Database-driven model predictive control system for online adaptation of an autonomous excavator to environmental conditions

This paper presents the design of a database-driven model predictive control (DD-MPC) system for the online adaptation of autonomous excavators to environmental conditions. Control systems for autonomous excavators should consider environmental conditions as these affect their performance for a given excavation operation. Moreover, these conditions may change during operation. MPC was performed using an excavator-environment interaction model, which was estimated online using DD-Modeling to represent changes in environmental conditions. The target excavation trajectory was modified by predicting excavation motion using MPC and decision based on the prediction to complete a given excavation operation regardless of the environmental conditions. The proposed system was experimentally verified using a radio-controlled excavator, and it was confirmed that a given operation could be completed by adapting to environmental conditions.