Multisensor Fusion and Integration seminar or presentation report
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ABSTRACT
Multisensor fusion and integration is a rapidly evolving research area. Multisensor fusion and integration refers to the combination of sensory data from multiple sensors to provide more accurate and reliable information.
The potential advantage of multisensor fusion and integration are redundancy, complementarity, timeliness and cost of the information.
Application of multisensor fusion and integration are also in the area of robotics, biomedical system, equipment monitoring, remote sensingand transportation system.

INTRODUCTION
Sensor is a device that detects or senses the value or changes of value of the variable being measured. The term sensor some times is used instead of the term detector, primary element or transducer.
The fusion of information from sensors with different physical characteristics, such as light, sound, etc enhances the understanding of our surroundings and provide the basis for planning, decision making, and control of autonomous and intelligent machines.

SENSORS EVOLUTION
A sensor is a device that responds to some external stimuli and then provides some useful output. With the concept of input and output, one can begin to understand how sensors play a critical role in both closed and open loops.
One problem is that sensors have not been specified. In other words they tend to respond variety of stimuli applied on it without being able to differentiate one from another. Neverthless, sensors and sensor technology are necessary ingredients in any control type application. Without the feedback from the environment that sensors provide, the system has no data or reference points, and thus no way of understanding what is right or wrong g with its various elements.
Sensors are so important in automated manufacturing particularly in robotics. Automated manufacturing is essentially the procedure of remo0ving human element as possible from the manufacturing process. Sensors in the condition measurement category sense various types of inputs, condition, or properties to help monitor and predict the performance of a machine or system.
SENSOR AND SENSOR TECHNOLOGY
IN THE PAST
The earliest example of sensors are not inanimate devices but living organisms. A more recent example of living organisms used in the early days of coal mining in the United States and Europe.
Robots must have the ability to sense and discriminate between objects. They must then be able to pick up these objects, position them properly and work with them without damaging or destroying them.
Intelligent system equipped with multiple sensors can interact with and operate in an unstructured environment without complete control of a human operator. Due to the fact that the system is operating in a totally unknown environment, a system may lack of sufficient knowledge concerning the state of the outside world. Storing large amounts of data may not be fesible. Considering the dynamically change world and unforeseen events, it is usually difficult to know the state of the world. Sensors can allow a system to learn the state of the world as needed and to cautiously update its own model of the world.
SENSORS PRINCIPLES
A sensor is defined as a measurement device which can detect characteristics of an object through some form of interaction with them.
Sensors can be classified into two categories:
Contact and noncontact. A contact sensor measure the response of a target to some form of physical contact .this group of sensors responds to touch, force ,torque,pressure,temperature or electrical quantities.
A noncontact type sensor measures the response brought by some form of electromagnetic radiation. This group of sensors responds to light, x-ray, acoustic, electric or magnetic radiation.

MULTISENSOR FUSION AND INTEGRATION
Multisensor integration is the synergistic use of the information provided by multiple sensory devices to assist in the accomplishment of a task by a system.
Multisensor fusion refers to any stage in the integration process where there is an actual combination of different sources of sensory information into one representational format.

MULTISENSOR INTEGRATION
The diagram represents multisensor integration as being a composite of basic functions. A group of n sensors provide input to the integration process. In order for the data from each sensor to be used for integration, it must first be effectively modelled. A sensor model represents the uncertainty and error in the data from each sensor and provides a measure of its quality that can be 7used by the subsequent integration functions.
After the data from each sensor has been modelled, it can be integrated into the operation of the system in accord with three different types of sensory processing: fusion, seperate operation, and guiding or cueing.
Sensor registration refers to any of the means used to make data from each sensor commensurate in both its spatial and temporal dimensions. If the data provided by a sensor is significantly different from that provided by any other sensors in the system, its influence on the operation of the sensors might be indirect. The separate operation of such a sensor will influence the other sensors indirectly through the effects he sensor has on the system controller and the world model. A guiding or cueing type sensory processing refers to the situation where the data from one sensor is used to guide or cue the operation of other sensors.
The results of sensory processing functions serve as inputs to the world model .a world model is used to store information concerning any possible state of the environment that the system is expected to be operating in. A world model can include both a priori information and recently acquired sensory information. High level reasoning processes can use the world model to make inferences that can be used to detect subsequent processing of the sensory information and the operation of the system controller.
Sensor selection refers to any means used to select the most appropriate configuration of sensors among the sensors available to the system.

