Dissertation/Thesis Abstract

Physically-based sampling for motion planning
by Gayle, Thomas Russell, Ph.D., The University of North Carolina at Chapel Hill, 2010, 252; 3409956
Abstract (Summary)

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.

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.

Indexing (document details)
Advisor: Manocha, Dinesh
Commitee: Amato, Nancy, Foskey, Mark, Lin, Ming C., Xavier, Patrick G.
School: The University of North Carolina at Chapel Hill
Department: Computer Science
School Location: United States -- North Carolina
Source: DAI-B 71/08, Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Robotics, Computer science
Keywords: Articulated robots, Deformable robots, Motion planning, Multiagent
Publication Number: 3409956
ISBN: 978-1-124-07854-0
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