A Vision-Guided Autonomous Vehicle

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IEEE TRANSACTIONS ON EDUCATION, VOL. 40, NO. 4, NOVEMBER 1997 253

A Vision-Guided Autonomous Vehicle:An Alternative Micromouse Competition

Ning Chen

Abstract— A persistent problem facing today’s engineeringeducators is how to promote students’ interest in science andengineering. One of the best approaches to this challenge isto sponsor a technological competition that combines publicity,technology, and student participation. A current leading com-petition called “ micromouse,” although earning high marks onpublicity and technology, has had difficulty attracting large-scalestudent participation. In this paper a vision-guided autonomousvehicle project developed at California State University, Fullerton(CSUF) is proposed as an alternative. The idea is to fit anordinary radio-controlled toy car with a charge-coupled device(CCD) camera and a transmitter. Image processing and controlof the car are accomplished by a personal computer (PC). The

road image captured by the camera is transmitted to a PC via atransmitter–receiver pair at 900 MHz. A frame grabber boarddigitizes the image, and an image-processing program subse-quently analyzes the road condition and generates adequate drivecommands that are transmitted back to the vehicle via the built-in radio controller. Student teams write programs to competein racing or maze solving. In this paper detailed hardware andsoftware designs of the project are presented. The merit of theproject with respect to the criteria of publicity, technology, andstudent participation is also addressed.

Index Terms— Autonomous vehicles, student competition,visopn-guided vehicles.

I. INTRODUCTION

THE micromouse competition is one of the major student

competitions that stimulates interests of students majoring

in electrical engineering, computer science, and computer

engineering. Nevertheless, this competition has not been pop-

ular enough to become a significant inter-school activity. For

example, IEEE Region Six holds a micromouse competition

annually and among the six to seven engineering schools that

attended the meeting regularly, there is usually only one or two

schools that have managed to produce fully functional mice.

The major reason for this low success rate is not due to a

lack of interest, instead, it is the technical and nontechnical

difficulties associated with the micromouse project. We at

CSUF have tried to establish the micromouse program forseveral years [1] but only have achieved modest results.

By learning from the weaknesses of the current micromouse

competition, we propose a vision-guided autonomous vehicle

project as a viable alternative.

The project consists of the following hardware: a radio-

controlled (RC) toy car, a CCD camera, a transmitter–receiver

Manuscript received August 26, 1996; revised September 2, 1997.The author is with the Department of Computer Science and Department of

Electrical Engineering, California State University, Fullerton CA 92634 USA.Publisher Item Identifier S 0018-9359(97)08366-0.

pair, and a video frame grabber. The vehicle is an off-the-

shelf RC toy car mounted with a miniature CCD camera and

a 900-MHz transmitter. A receiver connected to a PC receives

the image and generates an RS-170 video signal. The video

signal is digitized by a frame grabber built at CSUF and is

discussed in detail later. The digitized image is then processed

by a program written in the C language. The software performs

pattern recognition and generates drive commands. The hand-held controller that comes with the RC car is modified so that

it can be controlled by the PC directly. The drive command is

sent to the vehicle by the hand-held controller at a frequency

at 27 MHz. The vehicle runs on a white floor with black dashed lines as tracks. Tracks can be arranged as a single loop

for racing or as multiple branches for maze solving. Student

teams write programs that drive the vehicle. A large-screen

TV also receives the image in real time. The audience can

view the actual image on TV as seen by the vehicle while

also commanding a birds-eye view of the competition arena

from the audience stand.

Section II analyzes the weaknesses of the micromouse

competition. The hardware of the proposed vision-guided

autonomous vehicle is presented in Section III. Section IV

covers the software responsible for pattern recognition and

command generation. Section V discusses the advantages of

the proposed project for promotion and recruiting purpose interms of publicity, technology, and student participation. The

conclusion is presented in Section VI.

II. CURRENT MICROMOUSE COMPETITION

A. General Description

The current micromouse competition is conducted on a maze

as specified in Fig. 1. A typical micromouse built to maneuver

the maze is shown in Fig. 2.

