Mobile Robotics

By

ITHINYAI MOSES MUTWIRI

Department of computer science

South Eastern Kenya University

Kitui, Kenya

P15/1/1548/2011

Email: mosemutwi90@gmail.com

Abstract

As research is taking root in various branches and

interdisciplinaries of computer science robotics is one of the

field growing day by day, robotics is discipline that deals with

the design, construction, operation, and application of robots.

Many people have been thinking that robots look

like human beings, which is not true, there are various

classifications of robots, and these mainly include: Mobile

robotics, Stationary robotics, which are not only used to

explore areas or imitate human being. Most robots perform

repeating tasks without ever moving an inch. Most robots are

‘working’ in industry settings. Especially dull and repeating

tasks are suitable for robots. A robot never grows tired, it will

perform its duty day and night without ever complaining. In

case the tasks at hand are done, the robots will be reprogrammed

to perform other tasks..

Robotics also includes, Autonomous robots, which are self-

supporting or in other words self-contained. In a way they rely

on their own ‘brains’. Autonomous robots run a program that

give them the opportunity to decide on the action to perform

depending on their surroundings. At times these robots even

learn new behavior. They start out with a short routine and

adapt this routine to be more successful at the task they

perform. The most successful routine will be repeated as such

their behavior is shaped. Autonomous robots can learn to walk

or avoid obstacles they find in their way. Think about a six

legged robot, at first the legs move and random, after a little

while the robot adjust its program and performs a pattern which

enables it to move in a direction.

Remote-control Robot is An autonomous robot,

despite its autonomous not a very clever or intelligent

unit. The memory and brain capacity is usually

limited, an autonomous robot can be compared to an

insect in that respect. In case a robot needs to perform

more complicated yet undetermined tasks an

autonomous robot is not the right choice. Complicated

tasks are still best performed by human beings with

real brainpower. A person can guide a robot by

remote control. A person can perform difficult and

usually dangerous tasks without being at the spot

where the tasks are performed. To detonate a bomb it

is safer to send the robot to the danger area.

In computing there may also be virtual robots, which are

termed as they don’t exist in real life. Virtual robots are just

programs, building blocks of software inside a computer. A

virtual robot can simulate a real robot or just perform a repeating

task. A special kind of robot is a robot that searches the World

Wide Web. The internet has countless robots crawling from site

to site. These WebCrawler’s collect information on websites and

send this information to the search engines. Other popular virtual

robots are chatterbox, BEAM robots

In this research paper mobile robotics is featuring

1.0 Introduction

Working

Majority of mobile robots are required to work in un-engineered

environments. Compare the work-spaces of a welding robot in

automotive plant to one that delivers blood samples between labs

in a hospital. The former operates in a highly controlled, known,

time invariant (apart from the thing being built) scene. If

computer vision is used as a sensor then the workspace can be

lit arbitrarily well to mitigate against shadows and color

ambiguity. Many industrial robots work in such well known

engineered environments that very little external sensing is

needed they can do their job simply by controlling their own

internal joint angles. Hospitals are a different ball-park

altogether. The corridors are dynamic filling and emptying

(eventually) with people on stretchers. Even if the robot is

endowed with a map of the hospital and fitted with an upward

looking camera to navigate or markers on the ceiling it still has

to avoid fast moving obstacles (humans) while moving

purposely towards its goal destination. The more generic case

involves coping with substantial scene changes (accidental or

malicious) for example doors closing in corridors or furniture

being moved. The thing then, which makes mobile robotics so

challenging is uncertainty.

Robot should perceive its environment through sensors

and act upon it through actuators. A mobile robot might have A

robotic agent might have:

cameras and

infrared range finders for sensors and

Various motors for actuators.

Suppose an a mobile robot of taxi driver has to

be made ,its performance can be measured in terms

of safe, fast, legal, comfortable trip, maximize

profits.to make clear observation it may operate in an

environment such as: roads, other traffic, pedestrians

and customers. Actuators like steering, accelerator,

brake, signal, horn, display need to be included.

Cameras, sonar, speedometer, GPS, odometer,

accelerometer, engine sensors, and keyboard are the

sensors that might be used.

