3 accepted papers
We present a novel approach to learning-augmented, long-horizon navigation under uncertainty in large-scale environments in which considering the robot dynamics is essential for informing good behavior. Our approach tightly integrates sampling-based motion planning, which computes dynamically feasib…
While sampling-based approaches have made significant progress, motion planning with dynamics still poses significant challenges as the planner has to generate not only collision-free but also dynamically-feasible trajectories that enable the robot to reach its goal. To improve the efficiency of sam…
Autonomous navigation of steel bridge inspection robots are essential for proper maintenance. Majority of existing robotic solutions for bridge inspection require human intervention to assist in the control and navigation. In this paper, a control system framework has been proposed for a previously…