A swarm intelligence-based robotic search algorithm integrated with game theory

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. •Game theory has been used to distribute robots in the search environment efficiently.•With the help of the proposed algorithm, robots crowd less in local optimal areas.•As a result of the suitable distribution of the robots, the search speed boosts.•The proposed algorithm is not sensitive to hyperparameters and is stable.•The proposed algorithm does not require a communication center nor synchronization.