Student initiation project to image processing, with - Wseas.us

Student initiation project to image processing, with CMUCAM module

and “BoeBot” robot from Parallax inc.

Ph. DONDON- P.GRESELLE ENSEIRB, Av Dr A. Schweitzer 33405 Talence, France.

G. LEROYER, Let Cie, 6 rue de la paroisse, 78000 Versailles, France

[email protected]

Abstract: This paper first point out the necessity of permanent teaching evolutions and adaptation due to

student’s behaviour changes. We describe some major changes. The impacts on the quality and efficiency of

traditional pedagogy are then indicated. From these observations, we show that pedagogical adaptations must

be done, in particular with the new fashion thematic such as signal and image processing. As an example, we

present here a didactical concrete project for a first initiation to image processing and applications fields.

Finally, we discuss the advantages of our approach and give results we obtained through this pedagogical

process.

Key words : Image processing, Robot, Embedded system, Analogue circuits, Sensors, Pedagogy by project.

1. Introduction

1.1 ENSEIRB learning overview

1.1.1 Sciences teaching evolutions

Since a few years, we observe in our electronic and

informatics engineer school, a kind of increasing

gap between the student’s needs and what we gave

to them. A global disaffection for all theoretical

lessons appeared and our traditional pedagogical

reached its limits.

These tendencies we noticed in our school are

confirmed by French national statistics: there is a

global demotivation for the scientific curriculum.

Economical, commercial studies seem to be now

more attractive for this new generation of students.

In front of this situation, we have to find permanent

adjustments and adaptations: All the scientific,

electronic, and other engineering fields of the

ENSEIRB program will have to be reformatted.

Each one of us had to suggest improvement,

modification in each own field of competence.

1.1.2 Impact on teaching efficiency This situation generates a general loss in term of

efficiency. In particular, we can point out, in our

electronic department two specific problems:

- Till now, our generic “sensors and

actuator” course, was a full classroom traditional

teaching with ten sessions of one hour and a half

each. It looked like a tiresome enumeration of the

physical principles and descriptions of the sensors

perfectly classified. Since two years the student’s

satisfaction rate was so poor that we decided a total

modification of our strategy.

- In the same direction, our analogue

electronic Value Unit (UV representing around 60

hours) seems to be “old fashion” for the students,

even if some fundaments must always be taught.

And we observe a lost of interest for these courses

and a higher absenteeism rate than before.

1.1.4 Adaptation to the new deals

After discussions and comparisons [2] [3], we

started a few years ago, a teaching experience with

a “learning by project approach” in order to make

our teaching more attractive.

2. Concrete pedagogical application

2.1 Learning by project

If this concept is obviously not new, its

introduction in our scientific school is quite recent

(five or six years). The aim of this practical

approach is to restore the motivation and to develop

the curiosity of the students. Such approach allows

a soft approach to difficult theoretical courses

which are nowadays rejected by the students.

Proceedings of the 8th WSEAS International Conference on EDUCATION and EDUCATIONAL TECHNOLOGY

ISSN: 1790-5109 27 ISBN: 978-960-474-128-1

2.2 The “Boe bot” project introduction As an example of theses possible teaching

evolutions, we describe here a student project we

started two years ago. It introduces as simply as

possible “image processing concept” in a concrete

and attractive robotic application.

After many years of collaboration with Parallax inc

company [4] and its French retailer, the funny

“Boe Bot robot” (figure 1) has been chosen for this

purpose.

Based on a “new fashion” embedded system

concept, it allows a concrete approach to electronic

and sensors theory. For educational reasons the

initial processing board BS2 can easily be replaced

by a microcontroller PIC 16F876 board for

example, in order to program in assembler or C

language instead of the BASIC language from

Parallax.

3. Short description of the robot The “boe bot” robot can be seen as an embedded

system: It is an electronic autonomous system

dedicated to a precise task. Such system generally

consists of a processor, a set of sensors with their

conditioning circuits and actuators [9].

3.1 General description The “Boe Bot” robot consists mainly of:

-Four 1,25V battery cells

-A set of sensors,

-Two motors and wheels

-A Basic stamp processing board

- Mechanical parts

- A gripper system (optional)

Figure 1 : Boebot view (parallax inc)

3.2 The set of sensors

This boe bot is a perfect support to study the

“sensor world” and their conditioning electronic

circuits. In our small robot, a set of quite simple or

sophisticated sensors is available. The most

popular are shortly listed below.

