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

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

3. Outline 0 Introduction 0 Simulator Design 0 Helicopter Testbed 0 Evaluation 0 Future Works 0 Conclusions 3

4. Introduction 0 Simulation 0 Rapid prototyping 0 Emulation of future architectures 0 Testing at scale 0 Differences between static and dynamic WSN 0 Radio is not primary energy consumer 0 Duty cycling can’t work when sensors fly or move 0 Several system had purposed before, but not satisfied their requirements and design decisions 4

5. Introduction 4 Contributions: 0 Scalability 0 Simulate thousands of MAVs in a single scenario 0 Completeness 0 Model as much of the problem domain as possible 0 Variable fidelity 0 Adjust for each purpose without losing accuracy 0 Staged development 0 Facilitate the development of software and hardware 5

6. Simulator Design Simbeeotic: 0 Discrete event simulator 0 A simulation execution consists of one or more models that schedule events to occur at a future point in time 0 Virtual time – moved forward by an executive that get the next event and pass it to the intended recipent 0 Written in Java programming language 0 easily learned by neophytes 0 large repository of high quality, open source libraries 0 Repeatability 0 Ease of use 6

7. Simulator Design Architecture Bottom Top Heart Bottom Top 7

8. Simulator Design Physics engine- JBullet 0 Rigid Bodies 0 Simple shapes, complex geometries 0 Dynamics Modeling 0 Integrating the forces and torques 0 3D Continuous Collision Detection 0 Physical interactions between objects 0 Ray Tracing 0 Range finders and optical flow 8

9. Simulator Design MAV domain models 0 Virtual world 0 Weather 0 Sensors – inertial (accelerometer, gyroscope, optical flow), navigation (position, compass), environmental (camera, range, bump) 0 RF communication Software engineering tricks 0 Reflection 0 Runtime annotation processing 0 Parameterization: key-value pairs 9

10. Helicopter Testbed 0 Indoor MAV testbed 0 E-flite Blade mCX2 RC helicopter 0 Proprietary control board stabilizes flight (yaw axis only) 0 Without other processors, sensors, or radios 0 Not expensive, small V.S. toy Remote control 0 Using Vicon motion capture system for remote control 0 Input signal to the helicopter ‘s transmitter 0 yaw, pitch, roll, and throttle 10

11. Aircraft Yaw Motion 11

12. Aircraft Pitch Motion 12

13. Aircraft Roll Motion 13

14. Simbeeotic Integration 14 Ghost model

15. HWIL Discussion Advantages 0 Fly real vehicles using virtual sensors 0 Transform laboratory space into an arbitrarily Env. 0 Test the limits of proposed hardware and software Disadvantages: 0 Inaccuracy cauesd by Vicon motion capture system 0 Can’t fly outdoors 0 Heavy computing resources 0 Can’t process or sense on helicopter 0 Latency: processing, transmission, control 15

16. Evaluation 0 Workload 0 10Hz kinematic update rate 0 1Hz compass sensor reading 0 100 virtual seconds 0 Environment Complexity 16

17. Evaluation 0 Swarm Size 17

18. Evaluation 0 Model Complexity 0 Increase event execution time – event complexity, message explosion 18

19. Example Scenarios 1 0 Coverage 0 search a space for features of interest (e.g. flowers) 19

20. Example Scenarios 2 0 Explores the possibility of using RF beacons 20

21. Future Works 0 Scalability 0 Physics engine is a bottleneck 0 JBullet -> Bullet : JAVA->C++ 0 Fidelity 0 Improving networking models for communication 0 Expand HWIL capabilities to include real radios 0 Autonomy 0 Leverage ROS to control 0 TOSSIM-like approach to simulate 0 Kinect 21

22. Conclusions 0 Provide a feasible way to simulate MAV swarms 0 Cool, and may be useful in simulation but seems useless now in reality 0 Too complex to make whole system robust (network, motion capture, robot control) 22

23. Reference Airplane controls 0 http://www.grc.nasa.gov/WWW/k- 12/airplane/short.html http://www.grc.nasa.gov/WWW/k- 12/airplane/short.html 0 http://www.rc-airplane-world.com/rc-airplane- controls.html http://www.rc-airplane-world.com/rc-airplane- controls.html 23