Summary This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon based controllers, where the objective is to maximize progress on the track subject to the requirement of staying on the track and avoiding opponents. Two different control formulations are presented. The first controller employs a two-level structure, consisting of a path planner and a nonlinear model predictive controller (NMPC) for tracking. The second controller combines both tasks in one nonlinear optimization problem (NLP) following the ideas of contouring control. Linear time varying models obtained by linearization are used to build local approximations of the control NLPs in the form of convex quadratic programs (QPs) at each sampling time. The resulting QPs have a typical MPC structure and can be solved in the range of milliseconds by recent structure exploiting solvers, which is key to the real-time feasibility of the overall control scheme. Obstacle avoidance is incorporated by means of a high-level corridor planner based on dynamic programming, which generates convex constraints for the controllers according to the current position of opponents and the track layout. The control performance is investigated experimentally using 1:43 scale RC race cars, driven at speeds of more than 3 m s and in operating regions with saturated rear tire forces (drifting). The algorithms run at 50 Hz sampling rate on embedded computing platforms, demonstrating the real-time feasibility and high performance of optimization-based approaches for autonomous racing. Copyright © 2014 John Wiley & Sons, Ltd.
@cite_15: Understanding how a race car driver controls a vehicle at its friction limits can provide insights into the development of vehicle safety systems. In this paper, a race car driver's behaviour inspires the design of an autonomous racing controller. The resulting controller uses the vehicle's centre of percussion (COP) to design feedforward and feedback steering. At the COP, the effects of rotation and translation from the rear tire force cancel each other out; consequently, the feedforward steering command is robust to the disturbances from the rear tire force. Using the COP also simplifies the equations of motion, as the vehicle's lateral motion is decoupled from the vehicle's yaw motion and highlights the challenge of controlling a vehicle when the rear tires are highly saturated. The resulting dynamics can be controlled with a linear state feedback based on a lane-keeping system with additional yaw damping. Utilising Lyapunov theory, the closed-loop system is shown to remain stable even when the rear tires are highly saturated. The experimental results demonstrate that an autonomous vehicle can operate at its limits while maintaining a minimal lateral error.
@cite_18: Describes the design of an automated highway system (AHS) developed over the past ten years in the California PATH program. The AHS is a large, complex system, in which vehicles are automatically controlled. The design and implementation of the AHS required advances in actuator and sensor technologies, as well as the design, analysis, simulation, and testing of large-scale, hierarchical hybrid control systems. The paper focuses on the multilayer AHS control architecture and some questions of implementation. It discusses in detail the design and safety verification of the on-board vehicle control system and the design of the link-layer traffic-flow controller.
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@cite_11: The DARPA Grand Challenge was a landmark in the field of robotics: a race by autonomous vehicles through 132 miles of rough, cross-country Nevada terrain that showcased exciting and unprecedented capabilities in robotic perception, navigation, and control. The event took place in October 2005, and drew teams of competitors from academia and industry, and many garage hobbyists. This book presents fifteen technical papers that are written at a level that makes them easily accessible to a broad technical audience, describing the technology behind most of the robotic vehicles that participated in this famous race. The papers describe each team's driverless vehicle, race strategy, and insights. As a whole, they present the state of the art in autonomous vehicle technology, and offer a glimpse of future technology for tomorrows driverless cars. This book will serve as an authoritative, archival source for the DARPA Grand Challenge and a must have for robotics students and researchers, since it describes the state of the art in perception, planning and control.
Safe autonomous driving at moderate speeds has been demonstrated for example in the context of Autonomous Highway Systems (AHS) in 1997 @cite_18 within the Californian PATH programme, or in contests such as the DARPA Grand Challenge in 2005 @cite_11 and the Urban Challenge in 2007 @cite_2 by numerous research groups. In these projects, the main goal was do develop autonomous driving systems for public traffic situations. Autonomous racing has received less attention, but impressive advances have been made recently also in this discipline. For instance, a fully autonomous Audi TTS has been reported to race at high speeds in @cite_15 using a trajectory tracking controller, which follows a pre-computed trajectory. However, to the best knowledge of the authors, fully automatic racing including obstacle avoidance in dynamic racing situations has not been demonstrated yet in practice.