1.1. AREAS COVERED IN MOBILE ROBOTICS

Components of a Mobile Robot

A mobile robot is a combination of various physical

(hardware) and Computational (software) components.

Locomotion how the robot moves through its environment.

Sensing how the robot measures properties of itself and its

environment. Reasoning how the robot maps the

measurements into actions. Communication how the robot

communicates with an outside operator. In terms of

software components, a set of subsystems are responsible

for: Planning in its various aspects.

1.1.0. Robot locomotion, navigation and mapping

Mobile robots have the capability to move around in their

environment and are not fixed to one physical location (cited

from Wikipedia).They have the advantage of consuming less

energy and move faster than other type of locomotion

mechanisms. The application of mobile robots have been

successfully designed for both indoor and outdoor

environments. Mobile robots incorporating vision systems are

the most desirable type of mobile robots navigation which can

provide a lot of information of the real world enough for better

navigation. Furthermore, object tracking technique is utilized in

various robot vision applications such as autonomous vehicle

navigation, surveillance and many more applications. Although

a lot of research and experiments have been done in the past

few decades had been concerned and dedicated to solve the

problematic issue of tracking the desired target in a chaotic and

noisy environment.

There are several mechanisms to accomplish locomotion

mechanisms to make robot enable to move through its

environment; for example one, four, and six legged locomotion

and many configurations of wheeled locomotion. Legged robot

locomotion mechanisms are often inspired by biological

systems, which are very successful in moving through a wide

area of harsh environments. To implement locomotion

mechanisms in robotics the main problems are the mechanical

complexity of legs, stability and power consumption. The main

attributes of stability are the number and geometry of contact

points, the robots Centre of gravity, if the robot is static or

dynamic stable and the inclination of terrain. The

characteristics of ground contact depend on the type of the

contact point (in case of legged locomotion this is the

footprint), the angle of contact to the ground and the friction

between the robot and the surface. The attributes of the type of

the environment are the structure of the medium (for example

in case of hard ground, if the ground is either flat or rough) and

the medium itself (e.g. water, air, hard or soft ground).

Legged Locomotion

A legged robot is well suited for rough terrain; it is able to

climb steps, to cross gaps which are as large as its stride and to

walk on extremely rough terrain where, due to ground

irregularities, the use of wheels would not be feasible. To make

a legged robot mobile each leg must have at least two degrees

of freedom (DOF). For each DOF one joint is needed, which is

usually powered by one servo. Because of this a four legged

robot needs at least eight servos to travel around. Figure 3

shows the energy consumption of different locomotion

concepts. It strikes that the power consumption of legged

locomotion is nearly two orders of magnitude more inefficient

than of wheeled locomotion on hard, flat surface (e.g. railway

wheel on steel). One reason for this is that wheeled locomotion

requires in general fewer motors than legged locomotion.

Figure 3: Power consumption of several locomotion [1]

Mechanisms

When the surface becomes soft wheeled locomotion offers

some inefficiency, due to increasing rolling friction more motor

power is required to move. As figure 3 shows legged

locomotion is more power efficient on soft ground than

wheeled locomotion, because legged locomotion consists only

of point contacts with the ground and the leg is moved through

the air. This means that only a single set of point contacts is

required, so the quality of the ground does not matter, as long

as the robot is able to handle the ground. But exactly the single

set of point contacts offers one of the most complex problem in

legged locomotion, the stability problem.

Stability

Stability is of course a very important issue of a robot, because

it should not overturn. Stability can be divided into the static

and dynamic stability criterion.

Static stability means that the robot is stable, with no need of

motion at every moment of time. In Static stability, balance is

maintained as long as the Centre of mass is completely within

the legs. This results to a triangle called support polygon. The

support polygon is the convex hull which is set by the ground

contact points. Of course, in case of more ground contact

points, the polygon can be a quadrangle or a pentagon or a

different geometrical figure. More in general the following

must hold to support static stability: Static stability is given,

when the Centre of mass is completely within the support

polygon and the polygon’s area is greater than zero, therefore

static stability requires at least three points of ground contact.