The first difficulty is the maze itself. The specifications

require a large wood floor of 3 m 3 m that is expensive

and difficult to build. At CSUF we tried to build one, butafter spending $1700, it still did not fully comply with the

specifications. To utilize the maze is another problem. A

special room needs to be set aside permanently for the maze.

Most of the testing of the micromouse requires the maze and

it is virtually impossible for students to test their vehicles at

home.

The construction of the micromouse also presents difficulty.

The main reason is that the maze square is very small. No off-

the-shelf toy cars with steering mechanism can fit in the square.

As a result, students need to build a precision mechanism that

0018–9359/9710.00 © 1997 IEEE

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254 IEEE TRANSACTIONS ON EDUCATION, VOL. 40, NO. 4, NOVEMBER 1997

Fig. 1. Micromouse maze specification.

Fig. 2. A micromouse example.

is beyond the ability of most electrical engineering or computer

science students. The cost of such a precision mechanism is

also very high.

The on-board computer typically is a single board computer

built from scratch using the wire-wrap prototyping technique.

Microcontrollers used include the 68HC11 and 80C188EB.

To increase reliability and to reduce power consumption,

micromouse builders usually try to reduce the number of

components used. As a result, the on-board computer is usuallybarely able to handle the computation. The software program

is written from scratch and stored in EPROM’s. There is no

floppy or hard drive and the on-board computer does not

provide any programming environment making debugging an

extremely time-consuming process [1].

The sensor system is made of eight or more reflective

infrared sensors. These discrete sensors can only take in

limited amount of information.

B. Concerns

The major weakness of the micromouse competition is that

it is very difficult to achieve large-scale student participation

without a strong support from the faculty. Furthermore, high

school students and college freshmen who show significantinterests cannot build their own micromice without having to

take two to three years of engineering courses first. Our ex-

perience on micromouse project at CSUF gives the following

observations.

1) The maze specifications are not reasonable.

2) Professional machine-shop service is required to build

the mechanical portion of the micromouse.

3) Building and programming an embedded real-time sys-

tem is interesting. However, it can quickly become

a nightmare if expensive in-circuit-emulator, software-

developing tools and other equipment are not available.

4) It is very difficult to sponsor high school micromouse

teams. The value of the competition for the purpose of

recruitment is not high.

III. THE PROPOSED VISION-GUIDED

AUTONOMOUS VEHICLE COMPETITION

A. General DescriptionWe propose a new competition that takes advantages of the

following.

1) The use of CCD cameras, frame grabbers, and related

products that are becoming widespread due to the ex-

plosion of the multimedia market. These products are

inexpensive and readily available.

2) The fact that many students own powerful computers

with excellent programming environment and many of

whom possess excellent programming skills.

A vision-guided autonomous vehicle consists of a CCD

camera and a transmitter mounted on an RC car while a

receiver and a frame grabber are connected to a PC. Acompetition arena that consists of a white floor with black

dashed line serving as tracks hosts the game. The participants

run their software program on a PC. The program processes

the image seen by the vehicle and issues drive commands back

to the vehicle. Participants’ merit is judged by how fast the

vehicle finishes the loop or by how intelligently the vehicle

solves a maze. Major components of the project are discussed

in detail below:

B. Vehicle

The vehicle used is a popular radio-controlled toy car called

“little R/C buggy.” This toy car has a good steering mechanism

and has a changeable gear train. During testing and debugging,

the low gear feature is very handy. With three C batteries, it

can run almost 6 h without recharging. This also makes the

testing less painful.

C. Camera

We used an inexpensive black and white CCD camera with

a built-in wide angle lens costing about $130. It outputs a

standard RS-170 video signal and accepts a dc input from 8

to 14 V.

D. Transmitter/Receiver Pair

The transmitter/receiver pair is an off-the-shelf product

costing about $100 per pair. It can deliver quality video signal

at 900 MHz with a range of 125 ft. Fig. 3 shows a picture

of the vehicle fitted with a CCD camera, a transmitter, and a

battery pack.

