Optimization-Based Motion Planning and Model Predictive Control for Autonomous Driving

This thesis addresses motion planning and control of autonomous heavyduty 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 the near future, since it aims at more energy efficient, more convenient, and safer transport systems. Autonomous vehicles are expected to have their first big impact in closed environments, such as mining areas and construction sites. In these sites, autonomous vehicle enabling infrastructure expansion and law adaption are significantly simpler than in an urban environment. Also, most of the challenging problems that must be addressed in urban environments are absent. 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. The number of clothoids used to describe the path depends on the maximum deviation allowed between the reference and the clothoid-based 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. In the case of off-road applications, solving the problem successively improves the planned trajectories. In the case of on-road applications, the planning can be seen as an optimal refinement of the road centerline, and, in the case of vehicles with prominent overhangs, the algorithm minimizes the amount of vehicle overhang that exits the driving lane. Third, we present a progress maximization (i.e., traveling time minimization) model predictive controller for autonomous vehicles. The prediction model combines simple kinematics with vehicle dynamics constraints. The proposed controller optimizes the vehicle lateral and longitudinal motion simultaneously and its effectiveness is demonstrated, in simulation, including 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. Driving smoothness is encouraged by minimizing the firstand second-order spatial derivatives of the vehicle curvature. 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 with an average deviation from the path of 6 cm and never exceeding 30 cm in challenging tracks, at both high and low speeds. 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 terminal state set is the maximum positive invariant set over all the linear timeinvariant models in a multi-plant description. The terminal cost is the upper bound on the infinite cost-to-go incurred by applying a linear-quadratic regulator control law to any of the possible models in the multi-model representation. The controller successfully stabilizes an autonomous Scania construction truck even when other model predictive controllers, with none or milder terminal cost and terminal state set constraints, cannot do it.