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.
Highlights
Nowadays, water conveyance tunnels have been an important transportation route in hydraulic engineering.[1]
Ranging sonar is widely used in obtaining the distance of obstacles, its principle is the transducer actively emits acoustic waves and obtains the distance information of obstacle by receiving the echo reflected by the obstacle.[3]
From the engineering point of view, the existing time registration methods, such as least squares, extrapolation, maximum entropy, and so on, have some limitations and one-sidedness. These methods use the sampling frequency of the low-frequency sensor as the standard, reduce the utilization rate of the measured data, and reduce the accuracy of the system, so that the weighted polling mode proposed in this article has lost its meaning
Summary
Water conveyance tunnels have been an important transportation route in hydraulic engineering.[1]. The distance between AUV and wall obtained according to polling mode is taken as the input of reinforcement learning algorithm. When the safety alert distance threshold is not reached, the AUV will poll at the basic sampling frequency At this time, the obstacle information will not trigger the local obstacle avoidance plan. When the distance in all directions is greater than the safety alert distance, the sonar will be polled according to the basic sampling frequency fa At this time the poll mode taken is the bow and the port sonars launch sound waves firstly, after the interval time ta, the stern and the starboard sonars launch sound waves. In the process of wall following, AUV obtains the accurate distance from the wall according to the DWPM and selects the appropriate actions using reinforcement learning algorithm. The speed of the vehicle in the upper left, upper right, lower left, and
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