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.

中文翻译：

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基于强化学习和多声纳加权循环模式的自主水下航行器跟墙研究

自主水下航行器在跟随墙壁时，一个常见的问题是自主水下航行器配备的声纳之间的干扰。本文提出了一种可以独立识别环境、动态建立环境模型、切换声纳工作频率的加权轮询工作模式（也可称为“加权轮询模式”）。动态加权轮询模式解决了声纳干扰问题。通过动态切换声纳的工作频率，提高跟墙效率。通过基于速度插值的插值算法，对不同频率测距声纳的数据进行时间登记，解决多声纳的异步问题，系统根据高频声纳的频率输出。通过强化学习算法，自主水下航行器可以根据轮询模式得到的距离，跟随墙体进行一定距离的跟随。最后通过水槽试验验证了算法的有效性。