3D Dynamic Trajectory Planning for Autonomous Underwater Robots under the PPO-IIFDS Framework

Three-dimensional (3D) dynamic trajectory planning for Autonomous Underwater Vehicles (AUV) poses significant challenges in balancing trajectory quality, computational efficiency, and environmental adaptability within complex dynamic environments. To tackle these challenges, this paper proposes a novel trajectory planning framework by integrating Proximal Policy Optimization (PPO) and an Improved Interfered Fluid Dynamic System (IIFDS). The IIFDS serves as the planning layer, generating obstacle-adaptive trajectories for AUVs through dynamic adjustment of flow field parameters. Meanwhile, PPO functions as the learning and decision-making layer, optimizing critical parameters in IIFDS, including repulsion response coefficients, tangential response coefficients, and directional coefficients, to enhance adaptability and real-time decision-making. To meet specific mission requirements, the IIFDS incorporates dynamics and kinematics constraints, while the PPO reward function is improved with a multi-objective dynamic structure. This reward design integrates objectives such as obstacle avoidance, target distance minimization, trajectory smoothness, dynamics constraints, and energy efficiency. These enhancements address sparse reward issues effectively and significantly improve the convergence and practical applicability of trajectory planning. Additionally, a diverse and dynamically complex obstacle environment is constructed for model training and performance evaluation. Experimental results demonstrate that the proposed framework efficiently generates smooth, energy-efficient, and collision-free trajectories in high-density dynamic obstacle scenarios. The framework exhibits strong robustness, excellent generalization capabilities, and offers a reliable solution for 3D dynamic trajectory planning for AUV.