Efficient path planning for AUVs in unmapped marine environments using a hybrid local–global strategy

Abstract

The ability of autonomous undersea vehicles (AUVs) to plan paths in unknown marine environments is the precondition for executing complicated missions. However, existing path planning algorithms based on underwater sensing equipment often struggle to achieve efficient exploration and generate high-quality trajectories. In this paper, we introduce a novel approach to efficiently handle the challenge of AUV navigation under limited information. Our solution combines global and local planning techniques to generate optimized paths that guarantee collision-free and efficient operations. In global path planning, we incrementally use the rolling windows to make decisions on high-level path branching while utilizing waypoints from selected branches to refine the calculation of local paths for enhanced accuracy. We employ an efficient small-scale path search strategy at the local path computation level by leveraging sensor-detected environments. In this stage, we propose an advanced rapidly exploring random tree (RRT) algorithm called circle-RRT. By combining adaptive circle sampling with dynamic step sizes, this algorithm can significantly reduce the generation of redundant sampling points and improve the efficiency of local path planning. We evaluated the efficiency of our algorithm in unknown environments through simulations and compared it with previous leading methods.