Ship path planning based on multi-factor and multi-scale A* algorithm

Abstract Ship path planning represents a fundamental challenge in intelligent navigation, requiring careful balance between route optimality, safety in complex marine environments. To address the limitations of conventional A* algorithms, this paper proposes an improved multi-factor and multi-scale A* algorithm. The methodology begins with processing ENC data, where canny edge detection combined with adaptive thresholding constructs obstacle maps. A novel dual-layer multi-scale grid framework is established: They are used to rapid global path searching, and precise collision avoidance. The algorithm innovatively integrates a multi-factor function that simultaneously considers obstacle distribution, environment effects, navigation rules, and ship dynamic constraints, with adaptive weight adjustment optimizing the search process. Path refinement employs smoothing algorithms to significantly reduce waypoint numbers. Simulation experiments conducted in Dalian port demonstrate the algorithm’s superior performance: maintaining safe clearance even in obstacle-dense areas and using the shorter length. Experimental results confirm that generated paths better satisfy practical navigation requirements.