UAV Spiral Maneuvering Trajectory Intelligent Generation Method Based on Virtual Trajectory - Tao Chen; Shaopeng Li; Yong Xian; Leliang Ren; Zhenyu Liu (Drones)
This paper addresses the challenge of ineffective coordination between terminal maneuvering and precision strike capabilities in hypersonic unmanned aerial vehicles (UAVs). To resolve this issue, an intelligent spiral maneuver trajectory generation method utilizing a virtual trajectory framework is proposed. Initially, a relative motion model between the UAV and the virtual center of mass (VCM) is established based on the geometric principles of the Archimedean spiral. Subsequently, the interaction dynamics between the VCM and the target are formulated as a Markov decision process (MDP). A deep reinforcement learning (DRL) approach, employing the proximal policy optimization (PPO) algorithm, is implemented to train a policy network capable of end-to-end virtual trajectory generation. Ultimately, the relative spiral motion is superimposed onto the generated virtual trajectory to synthesize a composite spiral maneuvering trajectory. The simulation results demonstrate that the proposed method achieves expansive spiral maneuvering ranges while ensuring precise target strikes.