Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without human intervention. The robot’s software system relied predominately on state-of-the-art AI technologies, such as machine learning and probabilistic reasoning. This article describes the major components of this architecture, and discusses the results of the Grand Challenge race.

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Stanley: The Robot that Won the DARPA Grand Challenge

In order to achieve autonomous operation of a vehicle in urban situations with unpredictable traffic, several realtime systems must interoperate, including environment perception, localization, planning, and control. In addition, a robust vehicle platform with appropriate sensors, computational hardware, networking, and software infrastructure is essential.

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Towards fully autonomous driving: Systems and algorithms

This paper describes a real-time motion planning algorithm, based on the rapidly-exploring random tree (RRT) approach, applicable to autonomous vehicles operating in an urban environment. Extensions to the standard RRT are predominantly motivated by: 1) the need to generate dynamically feasible plans in real-time; 2) safety requirements; 3) the constraints dictated by the uncertain operating (urban) environment. The primary novelty is in the use of closed-loop prediction in the framework of RRT. The proposed algorithm was at the core of the planning and control software for Team MIT's entry for the 2007 DARPA Urban Challenge, where the vehicle demonstrated the ability to complete a 60 mile simulated military supply mission, while safely interacting with other autonomous and human driven vehicles.

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Real-Time Motion Planning With Applications to Autonomous Urban Driving

Survey of advanced suspension developments and related optimal control applications

This book attempts to find a middle ground by balancing engineering principles and equations of use to every automotive engineer with practical explanations of the mechanics involved, so that those without a formal engineering degree can still comprehend and use most of the principles discussed. Either as an introductory text or a practical professional overview, this book is an ideal reference.

Fundamentals of Vehicle Dynamics

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Steady-State Cornering

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Appendix B SAE J6a Ride and Vibration Data Manual

Road roughness is gaining increasing importance as an indicator of road condition, both in terms of pavement performance, and as a major determinant of road user costs. This paper defines roughness measurement systems hierachically into four groups, ranging from profilometric methods (2 groups), through response type road roughness measuring systems (RTRRMS's), and, subjective evaluation. The International Roughness Index (IRI) is defined, and the programs for it's calculation are provided. The IRI is based on simulation of the roughness response of a car travelling at 80 km/h. The report explains how all roughness measurements can be related to this scale, also when travelling at lower speeds than 80 km/h. The IRI emerges as a scale that can be used both for calibration and for comparative purposes.

Guidelines for conducting and calibrating road roughness measurements

The high wheel loads of heavy trucks are a major source of pavement damage by causing fatigue, which leads to cracking, and by permanent deformation, which produces rutting. Among heavy trucks, all do not cause equal damage because of differences in wheel loads, number and location of axles, types of suspensions and tires, and other factors. Further, the damage is specific to pavement properties, operating conditions, and environmental factors. The mechanics of truck-pavement interaction were studied to identify relationships between truck properties and damage (fatigue and rutting). Computer models of trucks were used to generate wheel load histories characteristic of the different trucks and operating conditions. Influence functions, obtained from rigid and flexible pavement structural models, were used to predict responses along the pavement resulting from the truck motions. The pavement responses were evaluated to estimate overall pavement damage caused by each truck. The study assessed the significance of truck, tire, pavement, and environmental factors as determinants of pavement damage. Maximum axle load and pavement thickness have the primary influences on fatigue damage. Truck properties, such as number and location of axles, suspension type, and tire type, are important but less significant. High temperatures in flexible pavements and temperature gradients in rigid pavements adversely affect the damage caused by truck wheel loads with a fairly strong interaction. The report discusses and quantifies the influence of these variables.

Thomas D. Gillespie

Papers

Fundamentals of Vehicle Dynamics

Steady-State Cornering

Appendix B SAE J6a Ride and Vibration Data Manual

Guidelines for conducting and calibrating road roughness measurements

Effects of heavy-vehicle characteristics on pavement response and performance