Research on Intelligent Control of Biped Robot Based on D-DQN Reinforcement Learning Algorithm
Aiming at the large trajectory deviation and low efficiency of existing intelligent control algorithms for biped robots,a control algorithm based on D-DQN reinforcement learning is proposed.Firstly,this paper analyzes the coordinate transformation rela-tionship in the biped robot motion and the compensation process of robot joints and links,and then achieves the dimension reduction of complex nonlinear motion process based on Q-value network.The double weight design method of Q-value network weight and auxil-iary weight is adopted to further strengthen the performance of DQN network,and the Tanh function is used as the activation function of neural network to improve the numerical training ability of DQN network.The experience playback pool plays a key auxiliary role in the data training and interaction.The reward value is input into the objective function to further improve the control accuracy of the biped robot.Finally,the virtual constraint control is used to improve the stability of the biped robot.The experimental results show that under the D-DQN reinforcement learning control algorithm,it takes only 115 s for the robot to complete the first stage test is on-ly 115 s,with a comprehensive trajectory deviation of 0.02 m,and the gait switching limit cycle test has a good stability.
万方数据
维普
