Subjects: Other Disciplines >> Synthetic discipline submitted time 2023-03-28 Cooperative journals: 《中国科学院院刊》
Abstract： Unmanned underwater vehicle (UUV) is one of the advanced technologies for human to explore and understand the ocean. It is an important equipment for China’s construction of a marine power. This study introduces the current development status of UUV, with special focus on the latest application achievements of China’s pedigree UUV developed by Shenyang Institute of Automation, Chinese Academy of Sciences, in deep-sea mineral resource exploration, deep-sea scientific research, abyss research and North-South polar expedition. Combined with the current research basis of UUV, the development and application of underwater vehicle in the future are prospected. Through the research and development of far-reaching marine scientific research equipment adapted to extreme marine environment, the technological leap from voyage type scientific research mode to deep-sea long-term resident scientific research mode is realized, and the revolutionary transformation from manned scientific research mode to unmanned scientific research mode is also realized.
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-06-06 Cooperative journals: 《计算机应用研究》
Abstract： Aiming at the problem that it is difficult to take into account both obstacle avoidance and formation when multi-AUV system plans path in unknown environment, this paper proposed a multi-AUV cooperative obstacle avoidance method based on leader-follower and behavior. First, this paper designed a local path planning method for AUV by constructing the evaluation function of collision risk and deviation from the target; on this basis, combined with formation control method, this paper designed different behaviors and behavior selection patterns for leader and followers respectively. The semi-physical simulation experiment results show that the algorithm can realize the cooperative obstacle avoidance of multi-AUV system in unknown environment, and the formation deviation and recovery time are better than traditional multi-robot obstacle avoidance algorithms. Experimental results verify the feasibility and effectiveness of the algorithm.
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