PATH FOLLOWING IN UNKNOWN ENVIRONMENT FOR A

CAR-LIKE MOBILE ROBOT

Niramon Ruangpayoongsak and Hubert Roth

Institute of Automatic Control Engineering, University of Siegen, Hoelderinstr. 3, D-57068 Siegen, Germany

Keywords: Path following, Mobile robot, Obstacle avoidance, Trajectory generation.

Abstract: The path following is the automatic control of the mobile robot along the specified path without human

interference. The proposed path following applies for the robot navigation in unknown environments, where

the robot has no preliminary information about obstacles. This paper presents an innovative idea for the path

following control that is to integrate the basic path following control with the obstacle avoidance and the

trajectory generation. The robot performs the basic path following control with obstacle detection using on

ultrasonic and infrared sensors. The obstacle avoidance is developed by wall following technique and the

fuzzy logic controller. The trajectory generation is to generate the fittest trajectory to the desired final

position and heading. These algorithms base on the car manoeuvring characteristics.

1 INTRODUCTION

The path following control for mobile robots is the

automatic control of robot along the specified path

without human interference. The path following

control in unknown environment requires intelligent

navigation and localization. That is the integration of

the basic path following control, the obstacle

avoidance, and the trajectory generation.

The basic path following control is the path

following control under the assumption that no

obstacle exists during the operation. The robot

moves along the specified path and stops at the

destination without obstacle detection. In unknown

environment, where the obstacle positions are not

priori known, the basic path following control

method with the obstacle avoidance.

The obstacle avoidance is to detect the obstacle

positions, to avoid collision into obstacles, and to

overcome obstacles into free space. For a small

mobile robot, the compact size and the light weight

sensors are suitable. Several sensors exploited on

mobile robots are discussed in (Nehmzow, 2003). As

a part of the path following control, the obstacle

avoidance algorithm decides the orientation of the

robot for the next move by considering not only the

obstacle position, but also the robot current position

and the desired final position.

When that robot is free from obstacles and is

no longer on the original path, the robot has to

approach to the desired final position. The trajectory

generation provides the fittest trajectory between the

current robot position and the desired final position

using on the car manoeuvring characteristics.

This paper is organized as follows. Section 2

describes the mobile robot, section 3 explains the

path following control with obstacle avoidance,

section 4 presents the experimental results and

section 5 is the conclusion.

2 MOBILE ROBOT

A series of mobile robots MERLIN has been

designed and developed (Kuhle et. al., 2004, Roth et.

al., 2003). For a broad spectrum of indoor and

outdoor tasks on basis of standardized functional

modules like sensors, actuators, wireless

communication are implemented. Sensors onboard

are

• a gyroscope for angular velocity

measurement

• hall sensors as odometer

• ultrasonic and infrared sensors for obstacle

detection

• bumpers for crash detection

• a 3D magnetic compass for absolute roll,

pitch and yaw angle

455

Ruangpayoongsak N. and Roth H. (2005).

PATH FOLLOWING IN UNKNOWN ENVIRONMENT FOR A CAR-LIKE MOBILE ROBOT.

In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Robotics and Automation, pages 455-458

DOI: 10.5220/0001180904550458

 SciTePress

Figure 2: Ultrasonic and infrared sensor positions

Figure 1: MERLIN

As shown in Fig. 1, MERLIN is controlled by

80C167 CR 16 bit-processor. The microprocessor is

employed for interfacing sensor data acquisition.

MERLIN is controlled with the driving principle of

a car, steering the front wheels by a servomotor and

propelling the rear wheels by a dc motor.

3 PATH FOLLOWING WITH

OBSTACLE AVOIDANCE

The obstacle avoidance algorithm design bases on

the perception of robot. Due to light on weight and

compact in size, the four ultrasonic and six infrared

sensors are exploited and their positions are shown

in Fig. 2. The infrared sensor provides short distance

obstacle detection of up to 0.8 meter whereas the

ultrasonic sensors provide far distance detection of

up to 7.0 meter. For the sensors on the front and on

the back, the infrared sensors have higher priority

than the ultrasonic sensors regarding the fast

measurement updates. The ultrasonic sensors take

longer cycle time on waiting for the reflected signal.

Therefore, the infrared measured data replaces the

ultrasonic measured data, when the obstacle lies

within 0.8 meter from the robot.

3.1 Obstacle avoidance by wall

following technique

The designed wall following algorithm can be

categorized into two modes, the left hand side and

the right hand side wall following. The wall

following control is to steer the robot to stay far

from the wall or the obstacle border at a specified

distance. Due to small memory requirement for the

computation, the fuzzy logic controller (

Driankov et.

al., 1993

) is selected for steering control. The steering

fuzzy logic control is also combined with if-then

control for wall edges following. The if-then control

is the corporation between steering and propelling

control for the series of concatenated movements.

3.2 Trajectory generation

Based on the car manoeuvring, the examples of the

trajectories are shown in Fig. 3. As shown in Fig. 3a,

the trajectory consists of two unsymmetrical sub-

paths with different radius of the curvature of each

sub-path r

≠

Also, the final heading angle of

each sub-path is unequal

≠

. The distance to

destination dx and dy are the distance between the

current position and the destination in x and y

direction. Note that the robot heading is referred to

x-axis direction. The distances to the destination is

calculated by

dx = r

sin

+ r

sin

(1)

dy = r

cos

+ r

cos

(2)

Let

and r

. As a result, the sub-paths are

symmetry as shown in Fig. 3b. The distances to the

destination of the symmetrical sub-paths are

dx = 2r

sin

(3)

dy = 2r

cos

(4)

As shown in the figure, by fixing the radius r

and varying the angle as

, and

, the final

destination 1, 2, and 3 are obtained. In the iterative

loop, the angle

and radius r

are varied from 5 to

90 degrees and from 1 to 5 meters, respectively. The

minimum of the 1 meter radius is the shortest

curvature radius that the robot can perform. The 90

ICINCO 2005 - ROBOTICS AND AUTOMATION

456

Figure 3: Trajectory generation

degrees is the maximum angle for each sub-path.

