AUV Path Planning Method Based on RRT* and Improved Artificial Potential Field

In response to the complex ocean current environment significantly affecting the navigation efficiency and energy use of autonomous undersea vehicle(AUV), an energy consumption-optimized path planning algorithm(FAPF-Bi-RRT*) was proposed by integrating bidirectional rapidly-exploring random tree with flow field-guided sampling and improved artificial potential field method. Firstly, a multi-strategy sampling probability correction method was designed based on flow field information. By calculating the consistency measure between the flow field and the target direction, the sampling weight was dynamically adjusted to guide the random tree to expand toward downstream and low-energy-consumption areas, thus solving the blind search problem of the traditional RRT* algorithm. Secondly, an adaptive flow field potential field model was established, converting the pushing and resistance effects of ocean currents into potential field gradients. An adaptive adjustment mechanism of flow field gain was introduced into the artificial potential field to assist node expansion and reconnection in real time, thereby avoiding countercurrent traps. Finally, a composite cost function including path length and energy consumption was constructed for progressive optimization. Simulation results under real terrain and flow field data show that while ensuring probabilistic completeness and obstacle avoidance capability, the proposed algorithm reduces the path length by approximately 14% and the path energy consumption by approximately 11% compared with the traditional Bi-RRT* algorithm in strong flow field environments. Efficient and energy-saving path planning in complex marine environments is thus achieved.