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SYS735 Intelligent Control Systems KaCC

2 Hand-Eye Tracker NN Sim Exp Ass 1 13-Jan-01

SIMULATION EXPERIMENT #NN1

Application of Neural Network to Emulate Your Human Eye-Hand Coordination

for Tracking a Target

Issued: 23 Jan 06 Due: 2 Feb 06

Goal. The goal is to train a FF ANN that duplicates your own eye-hand action in tracking a target. You will conduct

tracking experiments using a PC-based Matlab environment that simulates the dynamics of a motorized camera

which reveals an IR image of a target on its monitor as shown in Figure 1. Then you will train an ANN to replace

you as the tracker, as shown in Figure 2.

Premise. Thoughwe are able to track the target but cant describe what exactly goes on in our thought while we

were tracking with our eye-hand coordination. So we will let an ANN emulate the tracking pattern.

Basic programs. The basic Matlab/Simulink programs for this lab are found at www.oakland.edu/~cheok.

PC-based Simulation/Animation

Human-in-the-loopEyes

Decision

Hand

Visual Animation

Target tracking motion

Dynamics System Simulation

Dynamics of motorized IR camera

You & your neurons

Input Device

Mouse

Output Device

Monitor

Figure 1. Eye-Hand Tracking Coordination Simulation Experiment

Visual Animation

Target tracking motion

Dynamics System Simulation

Dynamics of motorized IR camera

FF Artificial Neural Network

Emulation of Tracking Skill

Figure 2. ANN Tracking Coordination Simulation Experiment

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7/31/2019 Hand-Eye Tracker NN Sim Exp Ass

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SYS735 Intelligent Control Systems KaCC

2 Hand-Eye Tracker NN Sim Exp Ass 2 13-Jan-01

Task 1. Collect Tracking Pattern. You would run the simulation (Figure 1) as explained in class lectures and

collect the input-output pattern of the eye-hand coordination. Set up the characteristics of the target movement such

that it moves slowly over the screen (so you can track it). Conduct tracking experiments and collect the data & u .

Task 2. Proportional-type Controller Premise. In this premise, we assume that the tracking control action is

somewhat proportional to the errors between the target and camera (crosshairs). So set out to train the ANN inFigure 3 with the error data as inputs. Report on the ANN structure and its trained parameters.

Task 3. Run the tracking simulation with the P-type trained ANN as shown in Figure 2. Capture the performance

and comment on what you see and your expectation.

Task 4. Next increased the speed of target motion, repeat tracking experiments with the P-type ANN. Capture the

performance and comment on what you see and your expectation. Explain the change in performance you observe.

Task 5. Proportional & Derivative-type Controller Premise. In this premise, we assume that the tracking control

action is somewhat proportional to the errors between the target and camera (crosshairs), and also to the rate of the

target-camera errors. So set out to train the ANN in Figure 3 with the error data and error differences as inputs.

Report on the ANN structure and its trained parameters.

Task 6. Run the tracking simulation with the PD-type trained ANN as shown in Figure 2. Capture the performance

and comment on what you see and your expectation. Explain the change in performance you observe.

Task 7. Next increased the speed of target motion, repeat tracking experiments with the P-type ANN. Capture the

performance and comment on what you see and your expectation. Explain the change in performance you observe.

Task 8. Submit a report with details for the tasks. Provide comments and insights.

Suggest improvements

More exciting target motion

Longer delay for the D in PD

Model the delay factor in eye-mind-hand

Artificial Neural Networks

( )

( )

2 2 2 2 1 2

1 1 1 1 1 1

s s= = +

= = +

u f W y b

y f s s W b

x

y

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Figure 3. FF ANN for Proportional-type Controller

Artificial Neural Networks

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1 1 1 1 1 1

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y f s s W b

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( ) ( 1)

( ) ( 1)x x

y y

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Figure 3. FF ANN for Proportional & Derivative -type Controller

The purpose of computing [simulation] is INSIGHT, not [just] numbersHamming