The brute force algorithm is applied for searching

the fittest trajectory by using

= x

final

– x

current

, (5)

= y

final

– y

current

, (6)

= x

– dx, (7)

= |y

| – dy, (8)

sum

= |e

|+ |e

|, (9)

where x

final

, y

final

, x

current

and y

current

are the final and

current coordinates, respectively. The fittest path is

the trajectory with the minimum error value e

sum

Note that for all generated candidate trajectories, the

current heading is also the final heading regarding

the symmetry of the two sub-paths as shown in Fig.

3b.

3.3 Basic path following control

The basic path following using the open loop

steering control is developed. During the operation,

the steering angle is fixed until the robot reaches the

destination. Two types of path are curve path and the

line path.

The examples of the curve paths are the sub-

paths in Fig. 3a. The path data are the final heading

, the radius r

, and the movement direction,

forward or backward. The well calibrated gyroscope

provides the angular velocity measurement. The

integration of this measured signal is the robot

heading. The robot stops, where the robot current

heading equals the final heading. For a line path, the

command packet consists of the path length and the

movement direction. Hall sensors provide the driven

distance measurement. The robot stops, where the

driven distance equals the path length.

Figure 4: Path following strategy

3.4 Path following strategy

The integration of the obstacle avoidance, the

trajectory generation, and the basic path following

control is presented here.

As shown in Fig. 4, the original path command is

a dash line through the obstacle to the destination.

Initially, the robot receives a path command from

the user and records its current position and heading.

When the robot detects the obstacle, it stops in front

of obstacle at position 1. The robot chooses to

perform the left or the right hand side wall following

by comparing the measured distance from the front

left and the front right sensors. The wall following is

accomplished, when there is no obstacle in the front.

The robot is free from obstacle at position 2. The

robot stops and calculates the different between the

current heading and the desired final heading. Then

the robot adjusts its heading by turning into the final

heading. The robot heading equals the final heading

at position 3.

At this moment, the robot checks the distance to

the final position y

. If the distance y

is shorter than

0.2 m, the trajectory generation is neglected and the

robot moves straight on to the destination.

Otherwise, the robot sends a request for the

trajectory generation. At this stage, since the robot

heading is pointing to the desired final heading, the

trajectory generation by using (3 - 9) is performed

on the client PC and the generated trajectory consists

of two symmetrical sub-paths. After the robot

receives the generated sub-paths, the robot moves

along the sub-path 1 and sub-path 2 and finally

reaches the destination. At the destination, the robot

heading points to the desired final heading and the

PATH FOLLOWING IN UNKNOWN ENVIRONMENT FOR A CAR-LIKE MOBILE ROBOT

457

ure 6: Robot headin

Figure 5: Robot

osition

robot position is at the desired final position. If the

robot founds obstacles before it reaches the

destination, the process is repeated again from

position 1. The localization technique used is

presented in the paper: “Localization for a car-like

mobile robot using nonlinear dynamic model”.

4 EXPERIMENTAL RESULTS

The desired final position is at 7 meter in forward

direction as shown in Fig. 5. The solid line

represents the estimated robot position and the dash

line represents the original path. The position

numbers are pointed as described in the previous

section. The robot founds the 2

obstacle at 4.3 m.

There, the process is repeated again from position 1.

The actual final position is the real position on the

ground and is close to the estimated final position

from localization. The actual final position lies

within the radius of 0.5 meter around the desired

final position. The robot heading during the

operation is shown in Fig. 6. The robot heading is

adjusted two times at position 3 and the actual final

heading is 0.13 radians.

5 CONCLUSIONS

The path following control for a car-like mobile

robot in unknown environment using the integration

of the basic path following control, the obstacle

avoidance and the trajectory generation is

implemented. The fuzzy controller with the if-then

control is applied for the wall following obstacle

avoidance using on four ultrasonic sensors and six

infrared sensors. The presented trajectory generation

produces two symmetrical sub-paths for approaching

the desired final position and heading. The

experimental results show that the robot performs

the designed path following process successfully and

the robot final position and heading are closed to the

desired final position and heading.

REFERENCES

Driankov, D., Hellendoorn, H., Reinfrank, M., An

Introduction to Fuzzy Control, Springer-Verlag, 1993.

Kuhle, J., Roth, H., Ruangpayoongsak, N., 2004.

MOBILE ROBOTS and airships in a multi-robot

team. The 1st IFAC Symposium on Telematics

Applications in Automation and Robotics, Helsinki

University of Technology, Finland, pp. 67-72.

Nehmzow, U., 2003. Mobile Robotics: A Practical

Introduction, 2

Edition, Springer-Verlag, London.

Roth, H., Schwarte, R., Ruangpayoongsak, N., Kuhle, J.,

Albrecht, M., Grothof, M., Heß, H., 2003. 3D Vision

Based on PMD-Technology for Mobile Robots.

Aerosense - Technologies and Systems for Defense &

Security 2003, SPIE Conference, Orlando, Florida,

paper no. 5803-66.

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