114 Int. J. Automation and Control, Vol. 3, Nos.

2/3, 2009

Navigational strategies of mobile robots: a review

Dayal R. Parhi*
Department of Mechanical Engineering,
NIT Rourkela,
Orissa 769008, India
Email: dayalparhi@yahoo.com
*Corresponding author

Mukesh Kumar Singh

Department of Mechanical Engineering,
Government Engineering College,
Bilaspur, Chattisgarh 495009, India
Email: mukesh3003@yahoo.co.in
Abstract: Present research and development in the area of mobile robots
mainly aims at study of various techniques, methods and sensors being used
for navigation of mobile robots. Different techniques have been discussed for
the navigation of mobile robots in the first part. These techniques can be
subdivided as (1) fuzzy logic technique, (2) neural network technique and
(3) genetic algorithm technique. In the second part, five methods are being
discussed for navigation of mobile robots. These methods are (1) potential field
method, (2) grid-type method, (3) heuristic method, (4) adaptive navigation
method and (v) Virtual Impedance method. The last segment focuses on
different sensors being used for navigation of mobile robots. The sensors
discussed are (1) ultrasonic sensor, (2) laser sensor, (3) magnetic compass disk
sensor, (4) infrared sensor and (5) vision (camera) sensor. Keeping the above
strategies in forefront, a comprehensive discussion has been made and is
described methodologically in the current paper.
Keywords: fuzzy logic; neural network; GA; genetic algorithm; sensors;
navigation; mobile robots.
Reference to this paper should be made as follows: Parhi, D.R. and
Singh, M.K. (2009) ‘Navigational strategies of mobile robots: a review’,
Int. J. Automation and Control, Vol. 3, Nos. 2/3, pp.114–134.
Biographical notes: Dayal R. Parhi received his first PhD in Mobile Robotics
from Cardiff School of Engineering, UK, and second PhD in Vibration
Analysis of Cracked Structures from Sambalpur University, Orissa, India. He
has 16 years of research and teaching experience in his fields. Presently, he is
engaged in mobile robot navigation research, and a faculty member in the
Department of Mechanical Engineering, National Institute of Technology
Rourkela, Orissa, India.
Mukesh Kumar Singh is a faculty member in the department of Mechanical
Engineering, Government Engineering College, Bilaspur, Chhattisgarh, India.
He has 12 years of research and teaching experience in his field. He did MTech
in CAD/CAM. Presently, he is doing research for his PhD in the field of
mobile robots navigation.

Copyright © 2009 Inderscience Enterprises Ltd.

 Navigational strategies of mobile robots 115

1 Introduction
For last several years, the scientists and engineers in the field of mobile robotics have
extensively given attention for development of various control strategies for navigation
of mobile robots. The researchers have been outlined two design principles for
the mobile robot navigation architecture. One is known as functional or horizontal
decomposition (Cameron and Probert, 1994) (Figure 1a) and the other is the behavioural
or vertical decomposition (Brooks, 1986) (Figure 1b).

Figure 1 (a) Flow diagram of the horizontal decomposition method for navigation of mobile
robot. (b) Flow diagram of the vertical decomposition method for navigation of
mobile robot

Navigation for mobile robots can be well-defined in mathematical (geometrical) terms.

