Abstract
The autonomous underwater vehicle (AUV) is widely used to search for unknown targets in the complex underwater environment. Due to the unpredictability of the underwater environment, this paper combines the traditional frontier exploration method with deep reinforcement learning (DRL) to enable the AUV to explore the unknown underwater environment autonomously. In this paper, a grid map of the search environment is built by the grid method. The designed asynchronous advantage actor-critic (A3C) network structure is used in the traditional frontier exploration method for target search tasks. This network structure enables the AUV to learn from its own experience and generate search strategies for the various unknown environment. At the same time, DRL and dual-stream Q-learning algorithms are used for AUV navigation to further optimize the search path. The simulations and experiments in an unknown underwater environment with different layouts show that the proposed algorithm can accomplish target search tasks with a high success rate, and it can adapt to different environments. In addition, compared to other search methods, the frontier exploration algorithm based on DRL can search a wider environment faster, which results in a higher search efficiency and reduced search time.
Highlights
In recent years, the target search task of the autonomous underwater vehicle (AUV) has been deeply studied with the deep development of oceans by all nations
Experiments with AUV in unknown underwater environment with different layouts show that the proposed method can accomplish the target search task with a high success rate
EXPERIMENTS AND RESULTS In order to study the performance of the proposed algorithm in the unknown environment, we conducted three sets of experiments: (1) performance testing of the front-end search method based on deep reinforcement learning (DRL) in different environments; (2) the frontier search method of DRL is compared with three other frontier search technologies; and (3) we apply the proposed algorithm to an AUV experiment in a real pool environment
Summary
The target search task of the autonomous underwater vehicle (AUV) has been deeply studied with the deep development of oceans by all nations. Experiments with AUV in unknown underwater environment with different layouts show that the proposed method can accomplish the target search task with a high success rate. Compared with other methods, the proposed algorithm can search the underwater environment with different obstacle distribution more quickly.
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