Autonomous Waypoint Generation with Safety Guarantees: On-Line Motion Planning in Unknown Environments

Abstract

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.

Enhancement for Robustness of Koopman Operator-based Data-driven Mobile Robotic Systems

“…Employing models for mobile robot motion planning and control can be beneficial for integrating motion constraints in planning [1,2] and deriving control performance guarantees (e.g., robustness guarantees [3,4]). Yet, there exist many instances in which robots interact physically with their environment and that these interactions are uncertain.…”

“…, ψ Q ]. 2 Then the Koopman operator can be approx- 1 We consider the most general formulation in which inputs are generated from an exogenous forcing term. For details on other formulations the reader is referred to [55, Section 3.1].…”

“…For details on other formulations the reader is referred to [55, Section 3.1]. 2 Examples of dictionaries include polynomial function bases, Fourier modes, spectral elements or other sets of (differentiable) functions of the full state observables. The choice of the specific dictionary to employ is a hyper-parameter inherent to EDMD, and is typically done empirically; see [36] for a discussion on this topic.…”