AUV Dynamic Obstacle Avoidance Method Based on Improved PPO Algorithm

Abstract

Designing a reasonable obstacle avoidance method for AUV 3D path planning is difficult, and existing obstacle avoidance methods have certain drawbacks. For example, they are only applicable to 2D planar applications and cannot effectively handle dynamic obstacles. To address these problems, we design an obstacle collision prediction model (CPM). Based on the results of the simulation of obstacles’ inertial motion, the safety of the AUV navigation is evaluated to improve the model’s sensitivity to dynamic obstacles. Then, we enhance the learning ability of the sequence sample data by combining it with a long short-term memory (LSTM) network, thus improving the training efficiency and effect of the algorithm. The trained proximal policy optimization (PPO) network can output reasonable actions in order to control the AUV to avoid obstacles, forming an AUV 3D dynamic obstacle avoidance strategy based on the CPM-LSTM-PPO algorithm. The simulation results show that the proposed algorithm has good generalization in uncertain environments. Moreover, it achieves dynamic AUV obstacle avoidance in different three-dimensional unknown environments, providing theoretical and technical support for real path planning.

Keywords

• AUV
• dynamic obstacle avoidance
• deep reinforcement learning
• proximal policy optimization algorithm
• collision prediction model