Research on Navigation Algorithm of Unmanned Ground Vehicle Based on Imitation Learning and Curiosity Driven

The application of deep reinforcement learning (DRL) for autonomous navigation of unmanned ground vehicle (UGV) has the problem of sparse rewards, which makes the trained algorithm model difficult to converge and cannot be transferred to real vehicles. In this regard, this paper proposes an effective exploratory learning autonomous navigation algorithm Double I-PPO, which designs pre-training behaviors based on imitation learning (IL) to guide UGV to try positive states, and introduces the intrinsic curiosity module (ICM) to generate intrinsic reward signals to encourage exploratory learning strategies. Build the training scene in Unity to evaluate the performance of the algorithm, and integrate the algorithm strategy into the motion planning stack of the ROS vehicle, so as to extend to the actual scene for testing. Experiments show that in the environment of random obstacles, the method does not need to rely on prior map information. Compared with similar DRL algorithms, the convergence speed is faster and the navigation success rate can reach more than 85%.