To enable autonomous robot navigation among unknown dynamic obstacles, a real-time adaptive motion planner (RAMP) plans the robot motion online based on sensing the environment as the robot moves with sensors mounted on the robot. However, the sensed environmental data from the robot's local view is usually incomplete due to occlusions from obstacles and limited sensing range.This paper incorporates learning about the environment into the RAMP framework by leveraging the Hilbert Maps framework to generate a probabilistic model of occupancy of the unknown dynamic environment based on past observations. Utilizing this probabilistic model enables RAMP to reason about trajectory fitness when sensing information is partial and incomplete. This allows the RAMP robot to take advantage of what it has experienced from being in the dynamic environment before to inform its subsequent executions even though the dynamic environment changes in unknown ways. The effectiveness of incorporating such learned probabilistic data into RAMP is shown in both simulation and real experiments.

This paper addresses the problem of real-time, non-holonomic motion planning in environments with moving obstacles of unforeseen, arbitrary motion. An approach is introduced to smoothly switch trajectories by generating feasible non-holonomic trajectory segments on the fly as the robot moves in such an environment, extending the real-time adaptive motion planning (RAMP) approach that is used for holonomic motion. It allows efficient on-line simultaneous planning and execution of non-holonomic trajectories and enables a robot to adapt to changes in the environment while taking into account robot motion uncertainty. The effectiveness and efficiency of the method has been verified through real experiments with a mobile robot and several dynamic obstacles of unforeseen motion to the robot.