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Simbeeotic : A Simulator and Testbed for Micro- Aerial Vehicle Swarm Experiments

Simbeeotic : A Simulator and Testbed for Micro- Aerial Vehicle Swarm Experiments. Study group 2012.04.23 Meng -Lin, Lu. IPSN 2012 Bryan Kate, Jason Waterman, Karthik Dantu (Harvard University), Matt Welsh (Google). MAV Swarms Background. Features. Simulation. Extremely small.

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Simbeeotic : A Simulator and Testbed for Micro- Aerial Vehicle Swarm Experiments

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  1. Simbeeotic: A Simulator and Testbed for Micro-AerialVehicle Swarm Experiments Study group 2012.04.23 Meng-Lin, Lu IPSN 2012 Bryan Kate, Jason Waterman, KarthikDantu (Harvard University), Matt Welsh (Google)

  2. MAV Swarms Background Features Simulation Extremely small Simbeeotic Staged deployment to prototype testbed Large number Modeling the key aspects: actuation, sensing and communication

  3. Outline • Introduction • Simulator Design • Helicopter Testbed • Evaluation • Future Works • Conclusions

  4. Introduction • Simulation • Rapid prototyping • Emulation of future architectures • Testing at scale • Differences between static and dynamic WSN • Radio is not primary energy consumer • Duty cycling can’t work when sensors fly or move • Several system had purposed before, but not satisfied their requirements and design decisions

  5. Introduction 4 Contributions: • Scalability • Simulate thousands of MAVs in a single scenario • Completeness • Model as much of the problem domain as possible • Variable fidelity • Adjust for each purpose without losing accuracy • Staged development • Facilitate the development of software and hardware

  6. Simulator Design Simbeeotic: • Discrete event simulator • A simulation execution consists of one or more models that schedule events to occur at a future point in time • Virtual time– moved forward by an executive that get the next event and pass it to the intended recipent • Written in Java programming language • easily learned by neophytes • large repository of high quality, open source libraries • Repeatability • Ease of use

  7. Simulator Design Architecture Top Bottom Heart Top Bottom

  8. Simulator Design Physics engine- JBullet • Rigid Bodies • Simple shapes, complex geometries • Dynamics Modeling • Integrating the forces and torques • 3D Continuous Collision Detection • Physical interactionsbetween objects • Ray Tracing • Range finders and optical flow

  9. Simulator Design MAV domain models • Virtual world • Weather • Sensors – inertial (accelerometer, gyroscope, optical flow), navigation (position, compass), environmental (camera, range, bump) • RF communication Software engineering tricks • Reflection • Runtime annotation processing • Parameterization: key-value pairs

  10. Helicopter Testbed • Indoor MAV testbed • E-flite Blade mCX2 RC helicopter • Proprietary control boardstabilizes flight(yaw axis only) • Without other processors, sensors, or radios • Not expensive, small V.S. toy Remote control • Using Vicon motion capture system for remote control • Input signal to the helicopter ‘s transmitter • yaw, pitch, roll, and throttle

  11. Aircraft Yaw Motion

  12. Aircraft Pitch Motion

  13. Aircraft Roll Motion

  14. Simbeeotic Integration Ghost model

  15. HWIL Discussion Advantages • Fly real vehicles using virtual sensors • Transform laboratory space into an arbitrarily Env. • Test the limits of proposed hardware and software Disadvantages: • Inaccuracy cauesd by Vicon motion capture system • Can’t fly outdoors • Heavy computing resources • Can’t process or sense on helicopter • Latency: processing, transmission, control

  16. Evaluation • Workload • 10Hz kinematic update rate • 1Hz compass sensor reading • 100 virtual seconds • Environment Complexity

  17. Evaluation • Swarm Size

  18. Evaluation • Model Complexity • Increase event execution time – event complexity, message explosion

  19. Example Scenarios 1 • Coverage • search a space for features of interest (e.g. flowers)

  20. Example Scenarios 2 • Explores the possibility of using RF beacons

  21. Future Works • Scalability • Physics engine is a bottleneck • JBullet-> Bullet : JAVA->C++ • Fidelity • Improving networking models for communication • Expand HWIL capabilities to include real radios • Autonomy • Leverage ROSto control • TOSSIM-like approach to simulate • Kinect

  22. Conclusions • Provide a feasible way to simulate MAV swarms • Cool, and may be useful in simulation but seems useless now in reality • Too complex to make whole system robust (network, motion capture, robot control)

  23. Reference Airplane controls • http://www.grc.nasa.gov/WWW/k-12/airplane/short.html • http://www.rc-airplane-world.com/rc-airplane-controls.html

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