E. Frequency Demodulator

The off-the-shelf receiver outputs a signal intended for TV

channel 4 which needs to be demodulated back to the RS-

170 video signal. Instead of buying or building a frequency

demodulator, we simply run the receiver output to a VCR. The




CHEN: A VISION-GUIDED AUTONOMOUS VEHICLE 255

Fig. 3. A radio-controlled toy car fitted with CCD camera, transmitter, andbattery pack.

Fig. 4. A track setup.

VCR’s VHF input takes in TV channel 4 signal and produces

an RS-170 video signal on its VIDEO OUT connector.

F. Video Frame Grabber

At CSUF we built a low-cost video frame grabber from

scratch. The description of the CSUF frame grabber controller

can be found in Appendix I.

G. Interface to the Hand-Held Controller

All RC toy cars are equipped with hand-held controller that

issues drive commands at 27 or 49 MHz. All control buttons on

the hand-held controller are on/off type mechanical switches.Using n-p-n transistors (e.g., 2N3904) we easily constructed

electronic switches that accepted commands from the PC.

H. Race Track/Maze

The race track/maze on which the vehicle operates consists

of a white base dotted with black dashed lines as the track.

Fig. 4 shows an example of a possible setup.

The white base can be easily constructed by taping white

poster sheets on the floor. The black dashed lines are made of

nonreflecting black paper cut into 1 cm by 2 cm rectangular

blocks. One can arrange the dashed black lines into a race

Fig. 5. A competition arrangement.

Fig. 6. An actual track image seen by the vehicle.

track or a maze. The cost and time for the construction is

minimal. Fig. 5 shows an arrangement of the competition. By

attaching an additional receiver to a TV, the audience can seethe actual image seen by the vehicle.

IV. SOFTWARE

We tested the vision-guided vehicle on a white floor with a

black dashed line. The vehicle is supposed to follow the dashed

line. The software used to achieve this task is written in C. The

first part of the software displays the incoming road image onthe PC monitor continuously. This was done by writing pixels

into VGA’s video memory directly [4]. Although the VGA

can display 256 colors at one time, the maximum number of

gray levels that can be shown is only 64. Fig. 6 shows an

actual road image seen by the vehicle.Note that the upper portion in Fig. 6 shows the image of

the surrounding objects. This is possible when the vehicle

travels near the edge of the white floor. The image processing

task begins with a reduction of the pixels from 512 512

to 256 256. Experiments showed this reduction did not

degrade the information significantly. Conversion of the image

to binary representation is the second step. By selecting a

proper threshold level, each pixel becomes either completely

white or completely black. The storage of the image can then

be achieved with a 256 32-byte array. This is done using

1 b, instead of 1 byte, to represent one pixel. Handling the





image by each pixel is not feasible for two reasons: one is

the effect of noise and the other is the speed of processing.

We applied the spatial digitization [5] with an 8 8-pixel

cell. All 64 pixels within the space covered by the cell are

examined. If the majority of them are black then the cell is

labeled as black and similarly for the white cells. The threshold

level was selected experimentally. Now the road image can be

represented by 32 32 cells with the following data structure:

struct cell

unsigned char color;

unsigned int group;

;

struct cell image cell [31] [31];

The first field represents the color of the cell, either black

or white. Note that each black block is part of a dashed line

in Fig. 6 and consists of clusters of black cells. The secondfield is intended for the following use. Once the road image

has been reduced to 32 32 records, we need to answer three

questions: How many clusters of black cells are there (i.e.,how many black blocks)? Where are the clusters’ coordinates?

What are the clusters’ shapes? After trying traditional edge-

detection techniques without much success, we proposed an

algorithm that solves this problem in real time. The description

of the proposed algorithm is given in Appendix II.

A. Driving

Once the coordinates of the blocks are determined, we can

plan a trajectory. The starting point of the trajectory is the

nearest black dashed line to the front end of the vehicle. By

examining the slope of the trajectory a drive decision can be

made. There are four basic drive decisions: forward, backward,

left turn, and right turn.

