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Miniature Rotorcraft as Aerial Explorers

Miniature Rotorcraft as Aerial Explorers. Ilan Kroo, Peter Kunz Dept. of Aero/Astro Stanford University. NASA/DoD Second Biomorphic Explorers Workshop JPL Dec. 5, 2000. Outline. Introduction Challenges Development approach Cooperative control issues Summary and future directions.

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Miniature Rotorcraft as Aerial Explorers

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  1. Miniature Rotorcraft as Aerial Explorers Ilan Kroo, Peter Kunz Dept. of Aero/Astro Stanford University NASA/DoD Second Biomorphic Explorers Workshop JPL Dec. 5, 2000

  2. Outline • Introduction • Challenges • Development approach • Cooperative control issues • Summary and future directions

  3. Introduction • Objectives: Examine feasibility of small autonomous rotorcraft Explore scaling issues and limits on feasible size Develop some of the required technologies • Bio-Inspired aspects: • Insect-scale aerodynamics • Testbed for cooperative control / swarm behavior

  4. The Concept: Meso-scale Flight • What is a meso-scale vehicle? • Larger than microscopic, smaller than conventional devices • Mesicopter is a cm-scale rotorcraft • Exploits favorable scaling • Unique applications with many low cost devices • Objectives • Is such a vehicle possible? • Develop design, fabrication methods • Improve understanding of flight at this scale

  5. The Concept: Rotorcraft • Why rotorcraft for meso-scale flight? • As Reynolds number and lift/drag decrease, direct lift becomes more efficient • Compact form factor, station-keeping options • More flexible take-off / landing • Direct 4-axis control • Scaling laws (and nature) suggest cm-scale flying devices possible.

  6. The Concept: Applications • Atmospheric Studies • Windshear, turbulence monitors • Biological/chemical hazard detection • Planetary Explorers • Swarms of low-mass mobile robots for unique data on Mars, Titan • Terrain-independent

  7. Aerial Explorers Complement Rovers

  8. Planetary Explorer Missions • Accompany rovers • Atmospheric sampling • Imaging / mapping • Search • Earth, Mars, Titan

  9. Features of Small Rotorcraft • Rotorcraft • Low ground speed • Operates in restricted areas • No runway requirement • Inefficient? • Small Vehicles • Favorable structural scaling • Lower cost (especially transport) • Many small > few large

  10. The Concept: Challenges • Insect-Scale Aerodynamics • 3D Micro-Manufacturing • Power / Control / Sensors

  11. Challenges: Aerodynamics • Insect-scale aerodynamics • Highly viscous flow • All-laminar • Low L/D • New design tools required

  12. Approach • Advanced aerodynamic analysis and design methods • Novel manufacturing approaches • Teaming with industry for power and control concepts • Stepwise approach using functional scale model tests

  13. Approach: Aerodynamics • Navier-Stokes analysis of rotor sections at unprecedented low Reynolds number • Novel results of interest to Mars airplane program • Nonlinear rotor analysis and optimization code

  14. Aerodynamics: Section Optimization • Nonlinear optimization coupled with Navier-Stokes simulation • New very low Re airfoil designs • Improved performance compared with previous designs

  15. Section Optimization • Preliminary solution bears strong resemblance to dragonfly section (Newman 1977) • Structural advantages to insect section Optimized Solution

  16. Aerodynamics: Section Flight Testing • Micro sailplanes permit testing of section properties • Difficulties with very low force measurements in wind tunnel avoided • Optical tracking system

  17. Aerodynamics: Rotor Optimization • Chord, twist, RPM, blade number designed using nonlinear optimization • 3D analysis based on Navier-Stokes section data • Rotor matched with measured motor performance

  18. Approach: Rotor Manufacturing 1. Micro-machine bottom surface of rotor on wax 2. Cast epoxy 3. Remove excess epoxy 5. Melt wax 4. Machine top surface of rotor

  19. Rotor Manufacturing: Materials and Methods • Wide range of rotor designs fabricated and tested • Scales from .75 cm to 20 cm • Materials include epoxy, polyurethanes, carbon

  20. Power and Control Systems: Sensors / Control Laws • Innovative passive stabilization under test at larger scale • Linear stability analysis suggests configuration features • MEMS-based gyros provide damping

  21. Approach: Prototypes • Initial 3g device with external power, controllers • Basic aero testing complete • Issues: S&C, electronics miniaturization, power

