Autonomous rally racing with AutoRally and model predictive control

Abstract

The ability to conduct experiments in the real world is a critical step for roboticists working to create autonomous systems that achieve human-level task performance. Self-driving vehicles are a domain that has received significant attention in recent years, in part because of their potential societal benefit. However, there is still a significant performance gap between human drivers and self-driving vehicles. The task of off-road rally racing is an especially difficult driving task where many of the unsolved challenges occur frequently. This thesis opens the domain of autonomous rally racing to researchers and conducts the first rally race between autonomous and human drivers. We created the AutoRally platform, a robust, scaled self-driving vehicle and demonstrated AutoRally driven at high speed on a dirt track by the model predictive path integral controller. The controller optimizes control plans on-the-fly onboard the robot using a dynamics model learned from data and a hand-coded task description, also called a cost function. To enable rally racing, an additional layer of cost function optimization, that operates on the time scale of lap times, was created to replace the hand-coded cost function with one adapted through interactions with the system. We explore representations and optimization methods for the racing cost function, and then compare driving performance to human and autonomous drivers using the AutoRally platform at the Georgia Tech Autonomous Racing Facility