受限密集环境下基于对比学习和强化学习的机器人导航方法OA北大核心CSTPCD

Robot navigation in dynamic environment is an important but challenging task.For the robot navigation in re-stricted and dense environment,this paper proposes a robot navigation method based on the combination of deep re-inforcement learning(DRL)and contrastive learning.Firstly,the trajectory vectorization is used to obtain the his-tory information of robot and humans,and a subgraph network is designed to aggregate it,so that the ability of robot to predict future scenes is improved.Secondly,the interaction information between agents(robot and humans)is extracted by the graph neural network(GNN),which gives the robot the ability to predict the intention of humans.Finally,on the basis of reinforcement learning,contrastive learning is integrated,and a positive sample enhance-ment method is proposed based on the nature of stochastic policy reinforcement learning algorithm,so as to give the robot the ability to judge the security of other position in the scene and to find more feasible paths,improving navi-gation success rate in complex environment.Simulation results show that the proposed method has better perform-ance than the existing method in restricted and dense environment.