To achieve statically stable walking a robot must have a

minimum number of four legs, because during walking at least

one leg is in the air. Statically stable walking means that all

robots’ motion can be stopped at every moment in the gait

cycle without overturning. Most robots which are able to walk

static stable have six legs, because walking static stable with

four legs means that just one leg can be lifted at the same time

(lifting more legs will reduce the support polygon to a line), so

walking becomes slowly. Most two legged walking machines

are dynamically stable for several reasons. Human like robots

have relatively small footprints, because of this the support

polygon is almost a line (in the double support phase, when

both foots are connected with the ground) which is even

reduced to a single point (in the single support phase, when just

one foot has ground contact) during walking. Therefore the

robot must actively balance itself to prevent overturning. In

face of that the robots’ Centre of mass has to be shifted actively

between the footprints. But the robots exact Centre of mass is

hard to predict due to the high dynamic of walking (for

example because of the force which is imparted to whole robot

when one leg swings forward). The realization of bipedal

dynamic stable walking machines is due to the continuous

danger of overbalance a high complex problem for engineers,

which is just solved for some special cases.

Leg configuration

To move a leg forward at least two degrees of freedom are

required, one for lifting and one for swinging. Most legs have

three degrees of freedom; this makes the robot able to travel in

rougher terrain and to do more complex manoeuvres. Figure 5

shows the leg of the Titan VIII robot from the Tokyo Institute

of Technology. This leg has three degrees of freedom. In

general, adding degrees of freedom to a robots leg means

increasing the manoeuvrability of the robot, the range of terrain

on which it can travel and the ability to travel in a variety of

gaits.

But adding degrees of freedom causes also some

disadvantages, because for moving additional joints and more

servos are required, this increases the power consumption and

the weight of the robot. Furthermore controlling the robot

becomes more complex, because more motors have to be

controlled and actuated at the same time.

If the robot has more than one leg there is the issue of leg

coordination for locomotion. The total number of possible gaits

in which a robot can travel depends on the number on legs it

has. The gait is a periodic sequence of lift and release events

for each leg. If a robot has k legs the number of possible events

N is, accordant to,

N=(2k-1)!

In case of a bipedal walking machine (k=2) the number of

possible events is

N=(2k-1)! = (2*2-1)! = 3! = 6

So there are six possible different events, these are

1. Lift left leg

2. Release left leg

3. Lift right leg

4. Release right leg

5. Lift both legs together

6. Release both legs together

In case of k=6 legs there are already 39916800 possible events,

in face of that, controlling a six legged robot is because of the

large number of possible events more complex than controlling

a two legged robot. But robots with fewer legs have some other

problems; one of the most complex problems is stability as

mentioned before. In the following different leg configurations,

advantages/disadvantages of these and examples of robots are

shown.

One leg

One leg is of course the minimum number of legs which a

legged robot can have. A smaller number of legs reduces body

mass of the robot and no leg coordination is needed. One-

legged locomotion requires just a single point of ground

contact; this makes the robot amenable to travel the roughest

terrain. As an example the robot is able to overcome an

obstacle like a gap that is larger than its stride by talking a

running start. A multi legged robot that cannot run is just able

to cross gaps that are as large as its reach. But the single point

of ground contact offers the main problem for single legged

robots – stability. Static stability is impossible even when the

robot is stationary, because the support polygon is reduced to a

single point. So singled legged robots must be dynamically

stable, that means that the robot has to actively balance itself

either by changing its Centre of gravity or by imparting

corrective forces. One of the first successful one-legged robots

was the one leg hopper form MIT, developed by Marc Raibert

in 1983.

Raibert’s hopper is not able to be stable when it is stationary,

so it has to hop all the time. To support locomotion and

stability there is of course the need of controlling the robot.

Raiberts’ hopper uses a simple controller, which divides the

control problem into three independent parts. These parts are

hopping height, velocity and attitude. [4]

• Hopping height: The control system controls hopping

height by manipulating hopping energy. The leg is

springy, so hopping is a bouncing motion that is

generated by an actuator (an external air-pressure

pump) that excites the leg. Hopping height is

determined by the energy recovered from the previous

hop, the losses in the hopping cycle and thrust

developed in the actuator. Height is regulated by

adjusting the amount of thrust on each cycle to just

make up for losses.

• Velocity: The control system manipulates forward

velocity by placing the foot with respect to the Centre

of the CG-print on each step. The CG-print is the locus

of points on the ground over which the Centre of

gravity of the system will pass during stance.

