3d stochastic reconfiguration of modular robots paul j white viktor zykov josh bongard hod lipson...
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3D Stochastic Reconfiguration of Modular Robots
Paul J WhiteViktor ZykovJosh BongardHod Lipson
Cornell Universityhttp://ccsl.mae.cornell.edu
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Motivation: Adaptive Morphology Robotic adaptation in nature involves
changing/learning morphology, not just control Over robot lifetime (behavior) Over evolutionary time (design)
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Evolution of morphology & control
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Evolution of morphology & control
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Transfer to reality
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Some of our printed electromechanical / biological components: (a) elastic joint (b) zinc-air battery (c) metal-alloy wires, (d) IPMC actuator, (e) polymer field-effect transistor, (f) thermoplastic and elastomer parts, (g) cartilage cell-seeded implant in shape of sheep meniscus from CT scan.
Printed Active Materials
With Evan Malone
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Motivation: Adaptive MorphologyModular robotics Robotic adaptation in nature involves
changing/learning morphology Over robot lifetime and evolutionary time
Scaling number of units (1000’s) Greater morphological flexibility (space) Better economical advantage
Micro-scale No moving parts, no onboard energy Scalable fabrication, scalable physics
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Murata et al: Fracta, 1994
Murata et al, 2000
Jørgensen et al: ATRON, 2004
Støy et al: CONRO, 1999
A Dichotomy
Fukuda et al: CEBOT, 1988
Yim et al: PolyBot, 2000
Chiang and Chirikjian, 1993
Rus et al, 1998, 2001
Modular Robotics: high complexity, do not scale in size
Stochastic Systems: scale in size, limited complexity
Whitesides et al, 1998
Winfree et al, 1998
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Simulation
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Proposed Stochastic System
No independent means of power or locomotion
The units are passive, only draw power when attached to ‘growing’ structure
Modules are driven by (artificial and natural) Brownian motion
Structure reconfigures by manipulating local attraction/repulsion field near bonding sites
Passive motion is natural for small scale implementations
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Stochastic Self Reconfigurable Systems
White et al, 2004
Two Solid-state, 3D implementations
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Implementation 1
(b)
Spring loaded contacts for distributing power & communication
Embossed patterns on all faces ensure proper alignment
Power storage 0.28 F capacitor for switchable bonding
Basic Stamp II controller
Permanent magnets embedded inside of the cube walls
Electromagnet
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Experiment Environment Oil medium agitated by
Fluid flow by external pump Mechanical disruption of fluid
Substrate with attracting bonding site
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Implementation 1: Magnetic Bonding
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Implementation 1: Magnetic Bonding
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Beneficial System Properties Reconfigurable Programmable Homogeneous/simple units 3D modules: 6 d.o.f.
Permanent magnets create undesired bonds Electromagnets require local power storage Viscous medium requires high actuation power Electromagnetic bonding and actuation does not scale
System Disadvantages
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Proposed Scalable Solution
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Fluid FlowΔP
F = A ΔP
To external pump
Valves: allow for selectable bonding
Substrate
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Construction Sequence
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Construction Sequence
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Construction Sequence
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Construction Sequence
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Construction Sequence
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Construction Sequence
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
3D Structures
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
3D Structures
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Implementation 2Inside of the cube: Servo- actuated
valves Basic Stamp II
controller Central fluid
manifold Communication,
power transmission lines
Embossed fluid manifold
Hermaphroditic interface
Orifices for fluid flow
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Implementation 2: Fluidic Bonding
Movie accelerated x16
Computational Synthesis Labhttp://ccsl.mae.cornell.edu
Conclusion 3D stochastic modular robotic system
In two implementations More scalable to microscale
A substrate with interesting algorithmic challenges: the factors that govern the rate of assembly and reconfiguration the effects of larger quantities of modules on the system