MULTISENSOR FUSION
The fusion of data or information from multiple sensors or a single sensor over time can takes place at different levels of representation.
The different levels of multisensor fusion can be used to provide information to a system that can be used for a variety of purposes.eg signal level fusion can be used in real time application and can be considered as just an additional step in the overall processing of the signals, pixel level fusion can be used to improve the performance of many image processing tasks like segmentation ,and feature and symbol level fusion can be used to provide an object recognition system with additional features that can be used to increase its recognition capabilities.

APPLICATIONS OF MULTISENSOR FUSION AND INTEGRATION
In recent years, benefits of multisensor fusion have motivated research in a variety of application area as follows
1 Robotics
Robots with multisensor fusion and integration enhance their flexibility and productivity in industrial application such as material handling, part fabrication, inspection and assembly.
Mobile robot present one of the most important application areas for multisensor fusion and integration .When operating in an uncertain or unknown environment, integrating and tuning data from multiple sensors enable mobile robots to achieve quick perception for navigation and obstacle avoidance.
Marge mobile robot equipped with multiple sensors.perception, position location, obstacle avoidance vehicle control, path planning, and learning are necessary functions for an autonomous mobile robot.
Honda humanoid robot is equipped with an inclination sensor that consists of three accelerometer and three angular rate sensors. each foot and wrist is equipped with a six axis force sensor and the robot head contains four video cameras.multisensor fusion and integration of vision ,tactile,thermal,range,laser radar, and forward looking infrared sensors play a very important role for robotic system.

Honda humanoid robot

Anthrobot five-fingered robotic hand holding an object in the field of-view of a fixed camera.

MARGE mobile robot with a variety of sensors
Military application
It is used in the area of intelligent analysis, situation assessment, force command and control, avionics, and electronic warfare. It is employed for tracking targets such as missiles, aircrafts and submarines.
Remote sensing
Application of remote sensing include monitoring climate, environment, water sources, soil and agriculture as well as discovering natural sources and fighting the important of illegal drugs. Fusing or integrating the data from passive multispectral sensors and active radar sensors is necessary for extracting useful information from satellite or airborne imaginary.
Biomedical application
Multisensor fusion technique to enhance automatic cardiac rhythm monitoring by integrating electrocardiogram and hemodynamic signals. Redundant and complementary information from the fusion process can improve the performance and robustness for the detection of cardiac events including the ventricular activity and the atria activity.
Transportation system
Transportation system such as automatic train control system, intelligent vehicle and high way system, GSP based vehicle system, and navigation air craft landing tracking system utilize multisensor fusion technique to increase the reliability, safety, and efficiency.

FUTURE RESEARCH DIRECTIONS
The current state of the art in multisensor fusion is in continuous development. there are therefore, promising future research areas the encompass multilevel sensor fusion ,sensor fault detection, micro sensors and smart sensors, and adaptive multisensor fusion as follows.
Multilevel sensor fusion
Single level sensor fusion limits the capacity and robustness of a system, due to the weakness in uncertainity, missing observation, and incompleteness of a single sensor.therfore there is a clear need to integrate and fuse multisensor data for advanced system with high robustness and flexibility and the multilevel sensor fusion system is needed in advanced system.
There are different levels, low level fusion methods can fuse the multisensor data, and medium level fusion methods can fuse data and feature to obtain fused feature or decision. High level fusion methods can fuse feature and decision to obtain the final decision.
Fault detection
Fault detection has become a critical aspect of advanced fusion system design. Failures normally produce a change in the system dynamics and pose a significant risk. There are many innovative methods have been accomplished.
Micro sensors and smart sensors
Successful application of a sensor depends on sensor performance, cost and reliability.
However, a large sensor may have excellent operating characteristics but its marketability is severely limited by its size. Reducing the size of a sensor often increases its applicability through the following.
1 lower weight and greater portability
2 lower manufacturing cost and fewer materials
3 wider range of application.
Clearly, fewer materials are needed to manufacture a small sensor but the cost of materials processing is often a more significant factor. The revolution and semiconductor technology have enabled us to produce small reliable processors in the form of integrated circuits. The microelectronic applications have led to a considerable demand for small sensors or micro sensors that can fully exploit the benefits of IC technology. Smart sensors can integrate main processing, hardware and software. According to the definition proposed by Breckenridge and Husson, a smart sensor must possess three features
The ability to
Perform logical computable functions
Communicate with one or more other devices and
Make a decision using logic or fuzzy sensor data
Adaptive multisensor fusion
In general, multisensor fusion requires exact information about the sensed environment.however, in the real world, precise information about the sensed environment is scare and the sensors are not always perfectly functional.therfore a robust algorithm in the presence of various forms of uncertainty is necessary.
Researchers have developed adaptive multisensor fusion algorithm to address uncertainties associated with imperfect sensors.

