Deep reinforcement learning and robust SLAM based robotic control algorithm for self-driving path optimization

Abstract

A reward shaping deep deterministic policy gradient (RS-DDPG) and simultaneous localization and mapping (SLAM) path tracking algorithm is proposed to address the issues of low accuracy and poor robustness of target path tracking for robotic control during maneuver. RS-DDPG algorithm is based on deep reinforcement learning (DRL) and designs a reward function to optimize the parameters of DDPG to achieve the required tracking accuracy and stability. A visual SLAM algorithm based on semantic segmentation and geometric information is proposed to address the issues of poor robustness and susceptibility to interference from dynamic objects in dynamic scenes for SLAM based on visual sensors. Using the Apollo autonomous driving simulation platform, simulation experiments were conducted on the actual DDPG algorithm and the improved RS-DDPG path-tracking control algorithm. The research results indicate that the proposed RS-DDPG algorithm outperforms the DDPG algorithm in terms of path tracking accuracy and robustness. The results showed that it effectively improved the performance of visual SLAM systems in dynamic scenarios.