Optimization-Based Motion Planning and Model Predictive Control for Autonomous Driving : With Experimental Evaluation on a Heavy-Duty Construction Truck

This thesis addresses smooth motion planning and path following control of autonomous large and heavy industrial vehicles, such as trucks and buses, using optimization-based techniques. Autonomous driving is a rapidly expanding technology that promises to play an important role in future society, since it aims at more energy efficient, more convenient, and safer transport systems. First, we propose a clothoid-based path sparsification algorithm to describe a reference path. This approach relies on a sparseness regularization technique such that a minimal number of clothoids is used to describe the reference path. Second, we introduce a novel framework, in which path planning problems are posed in a convex optimization format, even when considering the vehicle dimension constraints, which maximizes the path planning performance in very constrained environments. Third, we present a progress maximization (i.e., traveling time minimization) model predictive controller for autonomous vehicles. The proposed controller optimizes the vehicle lateral and longitudinal motion simultaneously and its effectiveness is demonstrated, in simulation, even in the presence of obstacles. Fourth, we design a smooth and accurate model predictive controller tailored for industrial vehicles, where the main goal is to reduce the vehicle "wear and tear" during its operation. The controller effectiveness is shown both in simulation and experimentally in a Scania construction truck. We showed that the proposed controller has an extremely promising performance in real experiments. Fifth, we propose a novel terminal cost and a terminal state set in order to guarantee closed-loop stability when designing and implementing a linear time-varying model predictive controller for autonomous path following. The controller successfully stabilizes an autonomous Scania construction truck.QC 20180824