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Planning Algorithms
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  • Cited by 1493
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Fernandez, Daniel C. and Hollinger, Geoffrey A. 2017. Model Predictive Control for Underwater Robots in Ocean Waves. IEEE Robotics and Automation Letters, Vol. 2, Issue. 1, p. 88.

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    Abayazid, Momen Pacchierotti, Claudio Moreira, Pedro Alterovitz, Ron Prattichizzo, Domenico and Misra, Sarthak 2016. Experimental evaluation of co-manipulated ultrasound-guided flexible needle steering. The International Journal of Medical Robotics and Computer Assisted Surgery, Vol. 12, Issue. 2, p. 219.

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    Abbadi, Ahmad and Matousek, Radomil 2016. Hybrid rule-based motion planner for mobile robot in cluttered workspace. Soft Computing,

    Adler, Aviv and Karaman, Sertac 2016. 2016 IEEE International Conference on Robotics and Automation (ICRA). p. 2788.

    Agustín, Alba Olivares, Alberto and Staffetti, Ernesto 2016. Modeling and Simulation in Engineering, Economics and Management.

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    Allen, Ross and Pavone, Marco 2016. AIAA Guidance, Navigation, and Control Conference.

    Althoff, Daniel Weber, Bernhard Wollherr, Dirk and Buss, Martin 2016. Closed-loop safety assessment of uncertain roadmaps. Autonomous Robots, Vol. 40, Issue. 2, p. 267.

    Arslan, Omur and Koditschek, Daniel E. 2016. 2016 IEEE International Conference on Robotics and Automation (ICRA). p. 1.

    Arslan, Omur Guralnik, Dan P. and Koditschek, Daniel E. 2016. Coordinated Robot Navigation via Hierarchical Clustering. IEEE Transactions on Robotics, Vol. 32, Issue. 2, p. 352.

    Awheda, Mostafa D. and Schwartz, Howard M. 2016. 2016 Annual IEEE Systems Conference (SysCon). p. 1.

    Awheda, Mostafa D. and Schwartz, Howard M. 2016. A Decentralized Fuzzy Learning Algorithm for Pursuit-Evasion Differential Games with Superior Evaders. Journal of Intelligent & Robotic Systems, Vol. 83, Issue. 1, p. 35.

    Bae, Hyunki Jeon, Soo and Huissoon, Jan P. 2016. Vision and force/torque integration for realtime estimation of fast-moving object under intermittent contacts. ROBOMECH Journal, Vol. 3, Issue. 1,

    Bardi, Martino and Maldonado López, Juan Pablo 2016. A Dijkstra-Type Algorithm for Dynamic Games. Dynamic Games and Applications, Vol. 6, Issue. 3, p. 263.

    Becerra, Israel Murrieta-Cid, Rafael Monroy, Raul Hutchinson, Seth and Laumond, Jean-Paul 2016. Maintaining strong mutual visibility of an evader moving over the reduced visibility graph. Autonomous Robots, Vol. 40, Issue. 2, p. 395.

    Bhandari, Subodh Farinella, Jeremiah and Lay, Clayton 2016. AIAA Infotech @ Aerospace.

    Björkenstam, Staffan Delfs, Niclas Carlson, Johan S. Bohlin, Robert and Lennartson, Bengt 2016. Enhancing Digital Human Motion Planning of Assembly Tasks Through Dynamics and Optimal Control. Procedia CIRP, Vol. 44, p. 20.

    Boidot, Emmanuel Marzuoli, Aude and Feron, Eric 2016. 2016 IEEE International Conference on Robotics and Automation (ICRA). p. 3154.

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    Planning Algorithms
    • Online ISBN: 9780511546877
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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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