It also involved many distinct sensory inputs and computational processes. Elementary
decisions like turn left, or turn right, or run or stop is made on the basis of thousands of
incoming signals (Waterman, 1989). Thus it is necessary first to define what navigation
is and what the function of a navigation system is. Levitt and Lawton (1990) tried to
define the navigation by following three questions: (1) ‘Where am I?’, (2) ‘Where are
other places relative to me?’ and (3) ‘How do I get to other places from here?’.
Underlying question (1) is the problem of recognising and identifying the particular place
and questions (2) and (3) focused the point, how to get rid of the obstacle and march
towards goal.
An Autonomous Mobile Robot (AMR) is a system capable of interpreting,
perceiving, executing and realising a task in an environment without any outside help.
To accomplish this, robot must first be able to interpret and perceive its environment,
then analyse and model it. Next using this information, a navigation algorithm must
allow the robot to determine a suitable trajectory with its available information. Finally, a
control process must be able to assume that the robot moves correctly within its
environment. In order to be able to move safely in a work place and to detect the object
near by, the mobile robot must have a way to perceive its environment. Researchers
prefer to use devices such as laser, sonar, vision, infrared sensor (Crowley and Coutaj,
1986; Borenstein, 1995; Borenstein and Koren, 1995; Racz and Dubrawski, 1995) or a
heterogeneous system using any two devices (Buchberger et al., 1993; Lin et al., 1996).
Using the environment information obtained at each instant of time ‘t’, a strategy
may be adopted permitting the robot to reach the target position. At the same time, the
robot should avoid the different obstacles situated in the robot work place. Information
available to the robot constitutes a problem that the AMR is confronted with. The classic
116 D.R. Parhi and M.K. Singh

methods used in robotics for resolving this type of problem are those using a graph
method, tree search method (Lozano-Perez and Wesley, 1979) and Potential Field
Method (PFM) (Guldner et al., 1997; Reigner et al., 1997; Shkel and Lumelsky, 1997).
Keeping in view of the various research publications in the recent years (Lin et al.,
1996; Cao et al., 1997; Janet et al., 1997a; Mataric, 1997a; Ram et al., 1997; Tsoukalas
et al., 1997) in this field, attempt has been made to summarise the various navigation
techniques for mobile robots. For reviewing the approaches for navigation of mobile
robots, the investigation has been divided into three main segments. (1) Different types
techniques, which is again subdivided into three subchapters such as (a) fuzzy logic
technique, (b) neural network technique and (c) Genetic Algorithm (GA) technique.
(2) Different methods, which is divided into five subchapters: (a) PFM, (b) grid-type
method, (c) heuristic method, (d) adaptive navigation method and (e) Virtual Impedance
method. The last part (3) is on the different sensors. These sensors are categorised into
five types, i.e. (a) ultrasonic sensor, (b) laser sensor, (c) magnetic compass disk sensor,
(d) infrared sensor and (e) vision (camera) sensor. It is found that the research for
navigation of mobile robot has to be modified in many terms. Research may be done in
finding out the optimal navigation technique for several mobile robots. Technical details
may be found out to achieve various interactive perceptions (e.g. communications)
between the robots and to recognise the obstacle ahead.

2 Different techniques used for navigation of mobile robots

In the recent years, three types of techniques come into picture, in the field of navigation
of mobile robots. They may be classified as: (1) fuzzy logic technique, (2) neural
network technique and (3) GA technique. Keeping in view of the navigation of mobile
robots, the applications of these techniques are summarised below.

2.1 Fuzzy logic technique

Fuzzy logic technique can be used for navigation of mobile robots. Many researchers
have used this technique in the recent years for the navigation of mobile robot and are
discussed below.
Martinez et al. (1994) have considered a problem which is consisted of achieving
sensor-based motion control of mobile robot among obstacles in structured and/or
unstructured environments with collision-free motion. Sensor-based navigation method,
which utilised fuzzy logic and reinforcement learning for navigation of mobile robot in
uncertain environments, has been proposed by Boem and Cho (1995). Liu and Lewis
(1994) and Zhang and Knoll (1998) have discussed about the navigation of mobile robot
using fuzzy logic. Beaufrere and Zeghloul (1995a, 1995b) have discussed about real-time
navigation planning through an unknown obstacle field for mobile robot. For their
approach, they have used fuzzy reasoning. They have tested the approach both by
simulation and experiment.
The robot direction modification imposed by the action ‘reaching the goal’ is decided
by the fuzzy rules, represented as follows:
If {distance ρ is MF and direction α is LM}, then (ψf = Cf)
where, ψf = robot direction angle, Cf = best free path defined by the sensor, ρ and α are
respectively the distance and the direction of the subgoal in the robot polar coordinate.
M = medium, MF = medium far, L = large.
 Navigational strategies of mobile robots 117

