Probabilistic approaches for self-tuning path tracking controllers using prior knowledge of the terrain

Nowadays, agricultural and mining industry applications require saving energy in mobile robotic tasks. This critical issue encouraged us to enhance the performance of path tracking controllers during manoeuvring over slippery and rough terrains. In this scenario, we propose probabilistic approaches under machine learning schemes in order to optimally self-tune the controller. The approaches are real time implemented and tested in a mining machinery skid steer loader Cat® 262C under gravel and muddy terrains (and their transitions). Finally, experimental results presented in this work show that the performance of the controller enhances up to 20% (average) without compromising saturations in the actuators.