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.

ROV-Based Autonomous Maneuvering for Ship Hull Inspection with Coverage Monitoring

“…The application of machine leaning for autonomous control will also be explored. Previous works [51,52] showed great potential, including in underwater inspection scenarios [12]. Finally, since most inspections occur in harbors, the use of external sensors installed within the inspection area to extend the inspection efficiency and capabilities will be explored.…”

“…A method to detect the wall and estimate the pose of the vehicle is proposed using the Random Sample Consensus (RANSAC) [10] using measurements received from a multibeam imaging sonar [11]. Recently, an approach using reinforcement learning was studied in [12], where a set of ranging sensor is used and efficiently manipulated to allow an underwater vehicle to navigate along the wall. However, the proposed methods often lack robustness and cannot adapt well to shape changes, especially in the presence of more objects than the wall or acoustic noise.…”

Design and Reliability Analysis of a Tunnel-Detection AUV Based on a Heterogeneous Dual CPU Hot Redundancy System

“…When the module fails, it cannot recover by itself. (2) The working status of the system is divided into three types: 1 Normal working state (R 1 ): All modules are working normally, or the network line or serial line in one or more dual-communication fails (that is, there is no failure of both the network line and the serial line in a dual-communication line). At this time, normal communication can still be guaranteed between all CPUs, and the AUV performs tasks normally.…”

“…However, there are many uncertain factors in the construction process of water conveyance tunnels, especially some longdistance and deeply buried water conveyance tunnels. After running for a certain time, tunnels are likely to suffer from diseases of different degrees [1]. Therefore, it is necessary to conduct regular inspections of water conveyance tunnels.…”