3.2.1 Camera sensor The most interesting sensor in our project is the

CMUcam [8], [10] with its CCD sensor OV6620,

and in-board image processing circuit for target

tracking or avoidance.

3.2.2 Other sensors Sophisticated MEMS inclinometer, optional

“whiskers” for mechanical obstacle detection,

working like simple on/off switches, are available.

Other infrared sensor looking downwards can be

added for border line detection on the floor. At

least, a couple of photo resistor for light avoidance

or tracking can be plugged with some external

components on the board.

3.3 Processing board Depending of the use of the robot, the students can

work with the included basic stamp BS2 board or

can design their own microcontroller board. In this

case, a micro chip PIC 16F876 is often used and a

C language management program can be written.

3.4 Actuators There are two classical servo motors for left and

right wheels driving. A small modification allows a

full 360° rotation.

As the two servo are not coupled, the rotation speed

must be calibrated (mechanically and/or by

software) to insure a right trajectory when the

command signals are identical.

At least , an optional servo motor (located on the

back side of the robot) can be used to open and

close a “gripper” (cf Figure 1).

4. Working with the CMUCAM

module A view of the full assembled robot is given in

figure 2. The CMU CAM module (figure 4)

designed by the Carnegie Mellon University [8]

consists of :


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- An OV6620 single-chip cmos CIF color digital

camera,

- An image processing board with SX28 processor,

- An “In board” image processing software.

Figure 2: assembled robot

This CMU CAM module is able to self calibrate on

a background colour. Detection and localisation of

coloured objects into the field of view, is then

possible. As the CMU CAM is an embedded

system, it has to be lighter as possible: so, there is

no display panel or other extra functions.

4.1 Video format The OV6620 sensor has a maximum resolution of

352x288 pixels. This resolution corresponds to the

CIF format (Common Intermediate Format) which

is a popular format used in videoconferences for

example. This format is recommended by the video

standard H261.

In order to reduce the data flow, only the results of

detection with some estimator flags, are transmitted

to the BS2 Boe bot controller board. The

CMUCAM resolution is also limited to 80x143

pixels for faster image analysis.

4.2 Obstacle detection with CMUCAM

4.2.1 Object colour detection The principle of obstacle detection is based on the

colour difference between the background and the

object to be detected. The CMUCAM returns

information on the size and average colour of the

object.

4.2.2 Obstacle localisation strategy The camera field of view can be represented as a

indicated in figure 3. The X,Y of a point

coordinates are obtained with the given axis

orientation.

Figure 3: obstacle localization into the camera

field

4.2.3 Obstacle position estimation

Figure 4: obstacle position detection

Defining sub areas in the camera field, makes it

possible to know if the detected object is rather on

the left side or on the right side. While the apparent

size of the object allows “guessing” if the object is

close or far. (Cf. figure 4)

4.2.4 Relative « obstacle-robot » position

detection algorithm The robot must obviously know if it is moving

away or if it is approaching the obstacle for a safe

navigation. The analysis algorithm (programmed

by the students) works as follow:

When the camera detects the ground with a vertical

ordinate Y higher than 50, then the object is still far

and the robot goes on running forward (figure 4

cases “a)”). If the object is close, it prevents the

camera from seeing the close ground. The ground is

Obstacle

X X

Y Y

Far obstacle

Close obstacle


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seen above the object and the vertical ordinate

becomes lower than 50 (figure 4 cases “b)”).

Figure 4 : front obstacle detection logic

In all the other cases (figure 4 cases “c)” and “d”),

if the confidence flag is weak (i.e. that the colour

observed is different from that of the ground), the

robot moves backward (large obstacle or table

border for example)

4.2.3 Motion programming strategy example Many motion strategies can be programmed

depending on the final goal: target tracking,

obstacle avoidance, coloured balls collecting or line

following for example. The figure 5 shows the

classical example of “line on the ground” follower.

The programmed feed back algorithm is quite

simple: if the black line is centred, the robot goes

straight away. If the black line “deviates” to the left

(or right) , the controller must act on the right (or

left) servo motor, to correct the trajectory.

Figure 5 : black line follower

Programming such motion strategy represents for

the students, the writing of a few hundred lines of

PBASIC code.

