Review of Research Methods for Hypersonic Vehicle Reentry Trajectory Planning

Abstract

Hypersonic vehicle has many advantages, such as wide range of maneuver, strong penetration ability, high strike accuracy, and so on. Reentry trajectory planning is one of the key technologies to support hypersonic vehicle systems. It is necessary to plan feasible or optimal trajectory under the process constraints such as heat flux, dynamic pressure, overload, and terminal constraints such as altitude and velocity. At present, traditional methods are difficult to meet the task requirements of trajectory planning and online trajectory generation under complex conditions with multiple constraints. As an artificial intelligence method, reinforcement learning has strong robustness and the characteristics of "offline training and online deployment", which can make up for the shortcomings of traditional methods and show great potential in trajectory planning. This paper introduces the current research status of traditional trajectory planning methods and reinforcement learning methods, and proposes that the reentry trajectory planning methods will be intelligent in the future.