A Novel Acoustic Diffraction Search Algorithm for Global Path Planning of Orchard Transport Robots

Abstract

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.