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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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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
0 5 10 15 20 25 30-50
-40
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-10
0
10
20
30
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Time (sec)
Vel
ocity
(rad
/sec
)
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
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