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

Bryan Kate, Jason Waterman, Karthik Dantu and Matt Welsh

Presented By: Mostafa Uddin

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Outline

• Introduction• Simulator Design• Helicopter Testbed• Evaluation• Future Works• Conclusions

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Introduction: What is MAV

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

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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.

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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.

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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:

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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

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Simulator Design: Architecture

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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.

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Simulator Design: Models

Modelers introduce new functionality by building on layers with mostly matched interface.

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Simulator Design: Models

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Simulator Design: Timer

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Simulator Design: Physics Engine

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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

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MAV Domain Models

MAV domain models• Virtual world• Weather• Sensors – inertial (accelerometer, gyroscope, optical

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

• RF communicationSoftware engineering tricks• Reflection• Runtime annotation processing• Parameterization: key-value pairs

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Simulator Design: RF

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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

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Simbeeotic Integration

Hybrid Experiment with simulated and real MAVs.

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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

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Evaluation

• Workload– 10Hz kinematic update rate– 1Hz compass sensor reading– 100 virtual seconds

• Environment Complexity

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Evaluation

• Swarm Size

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Evaluation

• Model Complexity– Increase event execution time – event

complexity, message explosion

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Example Scenarios 1

• Coverage– search a space for features of interest (e.g.

flowers)

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Example Scenarios 2

• Explores the possibility of using RF beacons

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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)

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