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51Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic Jonathan D. Gammell1 , Siddhartha S. Srinivasa2 , and Timothy D. Barfoot1 Abstract— Rapidly-explori

Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic Jonathan D. Gammell1 , Siddhartha S. Srinivasa2 , and Timothy D. Barfoot1 Abstract— Rapidly-explori

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Source URL: www.ri.cmu.edu

Language: English - Date: 2014-07-22 11:25:05
52Asymptotic Optimality in Sampling-based Motion Planning Sertac Karaman Although one of the fundamental problems in robotics, the motion planning problem is inherently hard from a computational point of view. In particula

Asymptotic Optimality in Sampling-based Motion Planning Sertac Karaman Although one of the fundamental problems in robotics, the motion planning problem is inherently hard from a computational point of view. In particula

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Source URL: iros2011.org

Language: English - Date: 2011-09-21 07:00:30
53Notes on Randomized Algorithms CS: Fall 2014 James Aspnes:04

Notes on Randomized Algorithms CS: Fall 2014 James Aspnes:04

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Source URL: cs.yale.edu

Language: English - Date: 2014-12-17 20:04:41
54Efficient Motion Planning for Humanoid Robots using Lazy Collision Checking and Enlarged Robot Models Nikolaus Vahrenkamp, Tamim Asfour and R¨udiger Dillmann Institute of Computer Science and Engineering University of K

Efficient Motion Planning for Humanoid Robots using Lazy Collision Checking and Enlarged Robot Models Nikolaus Vahrenkamp, Tamim Asfour and R¨udiger Dillmann Institute of Computer Science and Engineering University of K

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Source URL: wwwiaim.ira.uka.de

Language: English - Date: 2007-08-10 04:41:22
55EG-RRT: Environment-Guided Random Trees for Kinodynamic Motion Planning with Uncertainty and Obstacles L´eonard Jaillet, Judy Hoffman, Jur van den Berg, Pieter Abbeel, Josep M. Porta, Ken Goldberg Abstract— Existing s

EG-RRT: Environment-Guided Random Trees for Kinodynamic Motion Planning with Uncertainty and Obstacles L´eonard Jaillet, Judy Hoffman, Jur van den Berg, Pieter Abbeel, Josep M. Porta, Ken Goldberg Abstract— Existing s

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Source URL: www.ieor.berkeley.edu

Language: English - Date: 2011-11-18 02:34:58
56i A random zoo: sloth, un∨corn, and trx Arjen K. Lenstra and Benjamin Wesolowski EPFL IC LACAL, Station 14, CH-1015 Lausanne, Switzerland Abstract. Many applications require trustworthy generation of public random num

i A random zoo: sloth, un∨corn, and trx Arjen K. Lenstra and Benjamin Wesolowski EPFL IC LACAL, Station 14, CH-1015 Lausanne, Switzerland Abstract. Many applications require trustworthy generation of public random num

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Source URL: eprint.iacr.org

Language: English - Date: 2015-04-22 06:51:37
57From  AND/OR  Search  to  AND/OR     Sampling   Rina  Dechter     Bren  school  of  Informa3on  and  Computer  Sciences     University  of  California,  Irvi

From  AND/OR  Search  to  AND/OR     Sampling   Rina  Dechter     Bren  school  of  Informa3on  and  Computer  Sciences     University  of  California,  Irvi

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Source URL: www.ics.uci.edu

Language: English - Date: 2013-11-12 23:14:08
58The Internet research component of the American Foreign Policy course: Beyond random gleaning for bits of “information” Professor Wayne A. Selcher, Professor of International Studies Emeritus Department of Political

The Internet research component of the American Foreign Policy course: Beyond random gleaning for bits of “information” Professor Wayne A. Selcher, Professor of International Studies Emeritus Department of Political

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Source URL: users.etown.edu

Language: English - Date: 2010-09-16 23:02:56
59Chapter 16 Strategy Optimisation In prior chapters we have considered how to create both an underlying predictive model (such as with the Suppor Vector Machine and Random Forest Classifier) as well as a trading strategy

Chapter 16 Strategy Optimisation In prior chapters we have considered how to create both an underlying predictive model (such as with the Suppor Vector Machine and Random Forest Classifier) as well as a trading strategy

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Source URL: www.quantstart.com

Language: English - Date: 2015-05-18 04:58:44
60Poisson-RRT Chonhyon Park and Jia Pan and Dinesh Manocha http://gamma.cs.unc.edu/PoissonRRT/ Abstract— We present an RRT-based motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach

Poisson-RRT Chonhyon Park and Jia Pan and Dinesh Manocha http://gamma.cs.unc.edu/PoissonRRT/ Abstract— We present an RRT-based motion planning algorithm that uses the maximal Poisson-disk sampling scheme. Our approach

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Source URL: gamma.cs.unc.edu

Language: English - Date: 2014-02-14 21:55:05