3 accepted papers
Reinforcement learning has shown great potential in improving the performance of robot navigation. In response to the increasing deployments of mobile robots within various scenarios, a data-driven paradigm of navigation approach with safety verification is preferred where one can train RL algorithm…
In this paper, we propose a map-based end-to-end DRL approach for three-dimensional (3D) obstacle avoidance in a partially observed environment, which is applied to achieve autonomous navigation for an indoor mobile robot using a depth camera with a narrow field of view. We first train a neural netw…
It is challenging to develop an online path planning algorithm for Ackermann-steering vehicles to find collision-free and kinematically-feasible paths, that is efficient for dense environments, adaptable to various environments, and suitable for environments with narrow passages. In this paper, we p…