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

A multibody approach with graphical user interface for simulating truck dynamics

This document reports on the development of smoothness criteria for weigh-in-motion (WIM) scale approaches. The criteria are meant to screen sites for excessive truck dynamic loading that exacerbates WIM scale error beyond levels recommended by the American Society of Testing and Materials (ASTM). WIM scale error was related to pavement profile characteristics using a large simulation study of the response of virtual trucks over measured profiles. A distribution error was compiled over the truck population for steer axle, tandem axle, and total vehicle weight at each site. The error distributions were summarized by their 95th percentile absolute error levels. The error levels assigned to each site were then used as a correlation standard for the proposed roughness indices and for the selection of corresponding threshold values. Instead of a single index, two versions of the Butterworth filter were selected for use in the specifications. One addresses "Short Range" roughness and the other "Long Range" roughness. The Short and Long Range WIM error indices were then statistically related to WIM scale error to set the threshold values.

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Thomas D. Gillespie

Papers

A multibody approach with graphical user interface for simulating truck dynamics

Smoothness criteria for wim scale approaches

Characterizing the Road-Damaging Dynamics of Truck Tandem Suspensions

Simulation of the Performance Based Standards (PBS) Low-Speed 90° Turn Test in TruckSim by Jumping Back in Time

Using Vehicle Dynamics Simulation as a Teaching Tool in Automotive Engineering Courses