空中机器人悬停控制的强化学习算法OA

As flying carriers for aerial operations,aerial robots have high requirements for level flight performance.The equipment they carry and the impact of reaction forces during operation also require aerial robots to have strong stable pose capabilities.This article focuses on hovering control for aerial robots,with an emphasis on improving reinforcement learning efficiency for this task.Firstly,the actor-critic algorithm based on behavioural value Q(QAC)and the deep deter-ministic policy gradient algorithm(DDPG)are used for robot hovering control learning.Subsequently,a policy gradient algorithm with added supervisors was proposed,using a PID controller as the dynamic monitor.Finally,simulation was conducted and the algorithm was deployed to the actual environment.Simulation and experimental results show that the designed reinforcement learning algorithm can achieve self-learning of hovering control for aerial robots,and compared with QAC and DDPG,it has higher learning efficiency,faster convergence speed,and smoother hovering effect.