LiCS: Navigation Using Learned-Imitation on Cluttered Space

This work proposes a robust and fast navigation system in a narrow indoor environment for UGV (Unmanned Ground Vehicle) using 2D LiDAR. We used behavior cloning with Transformer neural network to learn the optimization-based baseline algorithm. We inject Gaussian noise during expert demonstration to increase the robustness of the learned policy and evaluate the performance of LiCS using both simulation and hardware experiments. It outperforms all other baselines in terms of navigation performance, achieving a success rate 100% in highly cluttered simulated environments. During the hardware experiments, LiCS can maintain safe navigation at maximum speed of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{1.5 m/s}$</tex-math></inline-formula>.