A neuro-fuzzy system architecture for behaviour-based control of mobile robot in

unknown environments has been presented by Li et al. (1997), Li and Chenyu (1997) and
Li et al. (1998). They acquired the range information by ultrasonic sensors. Based upon a
reference motion direction and distances between the robot and obstacles, they have
fused different types of behaviour by fuzzy logic to control the velocities of the two-rear
wheel of the robot (Figure 2). They have also shown the simulation result for the
proposed method. Benreguieg et al. (1997) have discussed about navigation of mobile
robot using fuzzy logic.
In Figure 2, N = negative, Z = zero, P = positive; left_V = velocity of left wheel,
right_V = velocity of right wheel.
The fuzzy rules are as follows:
If (left_obs is near and front_obs is near and right_obs is near and head_ang is N) then
(left_V is fast and right_V is slow).
If (left_obs is near and front_obs is med and right_obs is med and head_ang is N) then
(left_V is med and right_V is med).

Figure 2 Schematic diagram of the fuzzy logic for navigation of mobile robots. (a) If (left_obs
is near and front_obs is near and right_obs is near and head_ang is N) then (left_V is
fast and right_V is slow) (rule for avoiding obstacles) and (b) If (left_obs is near and
front_obs is med and right_obs is med and head_ang is N) then (left_V is med and
right_V is med) (rule for avoiding edge)
118 D.R. Parhi and M.K. Singh

Wang (1997) has used fuzzy systems to model higher levels of hierarchical systems and
design controllers for the hierarchical systems. Castellano et al. (1997) have also used
fuzzy rules for navigation of TRC Labmate mobile robot. Seraji and Howard’s (2002)
paper presents a new strategy for behaviour-based navigation of field mobile robots
on challenging terrain, using a fuzzy logic approach and a novel measure of terrain
traversability. Das and Kar (2006) have assumed a control structure that makes possible
the integration of a kinematic controller and an adaptive fuzzy controller for trajectory
tracking for nonholonomic mobile robots.

2.2 Neural network technique

In recent years, many engineers have used neural network technique for navigation of
mobile robot. The works carried out by them are described below.

Figure 3 Schematic view of the neural networks used for the navigation of mobile robots, the
output of the Kohonen network is fed into the feed forward network as a regression
 Navigational strategies of mobile robots 119

Tani and Fukumura (1994, 1995, 1997) have presented a novel scheme for sensory-based
navigation of a mobile robot. Their robot is trained to learn a goal-directed task under
adequate supervision, utilising local sensory inputs. They have shown that their scheme
constructs a correct mapping from sensory inputs sequences to the manoeuvring outputs
through neural adaptation, such that a hypothetical vector field that achieves the goal can
be generated (Figure 3). Their simulation results have shown that robot can learn task of
homing and sequential routing successfully in the work space of a certain geometrical
complexity. Janet et al. (1997a, 1997b) have discussed about the neural network
technique for navigation of mobile robot. They have used Kohonen and region-feature
neural networks for this purpose. In their method, single robot can transform its
knowledge of various learned regions to other mobile robots. Nelson et al. (2004) have
described the evolutionary training of artificial neural network controllers for competitive
team as game playing behaviours by teams of real mobile robots. Pradhan et al. (2006a)
have described the motion planning of multiple mobile robots. In their investigation,
rule-based and rule-based neuro-fuzzy techniques are analysed for multiple mobile robots
navigation in an unknown or partially known environment. Rusu et al. (2003) have
discussed a neuro-fuzzy controller for sensor-based mobile robot navigation in indoor
environments. The control system consists of a hierarchy of robot behaviours.