CONCLUSION
Sensors play an n important role in our everyday life because we have a need to gather information and process it for some tasks. Successful application of sensor depends on sensor performance, cost and reliability.
The paradigm of multisensor fusion and integration as well as fusion techniques and sensor technologies are used in micro sensor based application in robotics, defense, remotesensing, equipment monitoring, biomedical engineering and transportation systems. Some directions for future research in multisensor fusion and integration target micro sensors and adaptive fusion techniques. This may be of interest to researches and engineers attempting to study the rapidly evolving field of multisensor fusion and integration.

BIBLIOGRAPHY
1. Ren.C.Luo, Fellow, IEEE Chin Chen Yih and Kuo Lan Su Multisensor Fusion And Integration: Approaches, Applications, and Future Research Directions, IEEE Sensors Journal, Vol 2 ,No 2 April 2002 pp 107-118
2. Encyclopedia of instrumentation and control pp 610
3. Paul Champan, Sensors Evolution, International Encyclopedia of robotics Application and Automation,vol 3 pp 1505- 1516
4. M . Rahimi and P.A Hancock, Sensors, Integration, International Encyclopedia of Robotics application
& Automation Vol 3 pp 1523- 1531
5. Kevin Hartwig, Sensors,Principles, International Encycloprdia of Robotics Application and Automation, Vol 3 pp 1532-1536

CONTENTS
INTRODUCTION
SENSORS EVOLUTION
SENSORS AND SENSOR TECHNOLOGY IN THE PAST
SENSORS, PRINCIPLES
MULTISENSOR FUSION AND INTEGRATION BLOCK DIAGRAM
MULTISENSOR INTEGRATION
MULTISENSOR FUSION
APPLICATIONS OF MULTISENSOR FUSION
FUTURE RESEARCH DIRECTIONS
CONCLUSION
BIBLIOGRAPHY





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26-09-2010, 06:54 PM

Multisensor Fusion and Integration


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.pdf   29324589-Multi-Sensor-Eigenspaces.pdf (Size: 111.29 KB / Downloads: 137)
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13-10-2010, 05:10 PM


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MULTI SENSOR INTEGRATION $ FUSION



ABSTRACT:
A sensor is a device that responds to some external stimuli and then provides some useful output. One problem is that sensors have not been specified. In other words they tend to respond variety of stimuli applied on it without being able to differentiate one from another. Neverthless, sensors and sensor technology are necessary ingredients in any control type application. Without the feedback from the environment that sensors provide, the system has no data or reference points, and thus no way of understanding what is right or wrong with its various elements. Sensors are so important in automated manufacturing particularly in robotics. Automated manufacturing is essentially the procedure of removing human element as possible from the manufacturing process. Sensors in the condition measurement category sense various types of inputs, condition, or properties to help monitor and predict the performance of a machine or system.

INTRODUCTION
Sensor is a device that detects or senses the value or changes of value of the variable being measured.
The term sensor some times is used instead of the term detector, primary element or transducer.
The advantage gained through agreeing use of multiple sensors:
-Redundancy
-Complementarity
-Timeliness
-Cost of information



TYPES OF SENSORS:
CONTACT SENSORS:
A contact sensor measure the response of a target to some form of physical contact .this group of sensors responds to touch, force, torque,pressure,temperature or electrical quantities.
NONCONTACT SENSOR:
A non contact type sensor measures the response brought by some form of electromagnetic radiation. This group of sensors responds to light, x-ray, acoustic, electric or magnetic radiation.
WHAT DOES MULTIPLE SENSORS MEAN?
-For these system to use effectively the multiple sensors ,we require some method for integrating the information provided by these sensors into the operation of the system.
-In many multi sensor system, the information from each sensor serve as the separate input to the system.
-The actual combination or fusion of information prior to its use in the system has been a particularly active area of research.
-Typical of the application that can benefit from the use of multiple sensor are industrial task like assembly, military command and for battlefield management.

