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Book ChapterDOI

Nonholonomic Motion Planning

01 Oct 1992-Vol. 38, pp 355-394
TL;DR: In this article, nonholonomic kinematics and the role of elliptic functions in constructive controllability, R.W. Murray and S.J. Sussmann planning smooth paths for mobile robots, P. Jacobs and J.P. Laumond motion planning for non-holonomic dynamic systems, M. Reyhanoglu et al a differential geometric approach to motion planning, G.G. Lafferriere and H.
Abstract: Nonholonomic kinematics and the role of elliptic functions in constructive controllability, R.W. Brockett and L. Dai steering nonholonomic control systems using sinusoids, R.M. Murray and S. Shakar Sastry smooth time-periodic feedback solutions for nonholonomic motion planning, L. Gurvits and Zexiang Li lie bracket extensions and averaging - the single-bracket case, H.J. Sussmann and Wensheng Liu singularities and topological aspects in nonholonomic motion planning, J.-P. Laumond motion planning for nonholonomic dynamic systems, M. Reyhanoglu et al a differential geometric approach to motion planning, G. Lafferriere and H.J. Sussmann planning smooth paths for mobile robots, P. Jacobs and J. Canny nonholonomic control and gauge theory, R. Montgomery optimal nonholonomic motion planning for a falling cat, C. Fernandes et al nonholonomic behaviour in free-floating space manipulators and its utilization, E.G. Papadopoulos.
Citations
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MonographDOI
01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Abstract: 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.

6,340 citations


Cites background from "Nonholonomic Motion Planning"

  • ...The term nonholonomic planning was introduced by Laumond [596] to describe the problem of motion planning for wheeled mobile robots (see [598, 636] for overviews)....

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  • ...For other works on nonholonomic planning, see the survey [599] and [68, 279, 336, 337, 356, 359, 485, 582, 636, 675]....

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Book
20 May 2005
TL;DR: In this paper, the mathematical underpinnings of robot motion are discussed and a text that makes the low-level details of implementation to high-level algorithmic concepts is presented.
Abstract: A text that makes the mathematical underpinnings of robot motion accessible and relates low-level details of implementation to high-level algorithmic concepts. Robot motion planning has become a major focus of robotics. Research findings can be applied not only to robotics but to planning routes on circuit boards, directing digital actors in computer graphics, robot-assisted surgery and medicine, and in novel areas such as drug design and protein folding. This text reflects the great advances that have taken place in the last ten years, including sensor-based planning, probabalistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, rleating low-level implementation details to high-level algorithmic concepts.

1,811 citations

Proceedings ArticleDOI
01 Jan 2001
TL;DR: This paper proposes a randomized motion planning architecture for dynamical systems in the presence of fixed and moving obstacles that addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning.
Abstract: Planning the path of an autonomous, agile vehicle in a dynamic environment is a very complex problem, especially when the vehicle is required to use its full maneuvering capabilities. Recent efforts aimed at using randomized algorithms for planning the path of kinematic and dynamic vehicles have demonstrated considerable potential for implementation on future autonomous platforms. This paper builds upon these efforts by proposing a randomized motion planning architecture for dynamical systems in the presence of fixed and moving obstacles. This architecture addresses the dynamic constraints on the vehicle's motion, and it provides at the same time a consistent decoupling between low-level control and motion planning. Simulation examples involving a ground robot and a small autonomous helicopter, are presented and discussed.

644 citations

01 Jan 2010
TL;DR: A new voting-based object pose extraction algorithm that does not rely on 2D/3D feature correspondences and thus reduces the early-commitment problem plaguing the generality of traditional vision-based pose extraction algorithms is shown.
Abstract: Society is becoming more automated with robots beginning to perform most tasks in factories and starting to help out in home and office environments. One of the most important functions of robots is the ability to manipulate objects in their environment. Because the space of possible robot designs, sensor modalities, and target tasks is huge, researchers end up having to manually create many models, databases, and programs for their specific task, an effort that is repeated whenever the task changes. Given a specification for a robot and a task, the presented framework automatically constructs the necessary databases and programs required for the robot to reliably execute manipulation tasks. It includes contributions in three major components that are critical for manipulation tasks. The first is a geometric-based planning system that analyzes all necessary modalities of manipulation planning and offers efficient algorithms to formulate and solve them. This allows identification of the necessary information needed from the task and robot specifications. Using this set of analyses, we build a planning knowledge-base that allows informative geometric reasoning about the structure of the scene and the robot's goals. We show how to efficiently generate and query the information for planners. The second is a set of efficient algorithms considering the visibility of objects in cameras when choosing manipulation goals. We show results with several robot platforms using grippers cameras to boost accuracy of the detected objects and to reliably complete the tasks. Furthermore, we use the presented planning and visibility infrastructure to develop a completely automated extrinsic camera calibration method and a method for detecting insufficient calibration data. The third is a vision-centric database that can analyze a rigid object's surface for stable and discriminable features to be used in pose extraction programs. Furthermore, we show work towards a new voting-based object pose extraction algorithm that does not rely on 2D/3D feature correspondences and thus reduces the early-commitment problem plaguing the generality of traditional vision-based pose extraction algorithms. In order to reinforce our theoric contributions with a solid implementation basis, we discuss the open-source planning environment OpenRAVE, which began and evolved as a result of the work done in this thesis. We present an analysis of its architecture and provide insight for successful robotics software environments.

540 citations