2.3 Genetic algorithm technique

The GA has been used by many engineers for navigation of mobile robot. Many
researchers have used this algorithm in the recent years for the navigation of mobile
robots which are described below.
The paper by Wilson et al. (1997) is on genetic approach to evolving hierarchical
robot behaviour algorithm for navigation of mobile robot. Michel (1996) has used GAs
and artificial neural networks for navigation of autonomous robot. Ashiru et al. (1995,
1996) and Ashiru and Czarnecki (1997) have used genetic-based path-planning algorithm
for mobile robot navigation in cluttered environment. Ming et al. (1996) have used GA
for path planning of mobile robot. Their designed control system has allowed mobile
robot to avoid unexpected obstacles in an unknown environment. Noguchi and Terao
(1997) have developed a path for agricultural mobile robot using GA. Using GA they
have optimised the time series of the steering angle and have created the optimal work
path of the mobile robot. Hoffmann and Pfister (1997) have presented a learning method
which automatically designs the controllers by means of a GA for mobile robot. They
have tested their theory both in simulation and by experiment. Joo et al. (1997) have
discussed about GA to produce a model for navigation control of a mobile robot.
The validity of their result has been demonstrated by experiment.

3 Different methods

Researcher uses various methods for the navigation of mobile robot. They may be
classified as (1) PFM, (2) grid-type method, (3) heuristic method, (4) adaptive navigation
method and (5) Virtual Impedance method. The utilities of these methods are described
below.
120 D.R. Parhi and M.K. Singh

3.1 Potential field method

Many researchers have used this method for navigation of the mobile robot, which are
described below.
An algorithm based on an artificial potential field and hierarchical cell decomposition
technique has been developed by Hou and Zheng (1994) to solve the path-find problem
for a mobile robot. They have also presented the computer simulations for various
obstacles scenarios.
Tsourveloudis et al. (2001) have described an electrostatic potential field path planner
combining with a two-layered fuzzy logic inference engine. Their proposed approach is
experimentally tested using the Nomad 200 mobile robot. Gazi (2005) has considered a
control strategy of multiagent systems, or simply, swarms, based on artificial potential
functions and the sliding-mode control technique. Pathak and Agrawal (2005) have
presented navigation and control of an autonomous mobile unicycle robot in an obstacle-
ridden environment. Their scheme is first verified in computer simulation of a single
robot moving in amaze. It is then implemented on an experimental setup of robots
equipped with proximity sensors. Ren et al. (2006) have investigated the inherent
oscillation problem of PFMs in the presence of obstacles and in narrow passages. They
have validated this technique by comparing its performance with the gradient descent
method in obstacle-avoidance tasks with different potential models and parameter
changes. Pradhan et al. (2006b) have described modified PFM for robots navigation.

Figure 4 Schematic view of grid type world model used for path planning of mobile robot
 Navigational strategies of mobile robots 121

3.2 Grid method

Many scientists map the environment into grids and use them in the navigation of the
mobile robots. This method, which has been used by various researchers, has been
discussed below.
The paper presented by Lee and Chung (1993, 1994) is based on a methodology for
global path planning for AMR in a grid-type world model (Figure 4). The value of a
certainty grid representing the existence of an obstacle in the grid has been calculated
from reading of sonar sensors. They have implemented the methodology on the mobile
robot whose role was to transport materials in a flexible manufacturing system. Yun et al.
(1997) have suggested quad-tree exploration approach for navigation. The quad-tree has
been made up from a sonar probability map, which has been constructed by sonar range
sensing and Bayesian probability theory. They have implemented the method on a real
robot AMROYS-II. The paper by Lee and Recce (1997) has described map building and
exploration capabilities of mobile robot. They have used two types of map: (1) a set of
line and point features and (2) a grid-based free-space map. They have extracted potential
features for the mobile robot from sonar range readings and used distance-transform
algorithm to plan paths on this map.

