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Planning Algorithms
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  • Cited by 2173
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Fan, Changxiang Shirafuji, Shouhei and Ota, Jun 2019. Intelligent Autonomous Systems 15. Vol. 867, Issue. , p. 174.

    Bordalba, Ricard Ros, Lluís and Porta, Josep M. 2019. Advances in Robot Kinematics 2018. Vol. 8, Issue. , p. 170.

    Fleisch, Ruth Entner, Doris Prante, Thorsten and Pfefferkorn, Reinhard 2019. Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial and Societal Problems. Vol. 49, Issue. , p. 339.

    Ibanez, Aurélien Bidaud, Philippe and Padois, Vincent 2019. Humanoid Robotics: A Reference. p. 1541.

    Lai, Shih-Kung 2019. Cities as Spatial and Social Networks. p. 9.

    Noreen, Iram Khan, Amna and Habib, Zulfiqar 2019. Intelligent Computing. Vol. 857, Issue. , p. 346.

    Yoshida, Eiichi Kanehiro, Fumio and Laumond, Jean-Paul 2019. Humanoid Robotics: A Reference. p. 1575.

    Roggendorf, Simon Ecker, Christian Storms, Simon and Herfs, Werner 2019. Advances in Production Research. p. 228.

    Das, Arun and Woolsey, Craig A. 2019. Workspace Modeling and Path Planning for Truss Structure Inspection by Unmanned Aircraft. Journal of Aerospace Information Systems, Vol. 16, Issue. 1, p. 37.

    Ivan, Vladimir Yang, Yiming Merkt, Wolfgang Camilleri, Michael P. and Vijayakumar, Sethu 2019. Robot Operating System (ROS). Vol. 778, Issue. , p. 211.

    Yoshida, Eiichi and Mombaur, Katja 2019. Humanoid Robotics: A Reference. p. 1571.

    Tradacete, Miguel Sáez, Álvaro Arango, Juan Felipe Gómez Huélamo, Carlos Revenga, Pedro Barea, Rafael López-Guillén, Elena and Bergasa, Luis Miguel 2019. Advances in Physical Agents. Vol. 855, Issue. , p. 16.

    Park, Young-Jin and Choi, Han-Lim 2019. InfoSSM: Interpretable Unsupervised Learning of Nonparametric State-Space Model for Multi-modal Dynamics.

    Kortik, Sitar and Saranli, Uluc 2019. Robotic Task Planning Using a Backchaining Theorem Prover for Multiplicative Exponential First-Order Linear Logic. Journal of Intelligent & Robotic Systems,

    Sreenivasa, Manish Laumond, Jean-Paul Mombaur, Katja and Berthoz, Alain 2019. Humanoid Robotics: A Reference. p. 1679.

    Gilleron, Jerome B. Muehlberg, Marc Payan, Alexia Choi, Youngjun Briceno, Simon I. and Mavris, Dimitri N. 2019. Framework for Multi-Asset Comparison and Rapid Down-selection for Earth Observation Missions.

    Bouyarmane, Karim Caron, Stéphane Escande, Adrien and Kheddar, Abderrahmane 2019. Humanoid Robotics: A Reference. p. 1763.

    Liu, Chang and Ferrari, Silvia 2019. Adaptive Planning and Control with Convolutional Neural Network-based Perception for Autonomous Taxiing.

    Bai, Tingting and Wang, Daobo 2019. Cooperative trajectory optimization for unmanned aerial vehicles in a combat environment. Science China Information Sciences, Vol. 62, Issue. 1,

    Haj Darwish, Ahmed Joukhadar, Abdulkader Kashkash, Mariam and Lam, James 2018. Using the Bees Algorithm for wheeled mobile robot path planning in an indoor dynamic environment. Cogent Engineering, Vol. 5, Issue. 1,


Book description

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. This text and reference is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.


‘This is a terrific book, a milestone in the robotics literature.’

Matt Mason - Director of The Carnegie Mellon Robotics Institute

‘Motion planning is an important field of research with applications in such diverse terrains as robotics, molecular modeling, virtual environments, and games. Over the past two decades a huge number of techniques have been developed, all with their merits and shortcomings. The book by Steve LaValle gives an excellent overview of the current state of the art in the field. It should lie on the desk of everybody that is involved in motion planning research or the use of motion planning in applications.’

Mark Overmars - Utrecht University

‘A great book at the junction where Robotics, Artificial Intelligence, and Control are crossing their paths. For many problems you will find in-depth discussion and algorithms; for virtually all others in the field, an intriguing introduction to make you at ease and entice you to further probing the matter.’

Antonio Bicchi - della Università di Pisa

‘Since the early 90s, Latombe's book has been the authoritative source for students and researchers working on motion planning problems in robotics. During the succeeding decade and half, the motion planning field moved forward with significant developments. LaValle’s book picks up the field where Latombe's book left it, describing in detail major developments such as probabilistic roadmaps, manipulation, and coverage planning. Moreover, the book describes a fundamental generalization of configuration spaces to information spaces. The chapters on information spaces appear here for the first time, making them accessible to students and researchers who wish to tackle progressively more challenging real-world motion planningproblems in robotics.’

Elon Rimon - Technion

‘Planning Algorithms is a daring title. It aims at being ecumenical gathering students and their professors scattered in various departments of Engineering and calling them to share the same mathematical foundations. The story starts with motion planning algorithms. Steve LaValle’s deep extensive understanding and his effective expertise in that area are shared in this book. They allow the author to go further and to generalize the famous configuration space of the piano mover problem into the information space. This is the core of the title ambition. All the seminal material born with Robotics, Artificial Intelligence and Control, and developed for more than thirty years in a sparse way, are there uniquely unified. The book is not a catalogue of methods. It is the coherent view of a single researcher. The style is nice making the reading fluent: there is a good balance between informal introduction of concepts and the necessary technical developments. Students, researchers and engineers exploring routes in Artificial Intelligence and Robotics, in Graphics and CAD/CAM, and even Molecular Biology now, will find here amazing computational foundations for their topics.’

Jean-Paul Laumond - LAAS-CNRS

' … this book really is monumental and well-written piece of work, and although few will have cause to read more than a fraction of its content, at its price it deserves to find its way onto the bookshelves of many of us, as well as being recommended to our students.'

Source: ScienceDirect

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