Capstone spring 2009 preliminary design review
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Capstone Spring 2009 Preliminary Design Review. Cole Bendixen Electrical and Computer Engineering Erich Hanke Electrical Engineering. Erik Larson Electrical Engineering Quang Than Electrical Engineering. HAMSter. HAMSter Project Overview. Mobile Servo Powered Cart

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Capstone Spring 2009Preliminary Design Review

Cole Bendixen

Electrical and Computer Engineering

Erich Hanke

Electrical Engineering

Erik Larson

Electrical Engineering

Quang Than

Electrical Engineering

HAMSter


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HAMSter Project Overview

  • Mobile Servo Powered Cart

  • Stereo-Vision Obstacle Detection

  • Autonomous Navigation

  • FPGA Hardware / Software Control

  • IR Sensor “Failsafe” Collision Avoidance


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HAMSter Purpose

To create an autonomous platform that can be used as a mounting point for various sensors and monitors to test remote locations without human control.

To advance hardware stereo-vision algorithms.



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GPS

  • Used for long range directional control

  • Polled GPS to periodically calculate a destination vector

  • Hard coded destination


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RF Transceivers

  • Beacon at destination point

  • Medium/Short range destination proximity sensor

  • Overrides GPS when active

  • Implementation based on time

  • Could be achieved through stereo vision


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Stereo Vision

  • 2-NTSC cameras input 30fps image streams

  • Image processing detects obstacles

  • Use of parallax to determine distance


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IR

  • Provides short range obstacle detection

  • Highest sensor interrupt level – acts as failsafe to avoid collision


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Sensor Interrupt Levels

Highest Priority

IR Proximity Sensor

Polled

GPS

Frame Ready To Processes

RF Beacon Detector

Computer Stereo-Vision

Lowest Priority


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Code Flow Diagram

Software

Processing

Driver

Interface

Image

Processing

Wheel

Translation

Control

Hardware Filter

Servo Control

Board

Frame Grabber

Green = Hardware Implementation

Blue = Software Implementation

Yellow = Hardware/Software interface

Mobile Cart


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Stereo Vision

  • Based upon the phenomena of parallax, where an observation at two locations along a baseline of a common object appears to cause an offset of the object. This offset can be used to determine relative distance to the object.

Camera One

Camera Two

d1

d2


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Point Spread Function

  • Simple PSF is applied to the image to enhance edges.

  • Parameterized sweeper size and intensity.

  • Post convolution threshold filter applied.


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HDL IMAGE CO-PROCESSOR

PPC HARDCORE PROCESSOR

PPC REG

INTERFACE

CONTROL

REGISTERS

IMAGE PROCESSING

DATA

BUILDER

CONV

IMAGE

SRAM

CONTROL

DATA

BREAKER

DVI CONTROLLER

DVI

SIGNAL

GENERATOR

PLL

RAM BLOCK


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PowerPC 440

  • 1,100 DMIPS

  • Up to 550MHz

  • Out of Order Processing

  • Branch Prediction

  • 256MB DDR2 200MHz


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I/O Control

Interrupt Enabled Inputs

IR Sensors

Object Array stored in Block RAM

Polled Inputs

GPS via COM0

Transceiver

Outputs

Servo Control Board via COM1


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Path Determination

Initial direction of destination is determined using the GPS/transceiver data.

Objects are then realized in software.

Movement vector is determined based on direction and relative position of objects.

IR sensors are then used to update path model due to “invisible objects.”


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GPS Direction Determination

Given our current position (in the grid coordinate system) from the GPS, we calculate our north/south and east/west displacement from the destination position and use that to calculate a destination direction vector.

We then calculate the deviation of our current direction from the destination direction and rotate the robot the required amount to correct this difference.


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Transceiver Direction Determination

The transceiver will determine a direction and distance of the destination transceiver.

We then calculate the deviation of our current direction from the destination direction and rotate the robot the required amount to correct this difference, just as with GPS.

Transceiver is necessary because the GPS is only accurate to ~3m.


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Object Realization

To realize objects we will create a matrix representing the relative visible area in the direction of the destination.

Object input in the form of a distance and deflection will then be used to populate this matrix with objects.


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Movement Vector Determination

First we determine acceptable directions to travel based on object positions.

We then evaluate a vector in each valid direction space.

The length of movement vectors will place the robot just past the nearest object.

We then compare the valid vectors and choose the one that places the robot closest to the destination.

Instructions are then given to the servos to correct direction and move forward the determined distance.


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IR Recognition of 'Invisible' Objects

IR interrupts cause immediate motion halt.

Object is populated into matrix using IR sensor determined direction and distance.

Path is then re-evaluated using updated object matrix.

Motion continues.


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Power Systems

Battery 1

Battery 2

FPGA

Opto-Isolator

GPS

Servo-Control Board

ADC

Servo Motors

Camera

IR Sensor


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Goals

CDR

Robot that will move to a specified GPS location.

Milestone1

Object recognition hardware completed.

Milestone2

Robot that will move to a specified GPS location avoiding 'visible' objects.

Expo

Robot that will move to a destination object, via GPS and transceiver, and avoid objects using video imaging and IR sensors.





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RISKS

ADC interface to the FPGA – Loading images into memory

Wheels operating at same speeds

Inaccurate calculation of cart speed to judge distance traveled

Interfacing to the RF transceiver (signal magnitude not direction)

Cart frame parasitic to GPS or RF signals


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