V. ADVANTAGES OF THE PROPOSED PROJECT

We believe that the proposed vision-guided vehicle project

will achieve large-scale participation and can be used as an

effective recruiting tool for the following reasons.

1) The track/maze can be easily constructed.

2) All hardware, vehicle, CCD camera, receiver, and trans-

mitter are commonly available at a reasonable price. Al-

though we built the frame grabber at CSUF, commodity-

priced frame grabbers are showing up in stores for

multimedia applications.3) The level of challenge on software can vary to suit

the student. On the low end, entry level students with

one semester programming experience can produce a

working program without too much difficulty. On the

high end, a championship program can require extensive

knowledge ranging from target recognition, artificial

intelligence, to fuzzy logic.

4) There are many young people whose hobbies are com-

puter games and RC controlled cars. Channeling those

hobbies into real education may become popular among

students, parents, and educators.

Fig. 7. Frame grabber block diagram.

VI. CONCLUSION

In this paper a modified micromouse project is proposed.

The current micromouse competition has never achieved a

reasonable participation rate among engineering schools. The

major reasons are: 1) unreasonable maze specifications; 2) high

engineering cost, and 3) inadequate computation environment.

The proposed vision-guided autonomous vehicle consists of

five major parts: a low-cost radio-controlled toy car, a CCD

camera, a receiver/transmitter, a video frame grabber, and apersonal computer. The vision-guided vehicle is controlled

by a program run on the PC to follow a track or to solve

a maze. The proposed project has the following advantages:

1) low-cost track/maze construction; 2) reasonable engineering

cost with the use of the off-the-shelf components; 3) adequate

computation environment by using PC and PC programming

tools; and 4) adequate challenges that satisfy all levels of

students.

APPENDIX I

The frame grabber’s top level block diagram is shown in

Fig. 7.A low-cost high-speed analog-to-digital converter (HI1175

from Harris; about $7) is used to digitize the analog video

signal. A pin-to-pin compatible part is also available from

Texas Instruments. The converter HI1175 has 8-b resolution

and samples at 20 MHz. The video memory buffer consists

of two 128-Kbytes static random-access memory (SRAM)

CKX581000 from Sony (about $8 each). The memory buffer

is arranged as 128 K by 16 b to implement word transfers

between the frame grabber and the PC. A video sync separator

(LM 1881 from National Semiconductor, about $2) is used to

generate composite sync output, vertical sync output, burst

output, and odd/even output. The LM1881 data sheet [2]

offers a very good explanation on the RS-170 signal. Anapplication note from Harris Semiconductor [3] is also quite

helpful. The 74LS688 comparator implements a user-select

I/O base address. The control of all signals are done by

a field-programmable gate array (FPGA) (XC3030A from

Xilinx, about $10). The FPGA approach greatly reduces thecomplexity of wiring and is highly recommended. There are

two major circuits inside the FPGA. The first part handles

the data exchange between the frame grabber and the PC’s

ISA bus. There is a control register with its bit initializing

the read mode and bit switching between the read mode

and capture mode. When operating in the read mode a one-




CHEN: A VISION-GUIDED AUTONOMOUS VEHICLE 257

word register is used to buffer data transferred from the frame

grabber memory to the PC’s memory. This is necessary to

ensure that when the PC is reading one word the next word

is clocked into the buffer. The second circuit is an address

generator. In the read mode a read signal from the PC generates

a delayed pulse that advances the address generator to the next

address. By doing so, one frame of image can be transferred

to the PC memory by reading the same I/O address 131 072

times. In the capture mode, the address generator is driven by a

free-running crystal oscillator for the first 512 counts. Counts

above 512 are driven by the horizontal sync signal coming

from LM1881.

Using this approach any mis-synchronization will be

restricted to within one video line instead of propagating

throughout the whole image. The vertical sync signal is used

to reinitialize the address counter. The even/odd frame signaldirects the data flow to the even memory bank or to the odd

memory bank. The CSUF-built frame grabber shown in Fig. 8

was used to produce all the pictures in this paper.

