Learning based nonlinear model predictive control to improve vision based mobile robot path tracking in challenging outdoor environments
This paper presents a Learning based Nonlinear Model Predictive Control (LB NMPC) algorithm for an autonomous mobile robot to reduce path tracking errors over repeated traverses along a reference path. The LB NMPC algorithm uses a simple a priori vehicle model and a learned disturbance model. Disturbances are modelled as a Gaussian Process (GP) based on experience collected during previous traversals as a function of system state, input and other relevant variables. Modelling the disturbance as a GP enables interpolation and extrapolation of learned disturbances, a key feature of this algorithm. 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 1.8 km of travel by a four wheeled, 50 kg robot travelling through challenging terrain (including steep, uneven hills) and by a six wheeled, 160 kg robot learning disturbances caused by unmodelled dynamics at speeds ranging from 0.35 m/s to 1.0 m/s. The speed is scheduled to balance trial time, path tracking errors, and localization reliability based on previous experience. The results show that the system can start from a generic a priori vehicle model and subsequently learn to reduce vehicle and trajectory specific path tracking errors based on experience.
2014 IEEE International Conference on Robotics and Automation (ICRA)