3.3 Heuristic method

Many investigators have used heuristic search method for the navigation of mobile robot,
which is depicted below.
Simsarin et al. (1996) have discussed about the problem of mobile robot self-
localisation by a given polynomial and a set of observed edge segments. They have used
heuristic method to match its edges. In their approach, the map has been decomposed
into View-Invariant Regions (VIR’s). The VIR decomposition captures information
about map edge visibility and is used for robot navigation task. The automatic reaction of
a mobile robot, computed in real time during its movement towards a target, in an open
field with obstacles in it has been dealt by Xu and Tso (1996). They have implemented
the navigation control of the robot through fuzzy reasoning by utilising the above-
mentioned heuristic rules. Song and Chin (1996) have experimentally studied the
navigation system that allows mobile robot to travel in an environment about which it has
no prior knowledge. Meeran and Shafie (1997) have discussed about the optimal path
planning for mobile robot. For this, they have presented a system which uses heuristic
rules to augment the convex hull initial sub-tour created by the Graham scan algorithm.
Bruske et al. (1997) have discussed about reinforcement learning of reactive collision
avoidance. With the help of adaptive heuristic method, they are able to find an obstacle-
free path for the mobile robot. The local path-planning algorithm using a human’s
heuristic method along with laser range finder for real-time navigation of a free-ranging
mobile robot has been discussed by Cha (1997). Their algorithm utilises the human’s
heuristic by which the shortest path from various pathways to the goal can be found.

3.4 Adaptive method

Some of the scientists in recent years have used adaptive methods for the navigation of
mobile robots. The researches by them are described below.
122 D.R. Parhi and M.K. Singh

The design and implementation of a hybrid control technique for a tracked mobile
robot has been discussed by Fan et al. (1995). Their controller has utilised an adaptive
control algorithm to identify the system parameters in real time, which in turn are used to
reduce the vehicle external error. Ram et al. (1997) and Ram and Santamaria (1997) have
discussed about a continuous case-based reasoning and its application to the dynamic
selection, modification and acquisition of robot behaviours in an autonomous navigation
system. They have implemented the method in the ACBARR (A Case-Based Reactive
Robotic) system.
Yamada and Saito (2001) have described an adaptive method for multiple mobile
robots box-pushing in a dynamic environment. They have implemented their method on
four real mobile robots and make various experiments in dynamic environments. Dixon
et al. (2001) have considered the problem of position/orientation tracking control of
wheeled mobile robots via visual serving in the presence of parametric uncertainty
associated with the mechanical dynamics and the camera system. Simulation and
experimental results are included to illustrate the performance of the control law. Zalama
et al. (2002) describes an adaptive neural network model for the reactive behavioural
navigation of a mobile robot. From the information received through the sensors the
robot can exhibit one of several behaviours (e.g. stop, avoid, stroll, wall following),
through a competitive neural network. Ji et al. (2003) have presented a novel fault
adaptive control methodology for mobile robots. The physical processes of the robot are
modelled using bond graphs. New simulation results verify the proposed fault adaptive
control technique for a mobile robot.

Figure 5 Schematic view for the motion planning of mobile robots by Virtual Impedance method
 Navigational strategies of mobile robots 123

3.5 Virtual Impedance method

Virtual impedance is one of the emerging methods used by various engineers for
navigation of mobile robot. Ota et al. (1996a, 1996b) and Arai and Ota (1996a, 1996b)
have discussed about the concept of ‘groups’ in motion planning of multiple mobile
robots. They have classified the groups into ‘static groups’ and ‘dynamic groups’. Then
they applied the group algorithm in Virtual Impedance method (Figure 5). In Virtual
Impedance method, the trajectory is determined by means of virtual forces. The virtual
forces consist of three parts: that is the force generated between a reference point of the
robot at the present time and the real position of the robot, the force generated between
two robots and the force generated between a robot and an obstacle. The reference points
for each robot as a function of time are calculated in advance. The dynamic equations of
each robot are expressed as follows:
nobs,i nrob,i
1
xi =
 (Ftra,i + ∑ Fobs,i,j + ∑ Frob,i,j
Mi j j

