LUNARLunar Unmanned Navigation and Acquisition Robot

SECON I

Senior Design I

Final Design Review

November 29, 2007

Team 1

Dr. Bryan Jones,

Advisor

Ted

CopelandBryan Reese

Theresa Weisenberger

Jeffrey

Lorens

Block Detection X X

Path Detection X X

Object Avoidance X X

Communication X X

Outline

Competition Overview Practical Constraints Technical Constraints Models Testing Spring Semester Goals

Competition: Summary

Lunar mineral harvesting robot

Color-coded blocks with RFID tags

Collect maximum of four blocks and bring them back to home base

Final rounds head-to-head

Competition: CourtHome Bases

• Red/Blue/White Blocks

X Black Blocks

Pea Gravel

Sand

Paint

6 ft

6 ft

•Symmetrical Block Placement

•IR Beacons (2.5kHz and 4 kHz) on Home Bases

•Note: Grid will not be on the field during competition

X

Competition: Approach

Outline

Competition Overview Practical Constraints

Manufacturability Sustainability

Technical Constraints Testing Spring Semester Goals

Practical Constraints

Type Name Description

Manufacturability Modularity The robot must be designed as a set of subsystems that can be replaced independent of other subsystems.

Sustainability Dependability The robot must be sturdy enough to withstand repeated use.

Modularity

Team One Block Detection Path Planning Object Avoidance Home Base Detection

Team Two Locomotion Block Retrieval Block Storage

Modularity-Team 1 Subsystems

Environmental Sensing IR Distance Sensors Limit Switches

Vision Block Detection IR Sensor CMUCam3

Sustainability

Robot must be able to run full round (6 min) without repair.

Rugged enough to sustain normal wear. Only minor maintenance between rounds. Easily changeable battery

Sustainability

Battery Life: 5 rounds on 1 charge Performs consistently after multiple tests Normally no maintenance between rounds Battery slips into sleeve

Outline

Competition Overview Practical Constraints Technical Constraints Models Testing Spring Semester Goals

Technical Constraints

Name Description

Block Detection The robot must be able to detect and distinguish among red, blue, black, and white blocks.

Path Planning The robot must find a path to a target block while avoiding any obstacles.

Block Detection

Block Detection IR distance sensor Requests color identification from camera

Color Differentiation Prioritize block pick up Minimize the time spent collecting blocks

Path Planning

Center Line Detection Black block Reference point

Block Location Home Base Detection

Outline

Competition Overview Practical Constraints Technical Constraints Models Testing Spring Semester Goals

Physical Model

Camera Block Detection Sensor

Environmental Distance Sensors

Collision Detection Sensors

Collision Detection Sensors

Physical Model

Environmental Distance Sensors

Block Detection Sensor

Collision Detection Sensors

Wall DetectionIR Distance Sensors

Limit Switches (4)

Environmental Sensing Subsystem Vision Subsystem

Distance to wall

Distance to wall

Distance to wall

Distance to wall

CMUCam3

RS-232 bidirectional serial

SP

I

bid

irectio

na

l seria

l

Block Detecting

IR Distance Sensors

Block Present

Block Color

PIC18F4420

Microcontroller

Blo

ck

Co

llec

ted

Team 2 Microcontroller

Driv

e C

om

ma

nd

s

Front

Information Model

Back

Right Side

Left Side

Outline

Competition Overview Practical Constraints Technical Constraints Models Testing Spring Semester Goals

Testing-Block Detection

Camera returns mean color value of block PIC determines block color Tested at each of three possible locations Subsystems Tested

CMUCam3 RS-232 Serial

Communication

Block Detection Results

Color Identified by Vision Subsystem

Block Color Location A Location B Location C

Blue Blue Blue Blue

White White White White

Red Red Red Red

Testing-Path Planning

Robot starts at home base

Measures center-line detection accuracy

Subsystems Tested IR Distance Sensors SPI Communication Analog-to-Digital

Converter

x

x

x

x

x

Results-Path Planning

TrialDistance from

Center (inches)Percent Error

1 0 0%

2 0.625 1.74%

3 1 2.78%

4 0.25 0.69%

5 0.5 1.39%

Outline

Competition Overview Practical Constraints Technical Constraints Models Testing Spring Semester Goals

Spring Semester Goals

More precise environmental sensing Camera integration Enhanced object avoidance system Playoff round capability

References

Huntsville IEEE Section. "SoutheastCon 2008 Hardware Competition Rules: Return to the Moon," IEEE SoutheastCon 2008. 2007. Available: http://ewh.ieee.org/reg/3/secon/08/competition.html

Questions?