cell level, each grid cell acted as a node. A method was proposed for selecting the start and end points of the ant colony optimization algorithm based on the soil cut/fill demand. The initial pheromone concentration was constructed by referencing sub-optimal paths planned by the A* algorithm. A heuristic function was formulated by considering the soil cut/fill demand and blade load characteristics. A path quality evaluation coefficient was introduced to improve the pheromone update mechanism. This enabled detailed leveling path planning for the entire field after preliminary levelling. Simulation results demonstrated that, compared with the original ant colony algorithm, the IACO-based inter-regional path planning enhanced the maximum absolute elevation difference reduction by 73.97% and shortened the operation path length by 5.33%. Furthermore, compared with the original ant colony algorithm, the FIA*ACO-based intra-raster path planning reduced the operation path length by 2.34% and decreased the traversal frequency by 60.87%. The two-stage planning approach—starting with IACO-based inter-regional planning followed by FIA*ACO-based intra-raster optimization—significantly increased the area of farmland meeting leveling standards. It achieved better leveling effects in both significantly uneven and mildly uneven terrain zones. Field trial results indicated that the inter-region earthwork balancing operation path planning based on IACO reduced the maximum elevation difference by 0.058 m, with the 5 cm elevation difference distribution increasing by 4.18 percentage points. The grid-based earthwork balancing operation path planning based on FIA*ACO reduced the maximum elevation difference by 0.016 m, with the 5 cm elevation difference distribution increasing by 2.36 percentage points. Results indicated that planning inter-region operation paths based on IACO followed by inter-grid operation paths based on FIA*ACO further enhanced the levelling quality of the test field and yielded superior levelling effects for small areas with elevated or depressed terrain within plots. This resulted in a substantial improvement in operational efficiency and fuel consumption, offering practical value for advancing high-standard farmland construction, reducing labor requirements, and lowering carbon emissions.