Transcript Dynamic Physical Rendering What if software could morph physical shapes in real time? • New ways to do medical visualization • New approaches to.

Dynamic Physical Rendering
What if software could morph physical shapes in
real time?
• New ways to do medical visualization
• New approaches to design
-Interact with CAD results
immediately
-Reshape a physical object
(like molding clay), see the
results flow back to CAD
-Better interpret 3D data (CAT, MRI)
-Rehearse procedures off-patient
-Control surgical robots
• Reconfigurable antennas
- Programmable antenna,
reflector shapes complement
software defined radio
• Shape-shifting handhelds
-Convenience
-Optimize user interface
for the task at hand
-Create new form-factors
with software alone
• Games, entertainment,
home movies
• “Be there” without being there
-Not like “videoconferencing-jail”
-Full interaction: doctors make
house calls, tennis instructors
demonstrate technique
How might such “programmable matter”
be constructed?
Each 3D shape reproduced is formed of millions of tiny
cooperating spherical modules
Moving 3D Capture
Moving 3D reproduction,
using millions of tiny spherical
modules
300 microns
one module
(“catom”)
Each module contains
• processing, networking, energy storage, etc.
• a means of actuation (locomotion and adhesion)
• outer surface of each module is a video display
Properties of programmable matter
• Not just the an illusion of 3D (as with stereo glasses), but
real, tangible physical objects
• Both an output device (rendering) and an input device
(haptics, sensing)
• Each robot is a “physical voxel”
Research at Intel
Copyright © 2006 Intel Corporation. All rights reserved.
Intel and the Intel logo are registered trademarks of Intel
Corporation or its subsidiaries in the United States and other countries.
*Other names and brands may be claimed as the property of others.
What fundamental computer systems
problems need to be addressed?
1. How do we cope with scale + dynamism on these
levels?
2. How can we create self-organizing infrastructure
for power, networking, physical support,
proprioception, debugging, etc.?
3. How should we arrange and operate myriad smallscale actuators such that the ensemble can achieve
large-scale forces and motions?
Current Research Efforts
• Power Routing
• Shape Formation
• Programming Languages for Emergent Behaviors
• Macro-Scale Motion Planning
• Collective Actuation and Dynamic Motion
• Hardware Prototypes
• Distributed Consensus and Control
• Debugging Tools for Massively Distributed Software
• Adaptive Hierarchies for Communication and Control
• Scalable Coordination
• Scalable Physics-based Simulation
Collaborative Research
Intel
Jason Campbell, PI
Phil Gibbons
Casey Helfrich
Todd Mowry
Lily Mummert
Padmanabhan Pillai
Siddhartha Srinivasa
Rahul Sukthankar
Student
Alumni
Current
CMU Students
Khalid El-Arini (CMU)
Greg Reshko (CMU)
Lauren Chikofsky (CMU)
Ashish Gupta *
(Northwestern)
Bancha Dhammarungruang *
(CMU)
Burak Aksak
Nels Beckman
Preethi Srinivas Bhat *
Mike De Rosa *
Daniel Dewey
Stanislav Funiak *
Emre Karagozler
Brian Kirby
Eugene Marinelli
Ram Ravichandran *
Ben Rister *
Michael Weller
Byung Woo Yoon
Elsewhere
Kasper Stoy
(Univ. Southern Denmark)
Mark Yim
(Univ. Pennsylvania)
Ashsih Deshpande *
(student, Univ. Michigan)
Carnegie Mellon
Seth Copen Goldstein, PI
Johnathan Aldrich
Gary Fedder
Carlos Guestrin
James Hoburg
James Kuffner
Peter Lee
Matt Mason
William Messner
Illah Nourbakhsh
Metin Sitti
Srini Srinivasan
Dan Stancil
Manuela Veloso
Funding
* Intel Research Pittsburgh Interns
Research at Intel
Copyright © 2006 Intel Corporation. All rights reserved.
Intel and the Intel logo are registered trademarks of Intel
Corporation or its subsidiaries in the United States and other countries.
*Other names and brands may be claimed as the property of others.
Intel
DARPA
National Science Foundation
Carnegie Mellon University
How can we render and actuate shapes using
millions of tiny, cooperating robots?
Stochastic, distributed shape formation with Hole Motion
• Create and delete “holes” to modify
surface contours
• Holes move like gas molecules
• Local rules automate hole
movement
• Shape planning complexity is
independent of number of modules
Movement
Expansion
Contraction
Exact, centralized shape planning with Hierarchies
Metamodule at Level 1
Metaconfiguration at
Level 1
Metamodule at Level 2
• Optimal planning scales
exponentially
• Instead, group modules into self
similar hierarchies of metamodules
• Recursively apply precomputed
templates to simplify planning
Metaconfiguration at
Level 2
Continuous, forceful shape transformation with
Collective Actuation
• Parallel movement of modules to effect
larger-scale motion
• Can create flexible, extensible structures
• Can collectively exert large forces against
external objects
• Continuous, smooth global shape changes
Research at Intel
Copyright © 2006 Intel Corporation. All rights reserved.
Intel and the Intel logo are registered trademarks of Intel
Corporation or its subsidiaries in the United States and other countries.
*Other names and brands may be claimed as the property of others.