基于IL-TD3的轮式机器人无地图导航研究OA

In view of the dependence of the wheeled robot navigation method on high-precision maps and its a-daptability issues in dynamic and complex scenarios,this study proposes a mapless navigation method based on IL-TD3(Imitation Learning Enhanced Twin Delayed Deep Deterministic Policy Gradient).The navigation task is modeled as a POMDP(Partially Observable Markov Decision Process),and a LSTM(Long Short-Term Memory)network is combined to process historical information for improving the environmental state modeling.The robot quickly acquires limited mapless navigation capabilities through imitation learning,and continuously explores and trains the TD3 deep reinforcement learning network to enhance its navigation skills.The simulation experiment results show that the naviga-tion trajectory of IL-TD3 in unknown dynamic environments is stable,continuous,and safe,demonstrating good navi-gation performance.The Sim2Real(Simulation to Reality)test results indicate that the unadjusted IL-TD3 model per-forms well in real-world navigation tasks,which proves the robustness and generalization ability of the proposed model.