TraxBot

Assembling and Programming of a Mobile Robotic Platform

André Araújo

, David Portugal

, Micael S. Couceiro

, Carlos M. Figueiredo

and Rui P. Rocha

Institute of Systems and Robotics (ISR),University of Coimbra (FCTUC), Coimbra, Portugal

RoboCorp, Department of Electrical Engineering (DEE), Engineering Institute of Coimbra (ISEC), Coimbra, Portugal

Keywords: Mobile robot, Embedded system, Design, Assembling and testing.

Abstract: This work presents the TraxBot mobile robot design, a ground platform recently developed for applications

in the mobile robotics field. The assembling of the robotic system, with description of its components as

well as information about the microcontroller programming, development and testing are presented. The

TraxBot is a multidisciplinary platform and is ideal for education, since it is easily programmed with open-

source tools requiring basic knowledge of other areas beyond robotics, like mechanics, control or energy

management. Although being released in a stable version, the robot is continually in development, with the

ability to incorporate extensions to its design and new features.

1 INTRODUCTION

In recent years, a great deal of research on mobile

robotics has been noticed. Several different robotic

systems have emerged in order to assist or replace

human operators mostly in tiring, repeating or time-

consuming tasks. Earlier, the focus of research was

especially on large and medium structures. Howev-

er, with the advancement in sensor miniaturizations

and the increasing in the speed and capability of

microcontrollers in the past years, the emphasis

shifted to the development of smaller, lower cost

robots and experimentation with groups of robots.

In this paper, we present the design and imple-

mentation of a portable ground robot developed in

the Mobile Robotics Laboratory (MRL) at the Insti-

tute of Systems and Robotics (ISR) in the University

of Coimbra. The TraxBot is an ideal platform for

education, since it can provide students with basics

required to develop autonomous mobile robots, both

at the hardware level (mechanics, energy, locomo-

tion, embedded electronics, sensors) and software

level (control theory, microcontroller programming,

trajectory planning, localization). The setting up,

development and programming of this robot was

motivated by experimentation and research in coop-

erative multi-robot systems, more specifically teams

of robots with distributed control to perform cooper-

ative patrolling (Portugal and Rocha, 2010) and

swarm foraging (Couceiro et al., 2011) tasks.

Figure 1: Mechanical structure of the TraxBot.

2 HARDWARE SPECIFICATION

The robotic platform in focus is a differential drive

system built upon the Traxster II Robot educational

Kit (Traxster II, 2008), equipped with 2 DC gear-

head motors with quadrature wheel encoders. Rub-

ber bands were attached to the original tracks to

increase friction and reduce slip during locomotion.

The processing unit consists of an Arduino Uno

board (Arduino Uno, 2010) equipped with a micro-

controller ATmega 328p from Atmel, which controls

the platform’s motion through the use of the Bot’n

Roll OMNI-3MD board (Bot’n roll, 2011).

301

Araújo A., Portugal D., S. Couceiro M., M. Figueiredo C. and P. Rocha R..

TraxBot - Assembling and Programming of a Mobile Robotic Platform.

DOI: 10.5220/0003713303010304

In Proceedings of the 4th International Conference on Agents and Artiﬁcial Intelligence (ICAART-2012), pages 301-304

ISBN: 978-989-8425-96-6

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

For range sensing, the robot uses Maxbotix So-

nars MB1300 with a maximum range of approx-

imately 6 meters (Maxbotix, 2005), which can have

a configurable disposition and the possibility of

employing up to 4 sonars in one platform using the

analog ports of the Arduino Uno board.

Additionally, to enable point-to-point communi-

cation, the Xbee Shield, consisting on a ZigBee

communication antenna attached on top of the Ar-

duino Uno board was also incorporated.

As for power source, two packs of 12V

2300mAh Ni-MH batteries are deployed under the

chassis of each robot to ensure good autonomy.

Finally, the platform has the ability to include a

10” netbook on top of an acrylic support, extending

the processing power and providing more flexibility.

The netbook has the advantage of enabling commu-

nication via WiFi 802.11 b/g/n. In Figure 1, a 3D

general view of the TraxBot assembly is presented.

Some hardware specifications are presented in

Table 1. The choice of the robot design was made

essentially due to the following reasons:

• Robustness: All hardware is either aluminum or

stainless steel;

• Low Cost: The platform costs around 300€ (not

considering the netbook);

• Operability: It has the ability to maneuver in

many different types of terrain and surface topo-

graphies;

• Dimension: It is adequate for both indoor and

outdoor experiments;

• Autonomy: It can operate continuously around 2-

3 hours.

• Flexibility: It can incorporate many new exten-

sions and components (e.g., LEDs, cameras, LI-

DARs, grippers, etc.).

