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Physically-based sampling for motion...

Gayle, Thomas Russell.

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Physically-based sampling for motion planning.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Physically-based sampling for motion planning./
Author:	Gayle, Thomas Russell.
Description:	252 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-08, Section: B, page: 4920.
Contained By:	Dissertation Abstracts International71-08B.
Subject:	Engineering, Robotics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3409956
ISBN:	9781124078540

Physically-based sampling for motion planning.
Gayle, Thomas Russell.

Physically-based sampling for motion planning. - 252 p.

Source: Dissertation Abstracts International, Volume: 71-08, Section: B, page: 4920.

Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2010.

Motion planning is a fundamental problem with applications in a wide variety of areas including robotics, computer graphics, animation, virtual prototyping, medical simulations, industrial simulations, and traffic planning. Despite being an active area of research for nearly four decades, prior motion planning algorithms are unable to provide adequate solutions that satisfy the constraints that arise in these applications. We present a novel approach based on physics-based sampling for motion planning that can compute collision-free paths while also satisfying many physical constraints. Our planning algorithms use constrained simulation to generate samples which are biased in the direction of the final goal positions of the agent or agents. The underlying simulation core implicitly incorporates kinematics and dynamics of the robot or agent as constraints or as part of the motion model itself. Thus, the resulting motion is smooth and physically-plausible for both single robot and multi-robot planning.

ISBN: 9781124078540Subjects--Topical Terms:

1018454
Engineering, Robotics.

Physically-based sampling for motion planning.
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008 130515s2010 ||||||||||||||||| ||eng d
020 $a 9781124078540
035 $a (UMI)AAI3409956
035 $a AAI3409956
040 $a UMI $c UMI
100 1 $a Gayle, Thomas Russell. $3 1678227
245 1 0 $a Physically-based sampling for motion planning.
300 $a 252 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-08, Section: B, page: 4920.
500 $a Adviser: Dinesh Manocha.
502 $a Thesis (Ph.D.)--The University of North Carolina at Chapel Hill, 2010.
520 $a Motion planning is a fundamental problem with applications in a wide variety of areas including robotics, computer graphics, animation, virtual prototyping, medical simulations, industrial simulations, and traffic planning. Despite being an active area of research for nearly four decades, prior motion planning algorithms are unable to provide adequate solutions that satisfy the constraints that arise in these applications. We present a novel approach based on physics-based sampling for motion planning that can compute collision-free paths while also satisfying many physical constraints. Our planning algorithms use constrained simulation to generate samples which are biased in the direction of the final goal positions of the agent or agents. The underlying simulation core implicitly incorporates kinematics and dynamics of the robot or agent as constraints or as part of the motion model itself. Thus, the resulting motion is smooth and physically-plausible for both single robot and multi-robot planning.
520 $a We apply our approach to planning of deformable soft-body agents via the use of graphics hardware accelerated interference queries. We highlight the approach with a case study on pre-operative planning for liver chemoembolization. Next, we apply it to the case of highly articulated serial chains. Through dynamic dimensionality reduction and optimized collision response, we can successfully plan the motion of "snake-like" robots in a practical amount of time despite the high number of degrees of freedom in the problem. Finally, we show the use of the approach for a large number of bodies in dynamic environments. By applying our approach to both global and local interactions between agents, we can successfully plan for thousands of simple robots in real-world scenarios. We demonstrate their application to large crowd simulations.
590 $a School code: 0153.
650 4 $a Engineering, Robotics. $3 1018454
650 4 $a Computer Science. $3 626642
690 $a 0771
690 $a 0984
710 2 $a The University of North Carolina at Chapel Hill. $b Computer Science. $3 1020590
773 0 $t Dissertation Abstracts International $g 71-08B.
790 1 0 $a Manocha, Dinesh, $e advisor
790 1 0 $a Lin, Ming C. $e committee member
790 1 0 $a Xavier, Patrick G. $e committee member
790 1 0 $a Foskey, Mark $e committee member
790 1 0 $a Amato, Nancy $e committee member
790 $a 0153
791 $a Ph.D.
792 $a 2010
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3409956