5. Project flowchart Before introducing this exercise in the “normal”

pedagogical cycle, we first tested it with some

students during a 2 months training period GEII

IUT Bordeaux 1. We encountered first a major

problem: the use of CMUCAM is not well known

in France. Its implementation looks difficult and

many people was reticent to use it because the

absence of a hard technical documentation in

French. The first job was for us to write a well

detailed user manual (©2007) [8]. This makes it

possible to go further with our students.

Now, the project proceeds by group of 2 students

(second year study IUT GEII or ENSEIRB). It is

distributed over one six-month period with 10

practical lessons of 3 hours. The miles stones of the

project are :

- Student group constitution: (Initiation with the

team work)

- Practical demonstration: a Boebot in action is

shown to the students. They are invited “to play”

with it in order to improve their interest and to

develop their curiosity.

- Specifications definitions: The specifications are

defined all together in order to give some freedom

to the students: the motion strategy is decided

together.

- Project management initiation: Once the

specifications are defined, the job of each member

Y axis

Y axis

Y axis

Y axis


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of the team must be defined. By a short seminar on

project management, we help the students to make

their task repartition, project manager designation

and role attribution.

- Thematic bibliography: Showing the necessity of

collecting information before starting working, we

encourage the student to find documents, books,

and articles in our library. As examples of

bibliography we can mention the following topics:

- Using the CMUCAM module is the opportunity

to find UIT standards and other technical

documents about CIF video format and image

processing techniques [7].

- Using photo resistor sensors is the opportunity of

a technological manufacturing process description.

- Through the use of the MEMS accelerometer,

students have to make a “state of art” of all the

possible methods to measure tilt and acceleration.

- Sensors and conditioning circuit study: In a short

lessons, the Infrared and magnetic laws, the main

physical effects are explained. Each sensor used

during the project, is individually characterized and

the conditioning circuits are studied.

- Motion: a few lessons are dedicated to DC

motors, driver circuits, feed back theory, position

and speed control. Each servomotor used by the

students, is individually characterized (i.e

calibration, speed vs control signal, consumption).

(Cf. Figure 10, 11, 12, 13).

- Programming: the software is implemented by the

students into the processing board. (PBASIC,

assembler or C language)

- Practical test: once the whole system is correctly

manufactured, the behaviour of the sensors is

individually tested. Finally, a “under true

conditions test” is performed by verifying the

motion strategy on the playing area.

- Final report: In this complete report (design,

simulation, programming, manufacturing, test

procedure, results) we request the students to

reformulate what they understood during the

project. This enables us to be sure that the image

processing (and other) bases were assimilated.

- Oral report: each group must orally expose the

covered subject during the last meeting.

6. Validation tests and experiments

6.1 Electrical tests As indicated in the last paragraph, some

intermediate and final tests are performed during

the pedagogical process. As example we give in

figure 6, 7, 8, typical curves obtained during the

servo motors characterisation phases.

Figure 6: Servo speed characterisation versus pulse

width “t” (1,3ms < t < 1,7ms)

Figure 7: servo rotation speed characterisation vs

the PWM period signal T (10ms to 50ms)

The figure 8 shows the servo supply current during

different operating phases.

Figure 8: servo motor current consumption in

continuous rotation @ T=20ms

Zone A: starting peak current (300mA)

Zone B: consumption in normal conditions 100mA,

(after the starting peak current)

Zone C: stopping the rotation

6.2 Practical tests Final tests are performed with the robot rolling on a

“playing area”.

Discontinuous rotation

20ms : optimum point

Unwanted operating

area


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7. First pedagogical assessment - The funny aspects of the project (the robot is

moving and detecting) are a source of interest.

It is opportunity to teach some difficult fields of

electronic (in particular analogue electronic,

sensors and actuators) in a different pedagogical

way:

- The physical and mathematical complexity of the

electronic world is better received by the students

when there is a funny and motivating embedded

electronic project challenge.

- Some freedom into the design gives the

impression to the students to be more creative. It

acts like a creativity amplifier.

- Our « system » approach allows connecting

different fields of electronic (Analogue, digital,

sensors, micro programming, power motor

driving). It ensures a better comprehension, and

causes a global interest for the lessons.

- The project is also a time for a human experience,

a pleasant team work, and management

- This particular image processing project fits the

needs of our robotic student’s team and helps them

for the national and annual M6 TV robotic contest.

- At least, this project is used as demonstrator

during the French annual and traditional “day of

sciences”.

