An evading strategy for hypersonic vehicle against multiple interceptors via reinforcement learning

Xun Li; Hongyu Zhou

doi:10.1117/12.2641322

10 November 2022 An evading strategy for hypersonic vehicle against multiple interceptors via reinforcement learning

Xun Li, Hongyu Zhou

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123482Q (2022) https://doi.org/10.1117/12.2641322
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

This paper proposes an evading strategy based on reinforcement learning for the pursuit-evasion problem considering hypersonic vehicle dynamics (evader) and multiple interceptors (pursuers). Analytical solutions are hard to obtain, so a reinforcement learning method is adopted for the vehicle’s guidance commands in evasion. To cast the problem to a Markov-Decision-Process (MDP), we first establish the motion model involves multiple pursuers and one evader. Moreover, we choose their positions, and velocities change in the three-dimensional space as the state transition, considering a proportional navigation interception guidance. Furthermore, the weighted sum of the zero-effort-miss perpendicular to line-of-sight and the magnitude of maneuver is chosen to be a reward function. After training using a PPO-Clip algorithm, a policy for evasion is obtained. Numerical experiments validate that the method we put forward can be used in the sequential evasion decision against multiple pursuers for a hypersonic vehicle.

Citation Download Citation

Xun Li and Hongyu Zhou "An evading strategy for hypersonic vehicle against multiple interceptors via reinforcement learning", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123482Q (10 November 2022); https://doi.org/10.1117/12.2641322

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