Displacing the foot from the Centre of the CG-print

causes the system to run either faster or slower. The

control system calculates the length of the CG-print

from the measured forward velocity and an estimate of

the duration of stance. The error in forward velocity

determines a foot position that will maintain the correct

speed of forward travel.

• Attitude: The control system maintains an erect body

posture during running, by generating hip torques

during stance that servo the body angle. During stance

friction between the foot and ground permits large

torques to be applied to the body without causing large

accelerations of the leg. These torques are used to

implement a simple proportional servo that moves the

body toward an erect posture once each step.

Two legs

Bipedal walking robots have become very popular in the last

ten years; two of the most well-known examples are QRIO

from Sony (Figure 7) [5] and Asimo from Honda (Figure 8)

[6]. Qrio has a weight of 7 kg and a height of 58 cm, each leg

has six degrees of freedom; Asimo has a weight of 210 kg, a

height of 1.82 cm and a maximum walking speed of 2 km/h,

each leg has six degrees of freedom.

Other robots are four legged and six legged.

Wheeled Locomotion

The most popular locomotion mechanism in man made

vehicles is wheeled locomotion; so it is not surprising that it is

often used in mobile robotics. Reasons for this are the easy

mechanical implementation of the wheel, there is no need of

balance control if the vehicle has at least three or in some case

two wheels and wheeled locomotion is relatively power

efficient, even at high speed. The problems of wheeled robots

are different from the problems of legged robots, as mentioned

before, stability is not such a profoundly problem like it is in

legged locomotion, but there are some others. The focus of

research in wheeled robotics is on traction and stability in

rough terrain, maneuverability and control.

Localization, Path Planning and Obstacle Avoidance

A key component of a mobile robot system is the ability to

localize itself accurately and, simultaneously, to build a map of

the environment. Localization, mapping, visual landmarks are

important aspects of mobile robotics. Mobile robot localization

and mapping, the process of simultaneously tracking the

position of a mobile robot relative to its environment and

building a map of the environment, has been a central topic

research in mobile robotics .Accurate localization is a

prerequisite for building good map, and having an accurate

map is essential for good localization. A basic requirement of a

mobile autonomous vehicle is path planning. The combination

of path-planning, obstacle avoidance, kinematic constraints and

uncertainty makes for a very hard problem indeed one which is

still an active area of research. However we can do some

interesting things if we decouple some of the issues and make

some simplifying assumptions. Mobile robotics may have

properties that be categorized into two classes holonomic and

non-holonomic. Holonomicity is the term used to describe the

locomotive properties of a mobile with respect to its

workspace. Mathematical definition of the term may be

described by use of mobile vehicle robotics .Vehicle is termed

as holonomic if the number of local degrees of freedom of

movement equals the number of global degrees of freedom

while a vehicle is non-holonomic if the global degrees of

freedom are motion in x, y and heading however locally, a car

can only move forward or turn. It cannot move slide sideways.

(Even the turning is coupled to motion). It should be obvious to

you that motion control for a holonomic vehicle is much easier

than for a non-holonomic vehicle. If this isn't obvious consider

the relative complexity of parking a car in a tight space

compared to driving a vehicle that can simply slide into the

space sideways (a hovercraft). Mobile robots have arbitrary

shapes and these shapes make for complicated interactions with

obstacles which we would like to simplify. One way to do this

is to transform the problem to one in which the robot can be

considered as a point-object and a technique called the

\Murkowski-Sum" does just this. The basic idea is to

artificially inflate the extent of obstacles to accommodate the

worst-case pose of the robot in close proximity. The idea is to

replace each object with a virtual object that is the union of all

poses of the vehicle that touch the obstacle has taken a

conservative approach and \replaced" a triangular vehicle with

a surrounding circle. The minimal Murkowski-Sum would be

the union of the obstacle and all vehicle poses with the vehicle

nose just touching it boundary. With the obstacles suitably

infrared the vehicle can be thought of a point-object and we

have a guarantee that as long as it keeps to the new, shrunken,

free space it cannot hit an object. Note it is usual to fit a

polygonal hull around the results of the Murkowski-Sum

calculation to make ensuing path planning

Calculations easier.

