3D Stochastic Reconfiguration of Modular Robots

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

2. Motivation: Adaptive Morphology • Robotic adaptation in nature involves changing/learning morphology, not just control • Over robot lifetime (behavior) • Over evolutionary time (design)

3. Evolution of morphology & control

4. Evolution of morphology & control

5. Transfer to reality

6. Printed Active Materials 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. With Evan Malone

7. 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

8. A Dichotomy Stochastic Systems: scale in size, limited complexity Modular Robotics:high complexity, do not scale in size • Fukuda et al: CEBOT, 1988 • Yim et al: PolyBot, 2000 • Chiang and Chirikjian, 1993 • Rus et al, 1998, 2001 • Murata et al: Fracta, 1994 • Murata et al, 2000 • Jørgensen et al: ATRON, 2004 • Støy et al: CONRO, 1999 • Whitesides et al, 1998 • Winfree et al, 1998

9. Simulation

10. 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. Stochastic Self Reconfigurable Systems • White et al, 2004 • Two Solid-state, 3D implementations

12. Implementation 1 Permanent magnets embedded inside of the cube walls Electromagnet Spring loaded contacts for distributing power & communication Embossed patterns on all faces ensure proper alignment Basic Stamp II controller Power storage 0.28 F capacitor for switchable bonding (b)

13. Experiment Environment • Oil medium agitated by • Fluid flow by external pump • Mechanical disruption of fluid • Substrate with attracting bonding site

14. Implementation 1: Magnetic Bonding

15. Implementation 1: Magnetic Bonding

16. Beneficial System Properties • Reconfigurable • Programmable • Homogeneous/simple units • 3D modules: 6 d.o.f. System Disadvantages • Permanent magnets create undesired bonds • Electromagnets require local power storage • Viscous medium requires high actuation power • Electromagnetic bonding and actuation does not scale

17. ` ` ` Proposed Scalable Solution F= A ΔP Fluid Flow ΔP ` ` Valves: allow for selectable bonding ` Substrate To external pump

18. Construction Sequence

19. Construction Sequence

20. Construction Sequence

21. Construction Sequence

22. Construction Sequence

23. Construction Sequence

24. 3D Structures

25. 3D Structures

26. Implementation 2 Embossed fluid manifold Hermaphroditic interface Inside of the cube: • Servo- actuated valves • Basic Stamp II controller • Central fluid manifold • Communication, power transmission lines Orifices for fluid flow

27. Implementation 2: Fluidic Bonding Movie accelerated x16

28. 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