Autonomous navigation based on PPO for mobile platform

This paper presents an autonomous navigation method based on proximal policy optimization (PPO) algorithm for mobile platform. In this method, GNSS and LADAR are used for sensing environment information. To define the state of reinforcement learning model, an ego position evaluation method is introduced based on improved artificial potential field algorithm. After that, on the basis of PPO algorithm, a kind of action policy function is designed based on Gaussian distribution, which solves the continuity problem of the vehicle linear velocity and yaw velocity. Furthermore, the network framework and reward function of the model are also designed for navigation scenarios. In order to train the navigation model, a virtual environment based on Gazebo is built. The training results show that the ego position evaluation method obviously helps to improve the speed of model convergence. Finally, the navigation model is transplanted to a real environment, which verifies the effectiveness of the proposed method. © 2022 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.