An improved recurrent neural network for unmanned underwater vehicle online obstacle avoidance

Abstract

This paper focuses on online obstacle avoidance planning for unmanned underwater vehicles. To improve the autonomous ability and intelligence of obstacle avoidance planning, a recurrent neural network with convolution is proposed. In the proposed method, convolution replaces full connection in the standard recurrent neural network, thus reducing the number of parameters and improving the feature extraction capability. Training and test datasets are generated for the deep learning process and, combined with multibeam forward-looking sonar, this learning system can automatically realize online obstacle avoidance for unmanned underwater vehicles. Experiments are designed to compare the performance of the proposed structure with that of a recurrent neural network, gated recurrent units, and conventional ant colony optimization. The results fully verify the effectiveness and feasibility of the proposed method, and show that the powerful learning and memory capabilities of the proposed structure can be used in the unmanned underwater vehicle autonomous learning environment. This study demonstrates that recurrent neural networks with convolution greatly enhance the ability of unmanned systems to sense and adapt to unknown environments.