• Hybrid design: It is able to work with and with-

out a small netbook on top of the platform ac-

cording to the user’s desire and applications.

Table 1: TraxBot Hardware Specifications.

Voltage Range [V] 9-14

Electric Current in Operation [mA] 1200

Electric Current in Standby [mA] 110

Maximum Speed [m/s] 0,95

Weight [g] 2045

Weight with netbook [g] 3160

Width [mm] 203

Length [mm] 229

Height [mm] with sonars 110

Height [mm] with netbook 155

Figure 2: Main Electronic Circuit.

3 DEVELOPMENT

An outline of the electronics of the TraxBot platform

is presented in Figure 2. The Arduino Uno board, the

Bot’n Roll OMNI-3MD driver and the DC motors

are located inside the robot shield, while the Maxbo-

tix MB1300 sonar range finders are placed on top of

the chassis. The battery packs are under the chassis

and the circuit switch is located in the shield’s rear.

The batteries provide the energy source to the en-

tire system. Having the Arduino Uno board as the

central component of the system, a sonar range find-

er connects to it through the A0 port, receiving ana-

log inputs from the sonar and reading voltage values.

As for the connection to the Bot’n Roll motor driver,

the ports A4 and A5 are used for Serial Data and

Serial Clock Connection respectively.

The other three ports (A1-A3) of the Arduino

Uno are available for integration of more sensors.

The USB jack on the microcontroller connects to the

netbook and is used to receive (RX) and transmit

(TX) TTL serial data, which is decoded using a

USB-to-TTL Serial chip in the microcontroller.

The motor driver is connected to the two DC

Gearhead Motors through the encoder connectors.

The Bot’n Roll OMNI-3MD motor driver has the

ability to control three motors in omnidirectional

platforms by sending linear velocity, direction and

speed commands, performing both velocity and

position control. Furthermore, it has the flexibility to

reset the parameters of PID control, reading encod-

ers, measuring the battery voltage and monitoring

the temperature of the board. The robot incorporates

two DC motors with 624 pulses per output shaft

revolution. Encoder information is read by the motor

drive and provided to the Arduino Uno.

The Arduino Uno is an open-source hardware

board based on the ATmega328 microcontroller,

which provides serial communication. Its CPU runs

at 16 MHz and provides 14 MIPS of peak processing

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A stable version of the Arduino code used to program the

TraxBot and videos of all experiments are available at:

http://paloma.isr.uc.pt/~aaraujo/TraxBot/

power. An ATmega8U2 on the board channels the

serial communication over USB and appears as a

virtual COM port to software on the computer.

The open source Arduino environment is a po-

werful tool for education, in specific, microcontrol-

ler programming. In the current implementation, the

robot has the ability to perform navigation com-

mands, plan paths from an origin to a destination,

perform self-localization based on its odometry and

avoid obstacles in a reactive way using the sonars.

4 EXPERIMENTS

In order to evaluate the proposed platform, several

tests were conducted on a lab scenario composed by

a green and plain carpet with no flaws with a top

mounted camera which recorded the experiments

4.1 Odometry

The TraxBot was placed in the carpet with the objec-

tive to perform a square trajectory with one-meter of

side length, in both clockwise (CW) and counter-

clockwise (CCW) directions. This test is done to test

its odometry, being extremely challenging due to the

fact that the robot always turns in the same direction,

and tending to accumulate dead reckoning errors

without compensation in the opposite direction.

Figure 3 illustrates the scenario and trajectories

performed by the platform during the experiments.

The trajectories illustrated were collected using an

overhead camera mounted on top of the scenario.

a) b)

Figure 3: a) Odometry square test in CCW direction. b)

Odometry square test in CW direction.

The tests were performed relying on the odome-

try system of the robot and without the assistance of

any sensor or exterior localization information. As it

is depicted in Figures 3.a) and 3.b), the robot per-

forms movements in straight line with high accura-

cy, however finds it difficult to rotate exactly 90° as

expected, with the error varying in different turns.

Besides, it is noticeable that, as it rotates around

a fixed point, a minor slipping effect is present.

Nevertheless, the accumulated error in the end of

each test, after one lap, is reduced. The trial in CCW

direction ends with a positional difference of 9.67cm

and an angle difference of -4.93° to the robot’s ini-

tial pose; while in the CW direction the positional

error is 7.71 cm and the angle difference is +2.13°.

4.2 Sensing Accuracy

In this test, a calibration phase was conducted to

convert the analog output values given by the sonar

readings to centimeters. By measuring sonar read-

ings in a straight line at a distance to a wall between

5 to 200 cm, with an increment of 5 cm; a curve

fitting power function f(x)= a·x

+c converted the

analog values data to centimeters.

