Multi‐Objective Off‐Road Path Planning on the Basis of the Improved Dominance Check Strategy

ABSTRACT Multi‐objective off‐road path planning on the basis of Pareto optimality is a popular research topic. Nevertheless, computing Pareto‐optimal path solutions requires several repetitive dominance checks, which significantly affect path‐finding efficiency. This study quantifies grids around slope and land‐cover targets. On the basis of this grid, a multi‐objective off‐road traversability model was constructed to simulate the natural environment. Furthermore, a multi‐objective off‐road path planning algorithm on the basis of the dominance check factor (MOORPPA‐DCF) was proposed. Specifically, a heuristic function adapted to the multi‐objective off‐road traversability model was designed to guide non‐dominated path expansion. Additionally, an improved dominance check strategy was introduced, and a DCF was added to assist with path expansion for efficient planning. To assess the algorithm's feasibility and efficiency, it was compared with a single‐objective optimal algorithm and MOORPPA. The experiments demonstrated that the algorithm ensures single‐objective optimality while generating a broader range of alternative path solutions, enhancing route diversity for selection. Regarding efficiency, the DCF significantly reduces the number of dominance checks during path expansion. Time efficiency improved by an average of 93.79% compared to the conventional method.