PATH CONTROL OF AN AUTOMATED HAULER

The vision of self driving cars has existed for a long time and the field of autonomous vehicles has been of great interest to researchers and companies. Volvo construction equipment presented their Electrical Site project in September 2016, with predictions of reducing carbon emission up to 95% and total cost of ownership by 25%. In the project, multiple autonomous haulers are intended to work in a fleet, loading, unloading and charging in a cyclic behavior. This masterthesis focus on the lateral control system of the automated hauler platform HX. The platform is modeled in an comprehensive simulation environment and three different control algorithms have been implemented and tested; An adaptive Proportional, Integral and Derivative (PID) controller, Stanley and the Proportional Integral + Proportional controller. The PID controller is tuned using the Nyquist stability criterion and the other two algorithms are tuned using a Genetic Algorithm. Results indicate that, to reach the optimal performance of the tested algorithms, manual tuning from experimental testing is required