Risk-Sensitive Online Path Planning for AUVs Under Localization and Sensing Uncertainty

Online path planning is a core capability of autonomous underwater vehicles (AUVs). Its effectiveness in underwater environments is affected by uncertainty in localization and obstacle sensing. Insufficient consideration of these uncertainties can lead to risky paths or unnecessary detours. To address this challenge, we proposed a risk-sensitive online path planning (RSO-RRT). A Gaussian process occupancy map is constructed to incorporate localization and sensing uncertainty into collision risk assessment. It generates candidate paths by combining a goal-rooted reverse RRT* with local receding-horizon planning. To reduce computational cost, we reuse the existing search tree during the local planning. Candidate paths are evaluated using path length, mean collision risk, and conditional value-at-risk (CVaR) of collision loss. We conduct separate simulations and experiments to evaluate the contributions of individual terms in the weighted objective. Comparisons are also conducted with RRTX, DRRT, RT-RRT, and CC-RRT show that RSO-RRT achieves higher planning success rates, lower maximum collision risk, and greater minimum obstacle clearance under localization and sensing uncertainty. HoloOcean simulations demonstrate online risk-sensitive planning in underwater environments. Indoor experiments further indicate the algorithm’s applicability to other GPS-denied environments.