8. Results Even if it is always difficult to “measure” the

impact of a teaching strategy, this one seems to be

more attractive than before.

After two years of experimentation, we asked to

ours electronic department to make a opinion poll

or report, to get the comments of the students.

The last result shows that the satisfaction rate

raised from 45% up to 65%. Of course, we are far

from the perfection but what is important is that the

satisfaction rate increases.

In this report, the students point out the funny

aspect of the project, and also the system approach

which allows mixing the different field of

electronic. Even if the technical level of this

project is not very high, the most important for us is

to improve the global motivation, the interest for

the “oldest courses”, and also physical and mental

presence of ours students. In our example, the rate

of interested and “happier” students raised up to

80% and the absenteeism rate decreased

significantly.

Comparing to other experiences [2]; [3]; [5] done

in different French engineer schools, we see first

that many colleagues are now testing this kind of

approach. The same evolution in term of

motivation is observed, even if it will never

possible to obtained 100% of satisfaction rate.

9. Conclusion We showed in this paper the necessity of a

progressive adaptation of our teaching methods for

human and technical reasons. Replacing some

classical unpleasing lectures (analogue circuits,

sensors, image processing) by a “Learning by

project” approach using new fashion embedded

system thematic can be a good way (among others)

to improve the efficiency and the quality of our

teaching. Through a serious but funny robot

project, we showed that it was possible to improve

the behaviour, the motivation and the curiosity of

our students for the oldest and old fashion fields of

electronic. We are now trying to extend this

approach into other similar technical fields of our

electronic department.

References:

[1] DILTS Robert, GRINDER John. BANDLER

Richard, BANDLER L.C., DELOZIER Judith,

“neuro-linguistic programming, the study of

subjective experience” Meta Publications

Cupertino (California, USA) 1980

[2] M. AVILA, JC. BARDET, S. BEGOT, P.

VRIGNAT, N. STRIDE «La pédagogie par

projets » CETSIS Nancy 25-27 octobre 2005

France

[3] F. VINCENT, B.MOUTON, C.NOUALS,

« Radar de poursuite » CETSIS Toulouse 13-14

novembre 2003 France

[4] Parallax inc “Boebot manual”

http://www.parallax.com/index.asp

[5] CEPAGES project 2001, Test de l’Institut

Hermann France, 92500 Rueil Malmaison,

[6] Ph. DONDON “manuel d’utilisation

CMUCAM” Ph Dondon.-P.Greselle Enseirb ©

2007.

[7] Cheng-chuan chen, Ming-chih lu, Chin-tun

chuang, cheng-pei tsai “Vision-Based Distance and

Area Measurement System” WSEAS

TRANSACTIONS on SIGNAL PROCESSING

Issue 2, Volume 4, February 2008 ISSN: 1790-

5022

[8] Anthony ROWE “Boe-Bot CMUcam Manual

v1.1” Nov 2002 Carnegie Mellon University


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[9] P. Dassonvalle “ Les capteurs”, éd Dunod 2005

Paris ISBN 2100069977[10] Cheng-chuan chen,

Ming-chih lu, Chin-tun chuang, Cheng-pei tsai

“Vision-Based Distance and Area

Measurement System” WSEAS

TRANSACTIONS on SIGNAL

PROCESSING Issue 2, Volume 4, February

2008 ISSN: 1790-5022

[11] Ernesto Araujo, Cassiano R. Silva, Daniel

J. B. S. Sampaio “Video Target Tracking by

using Competitive Neural Networks” WSEAS

TRANSACTIONS on SIGNAL

PROCESSING Issue 8, Volume 4, August 2008

[12] Eugen Zaharescu “Redefining

Morphological Operators for Color Image

Contrast Enhancement and Segmentation”

WSEAS TRANSACTIONS on SIGNAL

PROCESSING, Issue 7, Volume 2, July 2006

[13] Ph. DONDON- J.M MICOULEAU- G.

LEROYER "An alternating practical pedagogical

approach for sensors and actuators teaching:

Learning through a multi thematic mini rolling

robot design project " WSEAS EDUTE 2006 10-13

juillet 2006 Athens (Greece)

[14] N. Y. A. SHAMMAS, S. EIO, D. CHAMUND

“Semiconductor Devices and their use in Power

Electronic Applications” WSEAS POWER'07 21-

23 Nov 2007 Venise (Italia)


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Student initiation project to image processing, with - Wseas.us