基于碰撞预测的强化模仿学习机器人导航方法OA北大核心CSTPCD

The learning-based robot navigation methods have high dependence on the dataset and imperfect performance in some specific environments,for example,agents cannot run towards its goal through a wide-open space and have high collision rate in space with dense obstacles.In order to improve the navigation performance of robots in multi-obstacle scenarios,a reinforcement imitation learning navigation method based on collision prediction is proposed.Firstly,the state space,action space,and reward function are built for the Markov decision process(MDP)based on the performance of the robot without model.The model is trained in simulation environment based on reinforcement learning to allow the robot to acquire navigation and obstacle avoidance abilities in sparse obstacle environments.To improve the shortcomings of reinforcement learning in terms of imperfect performance in specific environments,imitation learning is used to train the policy.Finally,a collision prediction model is designed to combine traditional control with deep learning to make the robot adaptively select the appropriate control policy in different environments based on the prediction results,which greatly improves the safety of navigation.The navigation performance and generalization capability of the proposed method are experimentally verified in a large number of never-before-encountered scenarios.