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A swarm intelligence-based robotic search algorithm integrated with game theory
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AI
DOI：10.1016/j.asoc.2022.108873.png)
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Abstract
En 中文
This paper proposes a novel decentralize and asynchronous swarm robotic search algorithm integrated with game theory to better disperse robots in the environment while crossing obstacles and solving mazes. This prevents early convergence and improves the efficiency of the searches. In the proposed algorithm, individual robots, while searching, play a sequential game at each iteration, and based on that, choose their velocity update rule. The effectiveness of the proposed strategic game is tested in a specially designed framework. As a validation, the introduced algorithm is compared with the state-of-the-art in simple and complex search environments. The results showed that the suggested algorithm outperforms other methods both in search duration and attained path length to the target, and its success rate is equal to the one of state-of-the-art (i.e., 100% in the conducted experiments). Also, it is shown that the proposed strategic game works well in search environments with different levels of complexity and especially improves search efficiency further in complex environments. (C) 2022 Elsevier B.V. All rights reserved.
Keywords:
Swarm robotic search
Game theory
Particle swarm optimization
Complex unknown environments
Autonomous mobile robots
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