where
Ftra,i = (Ftra,i,x , Ftra,i,y )T

Ftra,i,x = K tra,x (x des,i − x i ) + D tra,x (x des,i − x i ),

Ftra,i,y = K tra,y (y des,i − yi ) + D tra,y (y des,i − y i ),

Fobs[rob],i,j = (Fobs[rob],i,j,x ,Fobs[rob],i,j,y )T ,

⎧ ⎛ 1 1 ⎞ x obs[rob],j − x i
⎪K obs[rob] ⎜⎜ − ⎟⎟
⎪ ⎝ d i,j − L omin[rmin] L obs[rob] − L omin[rmin] ⎠ d i,j
⎪
⎪ L obs[rob] − L omin[rmin]
Fobs[rob],i,j,x = ⎨+ Dobs[rob] x (x obs[rob],j − x i ), for d i,j ≤ Lobs[rob] ,
⎪ d i,j − Lomin[rmin]
⎪0 for d i,j > Lobs[rob]
⎪
⎪
⎩

⎧ ⎛ 1 1 ⎞ y obs[rob],j − yi
⎪K obs[rob] ⎜⎜ − ⎟⎟
⎪ ⎝ d i,j − Lomin[rmin] Lobs[rob] − Lomin[rmin] ⎠ d i,j
⎪
⎪ L obs[rob] − L omin[rmin]
Fobs[rob],i,j,y = ⎨+ Dobs[rob] x (y obs[rob],j − y i ), for d i,j ≤ L obs[rob] ,
⎪ d i,j − L omin[rmin]
⎪0 for d i,j > Lobs[rob]
⎪
⎪
⎩
Di,j is the distance between robot i and obstacle[robot] j, nobs[rob],i the number of
obstacles[robots] within the sensing area of robot i, xi(=(xi,yi)T) the present position
of robot i, (xobs[rob],i, Yobs[rob],i)T the position of obstacle(robot) i and (xdes,i, Ydes,i)T is the
reference point of the robot at the present time. And Xdes, Ydes: desired position and
velocity of a robot at time T; Ktra,x, Ktra,y: spring coefficient for target; Krob, Kobs: spring
124 D.R. Parhi and M.K. Singh

coefficient for robot and obstacle; Dtra,x, Dtra,y: damper coefficient for target; Drob, Dobs:
damper coefficient for robot and obstacle; Ftra: force for planned trajectory; Fobs[rob]:
force for obstacle[robot]; Lomin, Lrmin: minimal length of spring; Lobs, Lrob: normal length
of spring.

4 Different sensors used for navigation of mobile robots

For navigation of mobile robot, sensors have an important role to play. There are
different types of sensors used for navigation of mobile robot in the recent years. They
can be classified into these categories: (1) ultrasonic sensor, (2) laser sensor, (3) magnetic
compass disk sensor, (4) infrared sensor and (v) vision (camera) sensor. Various
researchers have used these sensors in navigation of mobile robots and they are described
below.

