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

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

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

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

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.

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.

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:

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

8. Simulator Design: Architecture

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.

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

11. Simulator Design: Models

12. Simulator Design: Timer

13. Simulator Design: Physics Engine

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 interactionsbetween objects • Ray Tracing • Range finders and optical flow

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

16. Simulator Design: RF

17. 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

18. Simbeeotic Integration 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

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

21. Evaluation • Swarm Size

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

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

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

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)