Autonomous Waypoint Generation with Safety Guarantees: On-Line Motion Planning in Unknown Environments
This addresses the problem of safe and efficient navigation for mobile robots in unstructured settings, representing an incremental improvement by combining existing techniques like semi-definite programming and reinforcement learning.
The paper tackles on-line motion planning for mobile robots in unknown environments by developing RAW, a reactive planner that generates locally maximal ellipsoids for safety and uses reinforcement learning for waypoint selection, achieving collision avoidance with theoretical guarantees and near-optimal trajectories validated in experiments.
On-line motion planning in unknown environments is a challenging problem as it requires (i) ensuring collision avoidance and (ii) minimizing the motion time, while continuously predicting where to go next. Previous approaches to on-line motion planning assume that a rough map of the environment is available, thereby simplifying the problem. This paper presents a reactive on-line motion planner, Robust Autonomous Waypoint generation (RAW), for mobile robots navigating in unknown and unstructured environments. RAW generates a locally maximal ellipsoid around the robot, using semi-definite programming, such that the surrounding obstacles lie outside the ellipsoid. A reinforcement learning agent then generates a local waypoint in the robot's field of view, inside the ellipsoid. The robot navigates to the waypoint and the process iterates until it reaches the goal. By following the waypoints the robot navigates through a sequence of overlapping ellipsoids, and avoids collision. Robot's safety is guaranteed theoretically and the claims are validated through rigorous numerical experiments in four different experimental setups. Near-optimality is shown empirically by comparing RAW trajectories with the global optimal trajectories.