1. Simbeeotic: A Simulator and Testbed for Micro-Aerial Vehicle Swarm Experiments Bryan Kate, Jason Waterman, Karthik Dantu and Matt Welsh Presented By: Mostafa Uddin 1

2. Outline Introduction Simulator Design Helicopter Testbed Evaluation Future Works Conclusions 2

3. Introduction: What is MAV Micro-aerial vehicle (MAV) swarms are a group of autonomous micro robots to accomplish a common work. 3

4. Introduction: Challenges MAV is concerned with classic robotics challenges: obstacle avoidance, navigation, planning etc. MAV faces the challenges similar to static sensor network nodes: limited computation, energy scarcity and minimal sensing. Radio is no longer the primary energy sink- actuation needs more energy. Duty cycle is not an option for Hardware while flying. Treating Autonomous Mobility as a first class concern. 4

5. Introduction: Contribution New simulation environment and MAV testbed. Simbeeotic: A Simulator with following requirement: – Scalability: Simulate in large scale. – Completeness: Simulate as much of the problem domain. – Variable Fidelity: User can be focused on their own model. – Staged Development: Facilitate the development of software and hardware Deployment-time configuration. 5

6. Related Work: Swarms and MASON: opting for cell-based or 2D continuous world. Breve: Domain specific language limit the extension. Webots: Scalability issue Play-stage: First order geometric simulator. GRASP Micro UAV testbed: 6

7. 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 recipient Written in Java programming language – easily learned by neophytes – large repository of high quality, open source libraries Repeatability Ease of use 7

8. Simulator Design: Architecture 8

9. Simulation Engine Manages discrete event queue and dispatches events to model. Pushing the virtual time forward. Populates the virtual world from the configuration. Initializes all the models. Sim Engine is responsible for answering queries about model population and location. 9

10. Simulator Design: Models 10 Modelers introduce new functionality by building on layers with mostly matched interface.

11. Simulator Design: Models 11

12. Simulator Design: Timer 12

13. Simulator Design: Physics Engine 13

14. Simulator Design: Physics Engine Physics engine- JBullet Rigid Bodies – Simple shapes, complex geometries Dynamics Modeling – Integrating the forces and torques 3D Continuous Collision Detection – Physical interactions between objects Ray Tracing – Range finders and optical flow 14

15. MAV Domain Models 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 15

16. Simulator Design: RF 16

17. Helicopter Testbed Indoor MAV testbed E-flite Blade mCX2 RC helicopter – Proprietary control board stabilizes 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 17

18. Simbeeotic Integration 18 Hybrid Experiment with simulated and real MAVs.

19. 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 19

20. Evaluation Workload – 10Hz kinematic update rate – 1Hz compass sensor reading – 100 virtual seconds Environment Complexity 20

21. Evaluation Swarm Size 21

22. Evaluation Model Complexity – Increase event execution time – event complexity, message explosion 22

23. Example Scenarios 1 Coverage – search a space for features of interest (e.g. flowers) 23

24. Example Scenarios 2 Explores the possibility of using RF beacons 24

25. 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) 25

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