Abstract
As a new type of marine unmanned intelligent equipment, autonomous underwater vehicle (AUV) has been widely used in the field of ocean observation, maritime rescue, mine countermeasures, intelligence reconnaissance, etc. Especially in the underwater search mission, the technical advantages of AUV are particularly obvious. However, limited operational capability and sophisticated mission environments are also difficulties faced by AUV. To make better use of AUV in the search mission, we establish the DMACSS (distributed multi-AUVs collaborative search system) and propose the ACSLA (autonomous collaborative search learning algorithm) integrated into the DMACSS. Compared with the previous system, DMACSS adopts a distributed control structure to improve the system robustness and combines an information fusion mechanism and a time stamp mechanism, making each AUV in the system able to exchange and fuse information during the mission. ACSLA is an adaptive learning algorithm trained by the RL (Reinforcement learning) method with a tailored design of state information, reward function, and training framework, which can give the system optimal search path in real-time according to the environment. We test DMACSS and ACSLA in the simulation test. The test results demonstrate that the DMACSS runs stably, the search accuracy and efficiency of ACSLA outperform other search methods, thus better realizing the cooperation between AUVs, making the DMACSS find the target more accurately and faster.
Highlights
With the development of technology and oceanic applications, autonomous underwater vehicle (AUV) has played an important role in marine applications
In order to compare the effects of different RL algorithms when training ACSLA, we used the deep Q-network (DQN) algorithm based on value iteration and the deep deterministic policy gradient algorithm (DDPG) algorithm based on policy gradient to train ACSLA, respectively
We establish the DMACSS and propose the ACSLA that is integrated into the DMACSS
Summary
With the development of technology and oceanic applications, AUV (autonomous underwater vehicle) has played an important role in marine applications. Compared with HOV (human occupied vehicle) and ROV (remotely operated vehicle), AUV is an unmanned agent without cables and can accomplish the work independently, safely and efficiently [1]. Relying on these advantages, AUV has been widely used in minefield search, reconnaissance, and anti-submarine, marine exploration, marine rescue, marine observation [2,3,4,5], etc. MAS (multi-AUVs system) provides a new method to overcome these difficulties due to the great efficiency and high reliability brought by the space–time distribution and redundant configuration [6].
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