Reactive Trajectory Tracking for Mobile Robots based on Non Linear Model Predictive Control

Abstract

In this paper, a nonlinear model predictive tracking (NMPT) controller for mobile robots is presented. The basic idea is to use a motion model for the vehicle and compute in real-time an optimal M-step-ahead control sequence, which minimizes the total M+l step tracking error of the projected motion. In the presence of obstacles, the controller deviates from the reference trajectory by incorporating into the optimization obstacle-distance information from range sensors (e.g., laser scanner, ultrasound). Numerous simulations were performed and the NMPT consistently converged to the desired trajectories and followed them accurately, despite large initial errors and discontinuities in the desired velocities and orientations. The controller's performance depended strongly on parameters such as the optimization horizon M, and the cost-weights assigned to the various tracking errors. The optimization horizon regulates a trade-off between timely obstacle avoidance and tracking quality (large M) vs. consistently fast convergence (small M). The cost-weights affect tracking quality and also the shape of the path, by regulating trade-offs among position, orientation, and velocity errors. Overall, NMPT seems to offer a promising approach for advanced precision guidance applications, and deserves further investigation.