Feature Based Mapping and Localization

We apply the estimation techniques to two very important

mobile robotics tasks - Mapping and Localization. If for

example an autonomous vehicle is taken into consideration

these two tasks are important for its successful deployment. For

example

Mapping: Mapping managing autonomous open-cast

Mining, Battlefield Surveillance Cameras, Fracture detection x-

ray acoustic, Sub-sea Oil

AUV

Localization: GPS, Museum Guides, Hospital delivery system

A common way to approach these problems is to parameterize

both the robot pose and

Aspects of the environment's geometry into one or more state

vectors. Mainly 2D is discussed but the definitions that follow

are, of course, valid for the full 3D

Case.

Features and maps

We suppose that the world is populated by a set of discrete

landmarks or features whose location / orientation and

geometry (with respect to a defined coordinate frame) can be

described by a set of parameters which we lump into a feature

vector xf .We call a collection of n features a Map such that M

={ xf ,1; xf ,2; xf ,3…….. xf ,n}. To keep notation simple

sometimes we will use M to denote the map vector which is

simply the concatenation of all the features:

xf ,1

M xf ,2

= xf ,n

.

.

In this course we will constrain ourselves to using the simplest

feature possible - a point

feature such that for the ith feature:

xf ;i = xi

yi

1.1.1. Robot sensors

Robots with vision-based systems are quite complicated since

they must be equipped with the ability of detecting obstacle

and avoiding them while traversing in any environment. They

need to extract the desired information from the images taken

from a stream of the location or environment which for certain

consists of both stationary and moving obstacles by the robot

camera. The obstacle should be performed in real world

performance which makes it much more complex. Another

issue of obstacle avoidance is the fact that moving a mobile

robot to maneuver in an unknown environment is problematic

since there exist obstacles of all form and conditions.

Any information a robot collects about itself or its environment

requires sensing.

Robots that want to learn, map and/or navigate need to collect

information about their surroundings. All sensors have some

degree of uncertainty. Uncertainty can be reduced by multiple

measurements.

Two things to sense, Its own state (Proprioceptive)Motor

speed, battery voltage, joint angles and The world

(Exteroceptive); Everything and anything about the world

around itself. Two types of sensors Active (Project energy out

to measure its return) Passive (Sense the natural energy around

itself).

Improving Measurements: Improve calibration, Reduces

systematic errors, combining multiple measurements Reduces

effect of random errors, multiple measurements from single

sensor, and multiple measurements from different sensors. Not

all sensors just sense one thing.

Multiple measurements from the same sensor: Requires time,

latency; Introduces smoothing; has little effect on systematic

errors; Multiple measurements from different sensors; Can be

done simultaneously; Can reduce the effect of systematic

errors; requires more sensors

1.1.2. Robot vision

Vision in robots can be used to do: Facial recognition, object

classification, action recognition, Object tracking, image

labeling, scene reconstruction, Scene understanding, and image

alteration. The main goal is to do crowd tracking, image

dehazing, scene reconstruction, scene understanding, dealing

with the massive data, scene alteration, and recover projection.

Connectivity

Two conventions on considering two pixels next to each other.

8 point connectivity all pixels sharing a side or corner are

considered adjacent.

4 point connectivity only pixels sharing a side are considered

adjacent

To eliminate the ambiguity, we could define the shape of a

pixel to be a hexagon

Segmentation: Double Raster

Assume a binary image with values of 0 or 1

Initialize cntr to 0

Perform a raster scan – across and down

Encounter a pixel with a 1

Look up, look left

•If both 0

–Increment cntr by 1

–assign pixel P a value cnt

•If either is 1, assign P the label of the 1

•If both are 1

–Note equivalence

–Assign P’s label as minimum of 2

•2. Perform second raster scan to align equivalences

Stereo Vision

Is the extraction of 3D information from digital images, such as

obtained by a CCD camera. By comparing information about a

scene from two vantage points, 3D information can be

extracted by examination of the relative positions of objects in

the two panels. 3D stereo displays finds many applications in

entertainment, information transfer and automated systems.

