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Physically-based sampling for motion...

Gayle, Thomas Russell.

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Physically-based sampling for motion planning.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Physically-based sampling for motion planning./
作者:	Gayle, Thomas Russell.
面頁冊數:	252 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-08, Section: B, page: 4920.
Contained By:	Dissertation Abstracts International71-08B.
標題:	Engineering, Robotics. -
電子資源:	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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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.
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