Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking

This paper presents a Learning-based Nonlinear Model Predictive Control (LB-NMPC) algorithm to achieve high-performance path tracking in challenging off-road terrain through learning. The LB-NMPC algorithm uses a simple a priori vehicle model and a learned disturbance model. Disturbances are modelled as a Gaussian Process (GP) as a function of system state, input, and other relevant variables. The GP is updated based on experience collected during previous trials. Localization for the controller is provided by an on-board, vision-based mapping and navigation system enabling operation in large-scale, GPS-denied environments. The paper presents experimental results including over 3 km of travel by three significantly different robot platforms with masses ranging from 50 kg to 600 kg and

• Type: Journal
• Year: 2016
• Where: JFR
• Authors: Chris J. Ostafew, Angela P. Schoellig, Timothy D. Barfoot, Jack Collier
• Keywords: JFR 2016