Stereo vision is highly important in fields such as robotics, to

extract information about the relative position of 3D objects in

the vicinity of autonomous systems. Other applications for

robotics include object recognition, where depth information

allows for the system to separate occluding image components,

such as one chair in front of another, which the robot may

otherwise not be able to distinguish as a separate object by any

other criteria

Gaussian Masks

Are used to smooth images and for noise reduction .Used

before edge detection to avoid spurious edges.

1.1.3. KINEMATICS AND DYNAMICS

Kinematics is the process of determining the range of possible

movements for a robot, without consideration of the forces

acting on the robot, but taking into account the various

constraints on the motion. The kinematic equations for a robot

depend on the robot’s structure, i.e. the number of wheels, the

type of wheels used etc. Here, only the case of differentially

steered two wheeled robots will be considered. For balance, a

two-wheeled robot must also have one or several supporting

wheels (or some other form of ground contact, such as a ball in

a material with low friction). The influence of the supporting

wheels on the kinematics and dynamics will not be considered.

Dynamics

The kinematics considered in the previous section determines

the range of possible motions for a robot, given the constraints

which, in the case of the two wheeled differential robot,

enforce motion in the direction perpendicular to the wheel

axes. However, kinematics says nothing about the way in

which a particular motion is achieved. Dynamics, by contrast,

considers the motion of the robot in response to the forces (and

torques) acting on it. In the case of the two-wheeled,

differentially steered robot, the two motors generate torques (as

described above) that propel the wheels forward. The frictional

force at the contact point with the ground will try to move the

ground backwards. By Newton’s third law, a reaction force of

the same magnitude will attempt to move the wheel forward. In

addition to the torque _ from the motor (assumed to be known)

and the reaction force F from the ground, a reaction force _

from the main body of the robot will act on the wheel,

mediated by the wheel axis (the length of which is neglected in

this derivation).

1.1.4. Perception

A perception sensor for a mobile robot based on a

reconfigurable and flexible parallel architecture is developed.

Low-level image processing is implemented by using

reprogrammable FPGA technology, and high-level image

processing is performed by a dedicated reconfigurable parallel

architecture based on digital signal processor for example

TMS320C40. Hierarchical LINDA permits user-friendly

parallel programming and real-time application. To show that

our perception system can adapt to any application, we

undertake the parallelization of a 3D scene-reconstruction

algorithm using a geometric method, to meet the application

response time (10 Hz).

The basic configuration of a perception sensor hardware has

four main elements:

Image acquisition board;

Low-level image-processing board;

Medium- and high-level image-processing board;

Interconnection network board.

1.1.5. APPLICATIONS OF MOBILE ROBOTICS

Mobile robotics have various applications in the modern world

as compared to the past. In industries robots are replacing man

in various activities.

Application Example: Automated Inspection Robots

There are many applications of mobile robots, and their

importance in industrial processes continues to grow. Mobile

robots can be used for transportation tasks, surveillance, or

cleaning. Increasingly, they play an economic role also in the

entertainment industry (artificial pets being the best known

example).

One application of mobile robots of considerable economic

importance is that of automated inspection. Manual inspection

is a very costly process that is tedious to a human operator,

thereby increasing the risk that faults etc. are overlooked. It is

obvious that inspection would benefit from automation. A

major aspect of automated inspection is to detect abnormalities

automatically. Such novelty detection is hard to achieve with

classical machine learning methods, because those methods

typically require a balanced number of data points in all signal

classes to be classified. Yet by definition abnormalities are

rare, so that a classical machine learning algorithm cannot be

applied. A new approach to detect abnormality is to define

normality by some method, and then to compare all data points

with that measure of normality. Large deviations from \normal"

signals can then be aged as \novel". Kohen’s novelty Fillter

[7, 8] is an auto encoder neural network trained using back-

propagation of error, so that the network extracts the principal

components of the input. After training, any input presented to

the network produces one of the learned outputs, and the

bitwise difference between input and output highlights novel

components of the input. Other approaches include the manual

a priori definition of features to be detected to separate novel

perceptions from common ones [18].

It is possible to automate the acquisition of a model of

normality. In [10, 11] we present the implementation of an

automatic system for novelty detection, implemented on a

mobile robot and evaluated in unmodified \real world"

environments. Using the self-organising novelty Filter

presented there, Forty Two is able to construct a representation

of \normal" perceptions in its environment, and to detect novel

perceptions without prior knowledge installation.

