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School: University of North Carolina - Chapel Hill Year in Fellowship: 4 Practicum: Sandia National Laboratories (2006)
Field of Study: Computer Graphics
Contact:
rgayle@cs.unc.edu
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Summary of Research Over the past few years, graphics hardware (GPU) has improved in performance at a rate greater than that of the general CPUs. However, much of this computing power has gone unused. My research has been looking at various areas in which this hardware can be used to accelerate and solve difficult problems. Currently, I have been examining applications to physical simulation and its use for a class of problems related to robot motion planning. These problems provide an interesting blend of situations where geometric, kinematics, dynamics, and motion constraints are necessary and must satisfied simultaneously. Thus far, we have been able to provide practical solutions to problems that are usually considered intractable due to their complexity. We hope to generalize our solutions to a wider class of problems, generalizing a scalable framework for solving the problems, and finally show the usefulness of GPUs, or other highly parallelizable hardware, in solving complex problems related to physical simulation. Publications
Efficient Motion Planning of Highly Articulated Chains Using Physics-based Sampling
Lazy Reconfiguration Forests (LRF): An Approach for Motion Planning
with Multiple Tasks in Dynamic Environments
Reactive Deforming Roadmaps: Motion Planning of Multiple Robots in Dynamic Environments
Real-time Navigation of Independent Agents Using Adaptive Roadmaps
Surface Distace Maps
Adaptive Dynamics with Efficient Contact Handling for Articulated Robots
Fast Proximity Computation Among Deformable Models using Discrete Voronoi Diagrams
Interactive 3D Distance Field Computation using Linear Factorization
Constraint-Based Motion Planning of Deformable Robots
Interactive Collision Detection between Deformable Models using Chromatic Decomposition
Path Planning for Deformable Robots in Complex Environments
Quick VDR: Out-of-Core View-Dependent Rendering of Gigantic Models
Accelerating Line of Sight Computation Using Graphics Processing Units
Quick-VDR: Interactive View-Dependent Rendering of Massive Models |
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