APPENDIX IIThe group1 function scans through each image cell. If the

cell is black and still has the original initialized group number,

we then assign the next available group number to this image

cell and perform a roundup operation.

void group1(void)

int row, col;

for (t=0; t 50; t++)

gsummary[t].q=0;

gsummary[t].rowsum=0;

gsummary[t].colsum=0;

for (row=0; row 31; row++)

for (col=0; col 31; col++)

if (imacell[row][col].status

&& imacell[row][col].group 0)

gnumber++;

roundup(row,col);

/* end of group1 */

The function roundup recruits all neighboring black cells

that are directly or indirectly (through other neighbors) con-

nected to the starting image cell. During the rounding up

process, the number of cells and their averaged column along

with the row coordinates of the same group are computed. The

following is the listing of the function roundup

/* This is a recursive call */

void roundup(int row, int col)

Fig. 8. The frame grabber built at CSUF.

gsummary[gnumber].q ++;

gsummary[gnumber].rowsum

= gsummary[gnumber].rowsum + row;

gsummary[gnumber].colsum

= gsummary[gnumber].colsum + col;

gsummary[gnumber].rowave

= gsummary[gnumber].rowsum/gsummary[gnumber].q;gsummary[gnumber].colave = gsummary

[gnumber].colsum/gsummary[gnumber].q;

/* check left */

if (col 1)

if (imacell[row][col-1].color==1

&& imacell[row][col-1].group == 0)

imacell[row][col-1].group=gnumber;

roundup(row, col-1);

/* check right */ if (col 30)

if (imacell[row][col+1].color==1

&& imcell[row][col+1].group == 0)

imacell[row][col+1].group=gnumber;

roundup(row, col+1);

/* check up */

if (row 1)

if (imacell[row-1][col].color==1&& imacell[row-1][col].group == 0)

imacell[row-1][col].group=gnumber;

roundup(row-1, col);

/* check down */

if (row 30)

if (imacell[row+1][col].color==1

&& imacell[row+1][col].group == 0)





imacell[row+1][col].group=gnumber;

roundup(row+1, col);

/* end of roundup */

Running the above algorithm yields the following answers:

a) The number of groups (clusters).

b) The size of each group.

c) The center of gravity of each group.

Although the shape of each group is not determined, further

processing is possible. For example, computing the standard

deviation of the column and row may yield a good estimate

of the cluster. A rigorous check of all coordinates of the cells

within each group can definitely yield the shape of the group.

The size and the shape of each group can then be used to weed

out false blocks such as the objects that appear on the upper

corners of Fig. 6.

REFERENCES

[1] N. Chen, H. Chung, and Y. K. Kwon, “Integration of micromouse

project with undergraduate curriculum: A large-scale student partici-

pation approach,” IEEE Trans. Educ., vol. 38, pp. 136–144, May 1995.[2] “LM1881 video sync separator,” Application Note, National Semicon-

ductor.[3] P. Louzon, “Circuit considerations in imaging applications,” Application

Note AN9313.1, Harris Semiconductor.[4] M. Abrash, Zen of Graphics Programming. Coriolis Group Books,

1995.[5] M. C. Fairhurst, Computer Vision for Robotic Systems: An Introduction.

Englewood Cliffs, NJ: Prentice-Hall, 1988.

Ning Chen graduated from the National Cheng-Kung University, Tainan,Taiwan, in 1978 with the B.S. degree in hydraulics engineering and receivedthe M.S. and Ph.D. degrees, both in electrical engineering, from the ColoradoState University, Fort Collins, in 1984 and 1986, respectively.

After a postdoctoral appointment at the University of Illinois at Urbana-Champaign, he joined the faculty at California State University, Fullerton,in 1987. He is currently Associate Professor in the Department of ComputerScience and in the Department of Electrical Engineering at the CaliforniaState University, Fullerton. His major research interests are in the fields of robotics, real-time embedded systems, and artificial intelligence.

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A Vision-Guided Autonomous Vehicle