The Visual Targeting TurretProgress Report

Jason LamJohn LeeJonathan Rothberg

Overview

ObjectivesRevised SpecificationsProgress: HardwareProgress: Image ProcessingProgress: System Modeling and ValidationSchedule Assessment

Objectives

Visually track a moving target in various lighting conditions

Infrared spectrumTarget can travel freely throughout a predefined area around the visual system

360° panShoot Target

Target has laser detection capability

Specifications

Car Velocity: 0.684788 m/s

Camera Height (From Floor):0.889 m

Motion range: Tilt: 30.256° to 55.521° (from the horizontal plane)Pan: 360°

Speed: Tilt: 25.7197°/sPan: 64.2595°/s

Accuracy: Maximum degree of deflection, pan = .4699 °Maximum degree of deflection, tilt = .223 °

Payload: worst case less than 0.5 kg (webcam + laser pointer + mounting device)

Noise Tolerance: Needing the accuracy to be as high as possible requires the amount of noise evident to be very low. Too much noise can dramatically affect the image processing, thereby reducing the systems ability to accurately track the object.

Specifications (cont.)Friction Identification (Tilt):

Viscous Friction = 0.0011 Nm*s/radCoulomb Friction = 0.1094 Nm

Friction Identification (Pan):Viscous Friction = 0.0011 Nm*s/radCoulomb Friction = 0.1343 Nm

Voltage to Torque Conversion, pan and tilt = 0.0699 Nm/VPan and Tilt Inertia, calculated:Tilt: Pan:

⎥⎥⎥

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⎣

⎡

0037.00034.00004.0

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⎦

⎤

⎢⎢⎢

⎣

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0080.00071.00013.0

Progress: Hardware

Laser Detection Circuit CompleteGain circuit obstacles

Op Amp replacementPower Supply

Progress: Hardware

Integrated Emitter and Detector CircuitsPattern

Interference

Progress: HardwareLaser and Camera Mount v2

ready for fabrication

Image Processing Objectives

Maximize frames per secondMinimize distance car travels between framesProvide fast processing of images

Robust and reliableAccurately find moving car 90% - 100% of the time

Image Processing – Original Approach

Used built in MATLAB functionsPros:

Easily availableAccomplished goal

Cons:Poor performanceNot easy to improve

Frames per second: 2Image processing time: 0.5 seconds

Image Processing – Improved Approach

Removed slow MATLAB function callsStreamlined algorithmFrames per second: 10Image processing time: 0.1 seconds on average

Image Processing Example

Car movies approximately 6 cm per frame.

Image Processing Example (‘cont)

Image Processing Example (‘cont)

Modeling and Verificationvia Friction Identification

LLcMLMLLMLLML mglNBNBBNBJNJCCVV

ττ −Θ+=Θ′++Θ′++Θ′′+ )sin(*)sgn()*(*)*(*)*( 2

MLLL NaaI τ=Θ′+Θ′+Θ′′ )sgn(*** 21

12 /))0699.0(( aaVNL −=Θ′

Modeling and Verificationvia Friction Identification

Modeling and Verificationvia Friction Identification

Modeling and Verificationvia Parameter Identification

Modeling and Verificationvia Parameter Identification

Modeling and Verificationvia Parameter Identification

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Time (sec)

Vel

ocity

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Open-Loop Response (Chirp) - Actual and Simulated

Simulated

Actual

Plan of Action

Camera and Mathematical Model IntegrationFabricate Mounting assemblyElaborate on control system testingOptimizing photodiode array (if necessary)Gradually increase complexity of motion

speedDirection of movementobstacles

Maximizing efficiency of image processing Achieve zero ambient light scenario