4.1 Ultrasonic sensor

Ultrasonic sensors are widely used as external sensors for mobile robots because they are
simple to build and are low cost. Generally, in the pulse-echo method the distance to the
target can be accurately measured. The scientists use them for the navigation of mobile
robot, are discussed below.
Kurz (1995, 1996) has discussed about an approach, which will generate
environmental maps, based on ultrasonic range data for mobile robots. He has used
ultrasonic range data, dead-reckoning and graph nodes for generating a map of free space
in the form of graph for the mobile robot. Hong and Kleeman (1997) have discussed
about the sensing of three-dimensional (3-D) room boundaries for mobile robot using
ultrasonic sensor array. They have implemented their algorithm with the help of extended
Kalman filter. Budenske and Gini (1997) have discussed about the navigation of robot
with the help of sensory data. Their approach is based on two premises. (1) Plan
execution in an information gathering process where determining what information is
relevant in a great path of process and (2) plan execution requires that many details are
made explicit. They have also done the experimental verification. The paper by
Takamura et al. (1997) and Nakamura et al. (1996a, 1996b) has proposed a method to
acquire a statistical map representation robust to sensor noise and directly usable for a
navigation task. Their robot is equipped with ring of ultrasonic sensor, whose data are
used to give a graphical representation of the environment. They have shown the validity
of their method by computer simulation and experiments. Miyata et al. (1996) have
discussed about navigation of their mobile robot using ultrasonic sensor (Figure 6).
Han et al. (1996) have described about a mobile robot to follow a moving target.
Their paper proposes a new solution for this problem, using the virtual ultrasonic image
which is simply constructed by accumulating the returned ultrasonic signals. The mobile
robot implemented for testing the performance of the proposed algorithm has shown that
it follows a moving target successfully in various working environments. Choset et al.
(2003) have described about a new method for improving the azimuth accuracy of range
information using conventional (Polaroid) low-resolution ultrasonic sensors mounted
in a circular array on a mobile robot. Experimental results on an ultrasonic sensor array
situated on a mobile robot verify this approach.
 Navigational strategies of mobile robots 125

Figure 6 Schematic view of supersonic transmitter mounted on mobile robot DREAM-1

4.2 Laser sensor

Laser sensor is one of the popular sensors used for navigation of mobile robot. The use of
this sensor for navigation is summarised below.
Navigation of mobile robot in cluttered room using a range-measuring laser as a
sensor has been described by Takeda et al. (1994). They introduced a method called
‘sensory uncertainty field’ for every possible robot configuration ‘q’. This field estimated
the distribution of possible errors in the robot configuration. The paper by Horn and
Schmidt (1995) has described the localisation system of a free-navigating mobile robot.
They have determined the absolute positioning and orientation of the mobile robot and
experimentally verified the result. The focus of the paper presented by Jorg (1995) is to
describe an approach which has utilised heterogeneous information provided by laser
radar. They have tested the approach using a robot named MOBOT-IV.
The paper by Larsson et al. (1996) has presented an algorithm for environment
mapping by integrating scans from a time-of-flight laser and odometer readings from a
mobile robot. They have presented experimental results for their algorithm. Pears and
Probert (1996) have discussed about an eye-safe laser range finder which acquires 2-D
range data. Cha and Gweon (1996) have described a calibration method and range data
extraction algorithm of an omnidirectional laser range finder for navigation of mobile
robot. Min and Cho (2006) have described about intelligent AMRs. They have performed
a series of experimental tests to show the simplicity, efficiency and accuracy of this
proposed laser sensor system for 3-D environment sensing and recognition.
126 D.R. Parhi and M.K. Singh

4.3 Magnetic compass disc sensor

Magnetic compass sensor is mainly used for navigation of mobile robot. Their use has
been discussed in the following section.
Finding the steering angle of an AMR by using an encoded magnetic compass disc as
an orientation sensor has been discussed by Kim and Seong (1996). They have done
the experiments on two cases: (1) line path tracking test in a slippery environment and
(2) orientation steering test in a circular path. Borenstein and Koren (1995), Borenstein
(1995) and Borenstein et al. (1997) have shown that the magnetic compass is a very good
sensor for finding out the location and heading angle (x, y and θ) for navigation of
mobile robot. They have outlined different magnetic sensors, i.e. (1) mechanical
magnetic compass, (2) fluxgate compass, (3) hall-effect compass, (4) magneto-resistive
compass and (5) magneto-elastic compass. The compass best suited for use with mobile
robot applications is the fluxgate compass.
The paper by Noguchi et al. (1997) has described about a mobile robot system,
including a positioning system, using the geomagnetic direction sensor. They have found
the average error of the final position of each target position to be about 0.4 m. Bright
et al. (1997) have discussed about the inspection of the inner surface of a pipe by a
mobile robot. For this, they have used sensor, which can detect magnetic flux variation to
detect the defects.

