Design and Experimental Evaluation of Model Predictive Control for Autonomous Articulated Dump Trucks

Abstract Articulated dump trucks (ADT) serve as key vehicles in mining operations, as well as in urban construction sites. As demand for automation in these industries increases, using model predictive controllers (MPC) for the autonomous operation of these vehicles is becoming popular due to their ability to predict and optimize vehicle performance in real-time. However, successful implementation of these controllers requires accurate models of the vehicle. Previous research has only explored MPC concepts using well-established kinematic bicycle models. In order to improve the performance of the existing MPCs, this paper presents dedicated models for different operation scenarios (forward and backward driving) and different ADT sizes (full-sized and compact). These models capture and take advantage of the scenario- and size-related specifics, resulting in better backward driving performance across all ADT sizes, and also enhancements in forward driving specifically in small compact ADTs. The proposed controllers are tested in unpaved terrain of a mining field on a 1.3 t compact and a 20 t full-sized ADT. Experimental results indicate that the controllers successfully fulfill the requirements and improve tracking compared to state-of-the-art methods by 70% in terms of mean absolute lateral error (MAE) when a comparison is possible.