Principles of Robot Motion: Theory, Algorithms, and Implementations

Portada
MIT Press, 2005 M05 20 - 626 páginas
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.

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Derechos de autor

Otras ediciones - Ver todas

Principles of Robot Motion: Theory, Algorithms, and Implementations
Howie Choset,Kevin M. Lynch,Seth Hutchinson,George A. Kantor,Wolfram Burgard
Vista previa limitada - 2005
Principles of Robot Motion: Theory, Algorithms, and Implementations
Howie Choset,Kevin M. Lynch,Seth Hutchinson,George A. Kantor,Wolfram Burgard
Sin vista previa disponible - 2005

Términos y frases comunes

actuator algorithm angle approach boundary boustrophedon cell center of mass chapter Christoffel symbols closest obstacle collision collision-free compute configuration q configuration space connected component consider constraints construct convex coordinates corresponding critical points decomposition defined deformation retract degrees of freedom denoted described diffeomorphic distance function distribution dynamics edges environment equation estimate example Figure frame free space Gaussian goal configuration gradient grid inertia matrix initial input intersection Kalman filter kinematic Lie bracket linear manifold measurement metric mobile robot motion planning node optimal orientation output particle filter path pbwt pixel planar plane planner polygonal potential function priority queue problem Qfree qinit random reachable represent representation roadmap rotation sampling sampling-based sensor sequence slice STLC subset tangent tangent space theorem trajectory tree u₁ underactuated unicycle update vector fields vertices visibility graph workspace Y₁ zero

Pasajes populares

Página 572 - JA Castellanos, JMM Montiel, J. Neira and JD Tardos. The SPmap: A Probabilistic Framework for Simultaneous Localization and Map Building, IEEE Trans.
Aparece en 27 libros desde 2000-2008
Página 584 - JP Laumond, PE Jacobs, M. Taix and RM Murray, "A Motion Planner for Nonholonomic Mobile Robots," IEEE Trans, on Robotics and Automation, vol.
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Página 579 - D. Hsu, J.-C. Latombe, and R. Motwani. Path planning in expansive configuration spaces.
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Página 567 - J. Barraquand, B. Langlois, and J.-C. Latombe. "Numerical Potential Field Techniques for Robot Path Planning".
Aparece en 42 libros desde 1977-2005
Página 569 - Overmars, and AF van der Stappen. The Gaussian sampling strategy for probabilistic roadmap planners. In Proc.
Aparece en 21 libros desde 2001-2008
Página 575 - A. Elfes. Using Occupancy Grids for Mobile Robot Perception and Navigation.
Aparece en 41 libros desde 1989-2007
Página 582 - DE Koditschek and E. Rimon. Robot navigation functions on manifolds with boundary.
Aparece en 20 libros desde 1989-2006

Referencias a este libro

Acerca del autor (2005)

Howie Choset is Associate Professor in the Robotics Institute at Carnegie Mellon University.

Kevin M. Lynch is Associate Professor in the Mechanical Engineering Department, Northwestern University.

Seth Hutchinson is Professor in the Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign.

George Kantor is Project Scientist in the Center for the Foundations of Robotics, Robotics Institute, Carnegie Mellon University.

Wolfram Burgard is Professor of Computer Science and Head of the research lab for Autonomous Intelligent Systems at the University of Freiburg.

Lydia E. Kavraki is Professor of Computer Science and Bioengineering, Rice University.

Sebastian Thrun is Associate Professor in the Computer Science Department at Stanford University and Director of the Stanford AI Lab.

Información bibliográfica

TítuloPrinciples of Robot Motion: Theory, Algorithms, and Implementations
Intelligent Robotics and Autonomous Agents series
AutoresHowie Choset, Kevin M. Lynch, Seth Hutchinson, George A. Kantor, Wolfram Burgard
Ediciónilustrada
EditorMIT Press, 2005
ISBN0262033275, 9780262033275
Largo626 páginas
  
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