Optimization-Based Path-Velocity Control for Time-Optimal Path Tracking under Uncertainties

Abstract

This paper addresses the path-tracking problem of time-optimal trajectories under model uncertainties, by proposing a real-time predictive scaling algorithm. The algorithm is formulated as a convex optimization problem, designed to balance the trade-off between improving feasibility and time optimality of a trajectory. The predicted trajectory is scaled based on the presence of path segments that are particularly sensitive to model uncertainties within the prediction horizon. Numerical simulations and experiments demonstrate that the proposed scaling algorithm reduces the path traversal time, while preserving similar path-tracking accuracy compared to an existing non-predictive method.