1. 3D Stochastic Reconfiguration of Modular Robots Paul J White Viktor Zykov Josh Bongard Hod Lipson Cornell University http://ccsl.mae.cornell.edu

2. Computational Synthesis Lab http://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)

3. Computational Synthesis Lab http://ccsl.mae.cornell.edu Evolution of morphology & control

4. Computational Synthesis Lab http://ccsl.mae.cornell.edu Evolution of morphology & control

5. Computational Synthesis Lab http://ccsl.mae.cornell.edu Transfer to reality

6. Computational Synthesis Lab http://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

7. Computational Synthesis Lab http://ccsl.mae.cornell.edu Motivation: Adaptive Morphology Modular 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

8. Computational Synthesis Lab http://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

9. Computational Synthesis Lab http://ccsl.mae.cornell.edu Simulation

10. Computational Synthesis Lab http://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

11. Computational Synthesis Lab http://ccsl.mae.cornell.edu Stochastic Self Reconfigurable Systems  White et al, 2004  Two Solid-state, 3D implementations

12. Computational Synthesis Lab http://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

13. Computational Synthesis Lab http://ccsl.mae.cornell.edu Experiment Environment Oil medium agitated by  Fluid flow by external pump  Mechanical disruption of fluid Substrate with attracting bonding site

14. Computational Synthesis Lab http://ccsl.mae.cornell.edu Implementation 1: Magnetic Bonding

15. Computational Synthesis Lab http://ccsl.mae.cornell.edu Implementation 1: Magnetic Bonding

16. Computational Synthesis Lab http://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

17. Computational Synthesis Lab http://ccsl.mae.cornell.edu Proposed Scalable Solution ``` ` ` ` Fluid Flow ΔPΔP F = A ΔP To external pump Valves: allow for selectable bonding Substrate

18. Computational Synthesis Lab http://ccsl.mae.cornell.edu Construction Sequence

19. Computational Synthesis Lab http://ccsl.mae.cornell.edu Construction Sequence

20. Computational Synthesis Lab http://ccsl.mae.cornell.edu Construction Sequence

21. Computational Synthesis Lab http://ccsl.mae.cornell.edu Construction Sequence

22. Computational Synthesis Lab http://ccsl.mae.cornell.edu Construction Sequence

23. Computational Synthesis Lab http://ccsl.mae.cornell.edu Construction Sequence

24. Computational Synthesis Lab http://ccsl.mae.cornell.edu 3D Structures

25. Computational Synthesis Lab http://ccsl.mae.cornell.edu 3D Structures

26. Computational Synthesis Lab http://ccsl.mae.cornell.edu Implementation 2 Inside 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

27. Computational Synthesis Lab http://ccsl.mae.cornell.edu Implementation 2: Fluidic Bonding Movie accelerated x16

28. Computational Synthesis Lab http://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