Research on adaptive obstacle avoidance algorithm of robot based on DDPG-DWA.

In this paper, we propose an adaptive obstacle avoidance algorithm based on DDPG (Deep Deterministic Policy Gradient) and DWA (Dynamic-Window Approach) to study the obstacle avoidance problem of robots in complex continuous state space. First, the obstacle avoidance problem is converted into an optimal learning incentive problem, and the self-learning of the obstacle avoidance policy is realized based on DDPG; second, the DWA obstacle avoidance trajectory evaluation function is optimized using the DDPG reward incentive mechanism, and the Experience Replay mechanism; finally, the algorithm model is simulated. The experiments show that the model can significantly circumvent the deficiency of the DWA algorithm in limiting to the optimal local solution in a complex environment and solve the action output problem in the continuous velocity and turning angle value interval of the robot; through the trial and error interaction with the environment and the trajectory evaluation incentive feedback, the obstacle avoidance passing ability of the robot in a complex environment is improved. [Display omitted] • We propose an adaptive obstacle avoidance algorithm based on DDPG (Deep Deterministic Policy Gradient) and DWA (Dynamic Window Approach), named the DDPG-DWA. • The DDPG-DWA algorithm can solve the problem of action output in the continuous velocity and turning angle value range of the robot. • The DDPG-DWA algorithm can avoid the problem of DWA algorithm easily being limited to local obstacle avoidance optimal solutions in complex environments. • The model is suitable for path obstacle avoidance of intelligent agents in complex, narrow and irregular environments. [ABSTRACT FROM AUTHOR]