4.4 Infrared sensor

Infrared sensor remains one of the efficient devices for navigation of mobile robots.
The uses of this sensor for the navigation of mobile robot in the recent years have been
discussed below.
Yu and Malik (1995) have described about AIMY – an Autonomous Mobile Robot.
They have used infrared detector system to avoid collision with unexpected obstacles.
They have also given the experimental results. Kube and Zhang (1997) in their robot
have used infrared sensor for obstacle avoidance. Dawkins et al. (1997) have also used
infrared obstacle sensors for the navigation of their RASCAL mobile robot. Cheung and
Lumelsky (1989) and Lumelsky and Harinarayanan (1997) have presented an approach
for decentralised real-time motion planning for multiple mobile robots operating in
unknown stationary obstacles (Figure 7). They have used infrared sensor for their sensor
feedback. Vandorpe et al. (1996) has designed an AMR known as Leuvan intelligent
autonomous system. The robot is equipped with three type of sensors such as: ultrasonic
sensor, tri-aural sensor and infrared sensor. In their robot, an onboard system executes
different modular navigation task. Mataric (1994, 1997a, 1997b) has also used infrared
sensor for navigation of mobile robot.

4.5 Vision sensor

For object recognition by the mobile robot, scientists mainly dependent upon vision
sensor. The use of this sensor for the navigation of mobile robot in recent years has been
discussed below.
 Navigational strategies of mobile robots 127

Figure 7 Mobile robots navigation based on infrared sensor. T1 and T2 are the target positions
of the robots R1 and R2O1 and O2 are the obstacles

Figure 8 Schematic view for the navigation of autonomous mobile robot by vision sensor
128 D.R. Parhi and M.K. Singh

Delaescalera et al. (1997) and Salichs et al. (1997) have pointed out that vision-based
navigation of mobile robot has three main rules: (1) road detection, (2) obstacle detection
and (3) sign recognition. They mainly concentrate their research on sign recognition.
Li et al. (1997, 1998) have presented a method for recognising white line marking for
navigation of mobile robot. They have used fuzzy-reasoning-based general technique for
edge detection. They have used the method for the mobile robot THMR-III. Gofuku et al.
(1996) and Tanaka et al. (1995) have described an improved running control algorithm
based on the visual mapping for navigation of mobile robot. The robot is equipped with
a CCD camera for finding out its path (Figure 8). They have tested their approach on a
mobile robot in an unknown indoor environment to learn scenes and the associated
navigation experience. Sala et al. (2006) have figured out recent work in the object
recognition suited to landmark-based navigation. They have introduced a novel graph
theoretic formulation of the problem. Saeedi et al. (2006) have described a vision-based
system for 3-D localisation of a mobile robot in a natural environment. Their
experimental results show that good tracking and localisation can be achieved using the
proposed vision system.

5 Conclusions

This paper addresses the different strategies for navigational control of multiple mobile
robots. The strategies are mainly segmented into following three categories:
1 Navigation of mobile robot using different techniques such as:
a fuzzy logic technique
b neural network technique
c GA technique.
2 Navigation of mobile robot using different methods such as:
a PFM
b grid-type method
c heuristic method
d adaptive navigation method
e Virtual Impedance method.
3 Navigation of mobile robot using different sensors such as:
a ultrasonic sensor
b laser sensor
c magnetic compass disk sensor
d infrared sensor
e vision (camera) sensor.
 Navigational strategies of mobile robots 129

From the current investigation, it has been noticed that the mobile robot navigation can
be controlled successfully in a complex environment using the above strategies. Keeping
in view the above research, the following further works are suggested. Most efficient
control strategy for navigation of mobile robots may be found out and used for any
cooperative or individual task, for multidimensional robot environment. The robots
should able to handle the cooperative task in a coordinated manner.
The working principle and controllers for various sensors can be outlined for
powerful cognition of the complex environment around the robots to distinguish between
targets, surrounding obstacles, other moving robots and for cooperative behaviour.

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