Research on autonomous underwater vehicle wall following based on reinforcement learning and multi-sonar weighted round robin mode

Abstract

When autonomous underwater vehicle following the wall, a common problem is interference between sonars equipped in the autonomous underwater vehicle. A novel work mode with weighted polling (which can be also called “weighted round robin mode”) which can independently identify the environment, dynamically establish the environmental model, and switch the operating frequency of the sonar is proposed in this article. The dynamic weighted polling mode solves the problem of sonar interference. By dynamically switching the operating frequency of the sonar, the efficiency of following the wall is improved. Through the interpolation algorithm based on velocity interpolation, the data of different frequency ranging sonar are time registered to solve the asynchronous problem of multi-sonar and the system outputs according to the frequency of high-frequency sonar. With the reinforcement learning algorithm, autonomous underwater vehicle can follow the wall at a certain distance according to the distance obtained from the polling mode. At last, the tank test verified the effectiveness of the algorithm.