Miniature Rotorcraft as Aerial Explorers

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
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Outline

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

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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

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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

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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.

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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

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Aerial Explorers Complement Rovers

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Planetary Explorer Missions

• Accompany rovers
• Atmospheric sampling
• Imaging / mapping
• Search
• Earth, Mars, Titan

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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 gt few large

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The Concept Challenges

• Insect-Scale Aerodynamics
• 3D Micro-Manufacturing
• Power / Control / Sensors

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Challenges Aerodynamics

• Insect-scale aerodynamics
• Highly viscous flow
• All-laminar
• Low L/D
• New design tools required

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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

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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

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Aerodynamics Section Optimization

• Nonlinear optimization coupled with Navier-Stokes
  simulation
• New very low Re airfoil designs
• Improved performance compared with previous
  designs

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Section Optimization

• Preliminary solution bears strong resemblance to
  dragonfly section (Newman 1977)
• Structural advantages to insect section

Optimized Solution
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Aerodynamics Section Flight Testing

• Micro sailplanes permit testing of section
  properties
• Difficulties with very low force measurements in
  wind tunnel avoided
• Optical tracking system

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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

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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
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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

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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

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Approach Prototypes

• Initial 3g device with external power,
  controllers
• Basic aero testing complete
• Issues SC, electronics miniaturization, power

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Approach Prototypes

• Capacitor powered mesicopter
• 5mm Smoovy
• Integrated electronics
• Shrouded frame

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Approach Prototypes

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

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Approach Prototypes

• PC-board system with digital communication and
  on-board microcontroller

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Mesicopter Development Prototypes

Flight video
Prototypes From 13g to 200g
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Mars Rotor Development

• Very low Re environment
• Tests in Mars atmosphere simulator at JPL

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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

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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

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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

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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

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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)

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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.

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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

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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)

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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

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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.

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Distributed Design Example

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

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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

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Distributed Design Improved

• Control History
• Collaborative, rule-based control law

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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.

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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

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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

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Acknowledgements

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