Robust Constrained Learning based NMPC enabling reliable mobile robot path tracking
This paper presents a Robust Constrained Learning based Nonlinear Model Predictive Control (RC LB NMPC) algorithm for path tracking in off road terrain. For mobile robots, constraints may represent solid obstacles or localization limits. As a result, constraint satisfaction is required for safety. Constraint satisfaction is typically guaranteed through the use of accurate, a priori models or robust control. However, accurate models are generally not available for off road operation. Furthermore, robust controllers are often conservative, since model uncertainty is not updated online. In this work our goal is to use learning to generate low uncertainty, non parametric models in situ. Based on these models, the predictive controller computes both linear and angular velocities in real time, such that the robot drives at or near its capabilities while respecting path and localization constraints. Localization for the controller is provided by an on board, vision based mapping and navigation system enabling operation in large scale, off road environments. The paper presents experimental results, including over 5 km of travel by a 900 kg skid steered robot at speeds of up to 2.0 m/s. The result is a robust, learning controller that provides safe, conservative control during initial trials when model uncertainty is high and converges to high performance, optimal control during later trials when model uncertainty is reduced with experience.
Int. J. Robotics Res.