  22. Approach: Prototypes • Capacitor powered mesicopter • 5mm Smoovy • Integrated electronics • Shrouded frame

  23. Approach: Prototypes • Low cost unaugmented 60g system • Includes receiver, speed controllers, lithium batteries • Closed loop control using off-board vision

  24. Approach: Prototypes • PC-board system with digital communication and on-board microcontroller

  25. Mesicopter Development: Prototypes Flight video Prototypes From 13g to 200g

  26. Mars Rotor Development • Very low Re environment • Tests in Mars atmosphere simulator at JPL

  27. Mesicopters as Cooperative Control Testbeds • Ideal for studying collaborative control strategies (CO, COIN) • Multi-resolution mapping mission • Decentralized control and navigation • DoD / NASA applications • Real, 3D problem features

  28. Control Approaches • Self-organizing systems display ‘interesting’ emergent behavior. • Self-optimizing systems display ‘desired’ emergent behavior. • Approaches here employ nonlinear optimization, exploit recent progress in distributed design and large-scale MDO. • Focus on high-level control, planning

  29. Control Approaches • Centralized design • Behavior of each agent determined by system-level control law • Heuristic rules • Individual actions determined by global rules, local data • Reduced basis optimization • Rules used to reduce dimensionality of optimal design problem • Distributed design • Individuals seek local goals leading to desired system properties

  30. An Example Application • Simple example to illustrate approaches: Formation flight of geese • Goal is not just to maintain formation, but to optimize performance • Include aerodynamic interactions, test control concepts

  31. Formation Flight of Geese • Each bird leaves wake that influences others. Drag includes viscous, self-induced, interference. • Objective to is maximize the range of the group (minimize drag of least fortunate individual). • Control is individual speed • Consider coplanar formation (optimal)

  32. Centralized Design • Nonlinear optimization used directly to find best speed and position. • Works in steady case for limited size flock, good initial distributions. • Fails completely in other cases, scales poorly.

  33. Heuristic Rules • Assume V-Formation • Set Vi = V0 + k (xi – xi-1 Dx0) • Specify reasonable values of V0, k,Dx0 • Drag reduction is achieved • Requires little communication

  34. Reduced Basis Rule Design • Use rule to reduce design dimensionality • Optimize V0, k,Dx0 using nonlinear programming for steady state solution or Monte Carlo. • This works but: • Not robust. Individual parameters sensitive to uncertainties, disturbances • Not correct (sub-optimal)

  35. Distributed Design • Concept: • Let each individual seek ‘best’ local solution. • Choose objective definition and decomposition to produce system optimum. • Exploit previous work: • Collaborative optimization and MDO • Collective Intelligence concepts

  36. Distributed Design • Collaborative optimization (CO): • Multi-agent control problem analogous to large scale multidisciplinary design optimization problem. • CO is a multi-level decomposition and design strategy developed to solve this. • Collective intelligence (COIN): • Ideas under development in AI (NASA Ames) to help select local objectives. • Useful in traffic management, economics, network routing.

  37. Distributed Design Example • Greedy objective: every goose flies at speed that minimizes his or her drag with interference • Result: Tragedy of the Commons • Example

  38. Distributed Design Improved • Basic idea: • Modify local objectives to include effect on others. • Specific idea: • Vote on best speed to fly, then fly at Vi = ( k1 Vv + k2 Vi*) • k2/k1 determines self-interest or altruism

  39. Distributed Design Improved • Control History • Collaborative, rule-based control law

  40. Distributed Design • Result is a robust method that efficiently produces correct solutions with limited communications • Distributed design approaches allow “think globally, act locally” to work.

  41. Mesicopter Status • 5 self-powered prototypes at various scales • Largest (200g) can carry video, INS, digital FCS and fly for 15 min • Successful closed-loop hover demo using off-board vision • Work continues on FCS, simulation, optical flow stabilization, inter-vehicle communication

  42. Future Work • Near-term applications • Testbed for multi-agent, cooperative control • Earth-based tests • Longer term aspects • Mars rotorcraft • Alternate power source potential • Further miniaturization of electronics

  43. Acknowledgements • Work supported by: • NASA Institute for Advanced Concepts • JPL • Langley, Ames • Work undertaken by: • Profs. Prinz, Kroo • 5 Stanford Ph.D. students

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