An autonomous stair climbing method for the crawler robot based on a hybrid reinforcement learning controller

In response to problems in using deep reinforcement learning to perform the stair climbing task for the crawler robot, such as the lack of theoretical values for stair size range, the inaccurate force on the model using wheel to simulate track and difficulty in training, we propose an autonomous stair climbing method for the crawler robot based on a hybrid reinforcement learning controller. We propose a dynamics parameter settings method for fast simulation model by studying the distribution model of the ground pressure of the track, which improves the accuracy of the force on the fast simulation model in the stair climbing task. Our method calculates the climbing ability of the crawler robot by studying the kinematics and dynamics models of the real crawler robot in each stage of the stair climbing task, and combining with the control flow of the hybrid reinforcement learning controller. Further, we develop the training course of the hybrid reinforcement learning controller. The agent is trained in ROS-Gazebo. The course reduces the impact of poor trajectory on training, achieves higher success rates and faster convergence speed compared to random training, and effectively improves the generalization ability of the strategy.