Other areas of application include: Hazard Environments like

Inspection of hazard environments (catastrophic areas,

volcanoes, nuclear power plants, oil tanks) Inspection of gas or

oil pipes, and power transmission lines, Oil tank cleaning,

Construction and demolishing, Space exploration, Remote

inspection of space stations, Military, Surveillance vehicles,

Monitoring vehicles, Forests: Cleaning, fire preventing, tree

cutting, Material Handling: AGVs, SGVs, LGVs, Safety:

Surveillance of large areas, buildings, airports, car parking lots,

Civil Transportation: Inspection of airplanes, trains,

Entertainment: Robot Dog, Aibo – Robot dog from Sony,

Telepresence

Support to medical services – SERVICE ROBOTS,

Transportation of food, medication, medical exams,

Automation of pharmacy service. Automatic cleaning of (large)

areas:

• Supermarkets, airports, industrial sites

• Glass cleaning

• Domestic vacuum-cleaner

Client support:

• Museum tours, exhibitions guides

Agricultural:

• Fruit and vegetable picking, fertilization, planting

1.1.6. Challenges in Scientific Mobile Robotics

In the established sciences such as physics or chemistry, to

name but two examples, it is accepted practice that

experimental results are independently verified. To facilitate

this, precise (i.e. quantitative) descriptions of results are used.

Because research on quantitative descriptions of mobile robot

behavior is still in its infancy, mobile robotics to date is still an

empirical discipline that uses existence proofs extensively.

Robot systems to perform certain tasks are implemented, but,

for want of precise performance measures and behavioural

descriptions, are not independently verified. The first step

towards a science of mobile robotics, therefore, would be the

development of quantitative, rather than qualitative

descriptions of mobile robot behaviour. Some attempts have

been made to introduce quantitative evaluations to robotics.

Schoner et al. [16] use dynamical systems theory to investigate

robot environment interaction, and Smithers [17] discusses the

use of quantitative performance measures as a tool of scientific

mobile robotics research. In [13, 9, and 2] we present

quantitative evaluations of robot localisation systems (based on

contingency table analysis). Current work at Manchester

concentrates on the general, quantitative analysis of robot-

environment interaction, irrespective of the specific task

carried out or the control strategy used. This is done using

measures from chaos theory, [15] presents the approach and

results. In short, besides the technological challenges of mobile

robotics | fundamental sensor-motor competences, robot

navigation and application-oriented capabilities such as novelty

detection | the scientific challenge is to move mobile robotics

from a discipline of empirical practice towards a precise

science

CONCLUSION

Search and pursuit-evasion problems have recently become

central to many application domains in robotics, naturally

arising from the increased capabilities of autonomous agents.

Practical impact areas include: surveillance, emergency

response, and wilderness/ocean rescue. In keeping with its

interdisciplinary nature, robotics brings an applied context for

revisiting existing theoretical results, as well as inspiring new

ones. This paper has highlighted fundamental work in mobile

robotics as found in computer science, computer engineering,

and electronics and electrical and it has provided a deep

discussion on what a mobile robot is. In this technical research

paper various topics have been covered. Components that are

universal for building any mobile robot have been discussed

which includes sensors, vision, motion and communication.

Main topics under the research paper includes: Robot vision,

Robot sensors, Robot locomotion, navigation, mapping and

perception. Applications and the advantages of the mobile

robotics have led to the research and development of the

robots. Think of cargos without robots, space without robotic

satellites, and planets exploration without robots. Recent

discovery of the life in planet mass is as a result of the

investments done on robots. A series of experimental robots

named the Bearcats, have been constructed at the University of

Cincinnati over the past several years. This experience has

evolved into current, creative control design. Fortunately, our

intelligent robots have been able to use our increasingly

capable

computer controls in which multi-threaded, distributed

computing is now easily available.

The research has led to design intelligent robots that are

capable of adapting, learning

and predicting. This is a step toward understanding the

semiotic closure exhibited by biological

creatures and a further step toward appreciating the wonderful

capabilities of human intelligence

. So it will surely be interesting to consider the developments in

mobile robotics which are made in the next years.

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shrimp.php