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Simbeeotic: A Simulator and Testbed for Micro-AerialVehicle Swarm ExperimentsIPSN 2012Bryan Kate, Jason Waterman, Karthik Dantu

(Harvard University), Matt Welsh (Google)

Study group2012.04.23

Meng-Lin, Lu

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MAV Swarms Background

Features

Extremely small

Large number

Simulation

Modeling the key aspects: actuation, sensing and communication

Staged deploymentto prototype testbedSimbeeotic

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Outline

0Introduction0Simulator Design0Helicopter Testbed0Evaluation0Future Works0Conclusions

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Introduction

0Simulation0 Rapid prototyping0 Emulation of future architectures0 Testing at scale

0Differences between static and dynamic WSN0 Radio is not primary energy consumer0 Duty cycling can’t work when sensors fly or move

0Several system had purposed before, but not satisfied their requirements and design decisions

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Introduction

4 Contributions:0Scalability

0 Simulate thousands of MAVs in a single scenario0Completeness

0 Model as much of the problem domain as possible0Variable fidelity

0 Adjust for each purpose without losing accuracy0Staged development

0 Facilitate the development of software and hardware

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Simulator Design

Simbeeotic:0Discrete 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

0Written in Java programming language0 easily learned by neophytes0 large repository of high quality, open source libraries

0Repeatability0Ease of use

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Simulator Design

Architecture

Bottom

Top

Heart

Bottom

Top

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Simulator Design

Physics engine- JBullet0Rigid Bodies

0 Simple shapes, complex geometries0Dynamics Modeling

0 Integrating the forces and torques03D Continuous Collision Detection

0 Physical interactions between objects0Ray Tracing

0 Range finders and optical flow

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Simulator DesignMAV domain models0Virtual world0Weather0Sensors – inertial (accelerometer, gyroscope, optical

flow), navigation (position, compass), environmental (camera, range, bump)

0RF communicationSoftware engineering tricks0Reflection0Runtime annotation processing0Parameterization: key-value pairs

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Helicopter Testbed

0 Indoor MAV testbed0E-flite Blade mCX2 RC helicopter

0 Proprietary control board stabilizes flight (yaw axis only)0 Without other processors, sensors, or radios0 Not expensive, small V.S. toy

Remote control0Using Vicon motion capture system for remote control0 Input signal to the helicopter ‘s transmitter

0 yaw, pitch, roll, and throttle

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Aircraft Yaw Motion

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Aircraft Pitch Motion

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Aircraft Roll Motion

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Simbeeotic Integration

Ghost model

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HWIL Discussion

Advantages0Fly real vehicles using virtual sensors0Transform laboratory space into an arbitrarily Env.0Test the limits of proposed hardware and software Disadvantages:0 Inaccuracy cauesd by Vicon motion capture system0Can’t fly outdoors0Heavy computing resources0Can’t process or sense on helicopter0Latency: processing, transmission, control

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Evaluation

0Workload0 10Hz kinematic update rate0 1Hz compass sensor reading0 100 virtual seconds

0Environment Complexity

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Evaluation

0Swarm Size

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Evaluation0Model Complexity

0 Increase event execution time – event complexity, message explosion

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Example Scenarios 1

0Coverage0 search a space for features of interest (e.g. flowers)

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Example Scenarios 2

0Explores the possibility of using RF beacons

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Future Works

0Scalability0 Physics engine is a bottleneck0 JBullet -> Bullet : JAVA->C++

0Fidelity0 Improving networking models for communication0 Expand HWIL capabilities to include real radios

0Autonomy0 Leverage ROS to control0 TOSSIM-like approach to simulate0 Kinect

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Conclusions

0Provide a feasible way to simulate MAV swarms0Cool, and may be useful in simulation but seems

useless now in reality0Too complex to make whole system robust (network,

motion capture, robot control)

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Reference

Airplane controls0http

://www.grc.nasa.gov/WWW/k-12/airplane/short.html

0http://www.rc-airplane-world.com/rc-airplane-controls.html