An environment information-driven online Bi-level path planning algorithm for underwater search and rescue AUV

Autonomous underwater vehicles (AUVs) have significant potential in the underwater emergency rescue, where efficient path planning is crucial for search and rescue (SAR). However, implementing short, low-energy, and reachable paths in dynamic and unknown marine environments remains a challenge. This paper proposes an environment information-driven online bi-level path planning (EDOBP)algorithm for SAR to address this problem. Firstly, an improved global path planning algorithm is presented, which proposes a new way of path point selection in the EDOBP algorithm for unknown search areas to plan a global path to achieve full coverage search, and obtain the position of the target object. Secondly, real-time sensor data such as side-scan sonar (SSS) and acoustic Doppler current profiler (ADCP) are used to update the environmental state information, thereby triggering replanning in regions where target objects are detected through an automatic triggering mechanism. Lastly, an improved ant colony algorithm is designed to replan an energy-optimal path to confirm the target identity. The effectiveness of the proposed algorithm is validated through physical simulation experiments.