Reinforcement learning-based safety obstacle avoidance and capture guidance for UAV
To solve the problem that the unmanned aerial vehicle(UAV)faces the mutual constraint between the flying obstacle and the target tracking in the constrained environment,a method of reinforcement learning-based safety obstacle avoidance and capture guidance for UAV is proposed.According to the principle of polar coordinates,the surround tracking controller is designed to drive the UAV to a preset circular orbit in GPS-denied presence.The surround constraint and obstacle avoidance constraint are all transformed into the Markov process,taking velocity,radial error,angular velocity error and obstacle function as state space,and the compensation of control as action space.The reward function considering radial error and obstacle probability is designed.The tracking effect is enhanced and the obstacle avoidance ability is obtained by virtue of the deep deterministic policy gradient(DDPG)algorithm to train the generated agent,and then the UAV surround tracking of stationary/moving targets is realized.Additionally,the introduction of the course learning in the training process transfers past learning strategies to current events and has a faster convergence rate compared to the classical random parameter settings.Finally,the simulation results show that the proposed algorithm can guide the UAV to elliptical surround control and avoid obstacles efficiently.
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