Abstract
In recent years, with the rapid development of military technology and the evolution of battlefield mines, intelligent mines are the important embodiment of active attack mines. In the future, unmanned vehicles need to chase and capture intelligent mines, improve the efficiency of mine clearance, and reduce the casualties of soldiers. Therefore, it is necessary to study how to improve the efficiency of unmanned ground vehicle pursuit. Among them, the game method of pursuit and evasion between intelligent mines and unmanned ground vehicles based on reinforcement learning in the 2D simulation environment can effectively achieve this goal. The trained intelligent mines have active attack ability, unmanned ground vehicles have basic mine clearance ability, and the success rate of intelligent mine blasting is as high as 90%. In addition, unmanned ground vehicles can also effectively defend against the active attack of intelligent mines, and the defense success rate is also as high as 90%.
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This work was supported by the National Natural Science Foundation of China (no. U1936218).
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Tong, X., Ma, Y., Xue, Y., Zhang, Q., Li, Y., Tan, Ya. (2021). Anti Intelligent Mine Unmanned Ground Vehicle Based on Reinforcement Learning. In: Tan, Y., Shi, Y., Zomaya, A., Yan, H., Cai, J. (eds) Data Mining and Big Data. DMBD 2021. Communications in Computer and Information Science, vol 1454. Springer, Singapore. https://doi.org/10.1007/978-981-16-7502-7_7
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DOI: https://doi.org/10.1007/978-981-16-7502-7_7
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