In order to test the robustness of the calibration

function and the sensing equipment, a simple test

was conducted. As shown in Figure 4.a), the robot

was placed two meters away from a regular obstacle

and was driven in a straight line in its direction with

a constant velocity of 0.14m/s. The robot stopped

when it was 3 cm away from the obstacle. The sonar

data was saved during the experiment.

Figure 4: a) Experimental scenario. b) Range testing re-

sults.

As seen in Figure 4.b), sonar readings are very

close to the line of reference and can be used relia-

bly to assist the robot’s navigation. The average

sonar measuring error was 1.83 cm, while the max-

imum error was 5 cm. These few observable errors

have diverse sources, such as the limits imposed by

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303

the sonar resolution, approximation errors caused by

the calibration function, the manual measurement of

2 meters used in the experiment, and even the small

errors that accumulate when the robot diverts while

moving in a straight line.

4.3 Obstacle Avoidance

In the final test, an obstacle was added to the scena-

rio. Programmed to navigate in a straight line from

an initial configuration into an obstacle, the robot

reactively avoids it using three sonars mounted be-

low the acrylic support. After overcoming the ob-

stacle, the robot replans its trajectory and drives to a

final position. Figure 5 presents the trajectory of the

robot during the experiment.

Figure 5: Overview of the reactive obstacle avoidance test.

The red line denotes the robot trajectory in the experiment.

This test demonstrates that the robot is able to

avoid obstacles and navigate safely in the environ-

ment. It decides in which direction it should rotate,

while avoiding an obstacle through the composed

readings of the three sonars. Note that some posi-

tional errors still propagate during the test due to its

odometry system. Nevertheless, the robot is able to

drive itself autonomously.

5 CONCLUSIONS

This paper presents the development and experimen-

tal evaluation of a robotic platform named TraxBot.

The TraxBot is suitable for enhancing basic pro-

gramming skills, for exploring algorithms of interest

to the robotics community and will also be useful in

the fields of multi robot systems, since it is a cost-

efficient, off-the-shelf solution. Furthermore, it takes

advantage of the addition of computing power that a

laptop can offer, since it allows the capability to

extend its processing unit. Hence, the TraxBot offers

both a realization of a practical autonomous robot

and a novel resource that can be leveraged toward

educational and research goals.

In the near future, the ZigBee module will be

used to develop point-to-point communication with

a team of TraxBots in cooperative multi-robot tasks.

Moreover, in order to strengthen the robot’s naviga-

tion, we intend to use the overhead cameras on our

lab scenario for tracking and correcting robot’s posi-

tions eventually with the assistance of RGB LEDs

deployed on top of the robot. Finally, we intend to

release a TraxBot driver for ROS (Quigley et al.,

2009), a popular robotic integration framework used

in research laboratories and industry worldwide.

ACKNOWLEDGEMENTS

This work was supported by PhD scholarships

(

SFRH/BD/64426/2009) and (SFRH/BD/73382/2010) by

the Portuguese Foundation for Science and Technol-

ogy (FCT), the Institute of Systems and Robotics

(ISR) and RoboCorp at the Engineering Institute of

Coimbra (ISEC) also under regular funding by FCT.

The authors gratefully acknowledge Soluções de

Automação e Robótica (SAR) for their contribution

and feedback.

REFERENCES

Arduino Uno, 2010: Available at: http://arduino.cc/

en/Main/ArduinoBoardUno. Retrieved on 18

October

2011.

Bot’n roll, 2011: Manual OMNI-3MD. Available at: http://

botnroll.com/omni3md/downloads/Manual%20OMINI

3-MD(18-07-2011).pdf. Retrieved on 18

October

2011.

Couceiro, M., Ferreira, N., Rocha, R., 2011: Multi-Robot

Exploration based on Swarm Optimization Algo-

rithms. In ENOC’2011: Proc. of the 7th European

Nonlinear Dynamics Conf. Rome, Italy, 24-29 July.

Maxbotix, 2005: MB1300 XL-MaxSonar®-AE0™ Data-

sheet. Available at: http://www.maxbotix.com/docum

ents/MB1200-MB1300_Datasheet.pdf. Retrieved on

October 2011.

Portugal, D., Rocha, R., 2010. MSP Algorithm: Multi-

Robot Patrolling based on Territory Allocation using

Balanced Graph Partitioning. In SAC 2010: Proc. of

25th ACM Symposium on Applied Computing. Sierre,

Switzerland, March 22-26, pp. 1271-1276.

Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T.,

Leibs, J., Berger, E., Wheeler, R. and Ng, A., 2009:

ROS: an open-source Robot Operating System. In

ICRA’2009 Workshop on Open Source Software.

Traxster II, 2008: Traxster

II Assembly Manual, Version

1.0. Available at: http://www.roboticsconnection.

com/multimedia/docs/TraxsterAssemblyGuide.pdf.

Retrieved on 18

October 2011.

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