关键词: 水下自主航行器, 路径规划, 偏向扩展, 启发式RRT, 速度矢量合成

Abstract:

In response to the autonomous underwater vehicle (AUV) path planning problem in complex underwater environments, an improved path planning algorithm based on Informed rapidly-exploring random trees (RRT) is proposed in this study. A target-biased sampling strategy and a target-biased extension strategy are employed to address the issue of lack of goal orientation in the sampling process, ensuring that target nodes become sampling points during random sampling. During path points extension, non-target sampling points are guided in the direction of the target point, thereby enhancing the algorithm's ability to search for the target during random sampling and extension processes. A heuristic sampling strategy is introduced during random sampling to tackle the problem of an abundance of invalid search spaces in underwater path planning. This strategy constructs a subset of the sampling space that includes all initial paths, thereby reducing the range of the sampling space and improving the spatial search efficiency of the algorithm. Finally, a velocity vector synthesis method is applied to address the issue of insufficient resistance to underwater flow disturbances. This method aligns the AUV's velocity vector with the desired path by synthesizing the AUV's velocity vector with the flow velocity vector. This effectively counteracts the impact of water currents. Multiple simulation experiments are conducted by superimposing Lamb vortex flow simulations on underwater terrain, and the results demonstrate that the enhanced informed RRT algorithm successfully mitigates randomness in the sampling process, significantly reduces the search space, balances path safety and smoothness, and equips the AUV with robust resistance to underwater flow disturbances.

Key words: autonomous underwater vehicle(AUV), path planning, expansion bias, informed RRT, velocity vector synthesis