MULTISENSOR INTEGRATION VS. FUSION
-Multi sensor integration refers to the agreeing use of the information provided by the multiple sensory devices to assist in the accomplishment of a task by a system.
-Multi sensor fusion refers to any stage in the integration process where there is an actual combination of different sources of the information into one representational format.
-This difference leads to the conclusion that in many multi sensor integrated system, The output from one sensor may be used to guide the operation of other sensor in the system without ever actually fusing the sensor information.
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Multisensor Fusion and Integration seminar and presentation report

MULTISENSOR FUSION AND INTEGRATION

Multisensor integration is the synergistic use of the information
provided by multiple sensory devices to assist in the
accomplishment of a task by a system.
Multisensor fusion refers to any stage in the integration
process where there is an actual combination of different sources of
sensory information into one representational format.




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MULTISENSOR INTEGRATION AND FUSION

Presented by:
Prince Garg

INTRODUCTION

Sensor is a device that detects or senses the value or changes of value of the variable being measured.
The term sensor some times is used instead of the term detector, primary element or transducer.
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Architecture
(US-JDL/UK-TFDF)
Feature Space
(Data representations, Task-specific, feedback)
Dimensionality
(Communication bandwidth constraints,High [ Low, increase SnR
Uncertainty
 Data, sensor, communication noise, high level ignorance, model uncertainty
 `soft’ decisions – Bayesian inference framework… but ….
 Incorrect use of independence between models [ Veto Effect
 Inaccurate estimation of probabilities can lead to severe distortion of decisions (product rule dominated by low probability errors)
 Simpler decision methods more robust
Dynamics
 Fusion is an iterative dynamical process - Continually refining estimates, representations ..
 How do constraints on communication bandwidth and processing limit architectures for fusion?
 How does the brain create and modify its data representation?
 How does the brain encode time, dynamics and use feedback?
 How does the brain encode and process probabilities and uncertain knowledge?
 Apart from very low level (cellular/subcellular) and very high level binding, the brain appears to leave data sources fragmented. Why?
 (interesting clinical exception in synaesthesia! – do we learn ICA?)
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INTRODUCTION
Sensor is a device that detects or senses the value or changes
of value of the variable being measured. The term sensor some
times is used instead of the term detector, primary element or
Transducer.
The fusion of information from sensors with different physical
characteristics, such as light, sound, etc enhances the
understanding of our surroundings and provide the basis for
planning,decision making, and control of autonomous and intelligent
machines.
A sensor is a device that responds to some external stimuli
and then provides some useful output. With the concept of input and
output, one can begin to understand how sensors play a critical role
in both closed and open loops.
One problem is that sensors have not been specified. In other
words they tend to respond variety of stimuli applied on it without
being able to differentiate one from another. Neverthless, sensors
and sensor technology are necessary ingredients in any control type
application. Without the feedback from the environment that sensors
provide, the system has no data or reference points, and thus no
way of understanding what is right or wrong g with its various
elements.
Sensors are so important in automated manufacturing
particularly in robotics. Automated manufacturing is essentially the
procedure of remo0ving human element as possible from the
manufacturing process. Sensors in the condition measurement
category sense various types of inputs, condition, or properties to
help monitor and predict the performance of a machine or system.


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HYBRID BEHAVIOURAL ARCHITECTURE
The behavioural approach is based on the existence of
individual behaviours and on the coordination of
these behaviours. It states: autonomy emerges from
the co-operative work of various behaviours [l].
We define a behaviour as an independent stereotyped
action that is maintained by a specific perceived
stimulus. Examples of robot navigation behaviours are
Wonder arortnd or Go along. Each behaviour is activated
by a stimulus. The overall agent behaviour emerges
from the coordination of the various active behaviours.
The behavioural approach is of special interest for
building agents which interact strongly with their
environment by sensing.
Figure 2 illustrates the basics of a the behavioural
architecture which derives from this approach. We
recognise a structure organised in three levels of
abstraction:
Level 1: physical
Level 2: behavioural
Level 3: cognitive
Level 1 includes all devices for acting on the robot and
for sensing the environment.
Level 2 is the heart of the architecture and implements
the behaviours as independent modules.
Level 3 includes units which implement the tasks to be
performed by the agent. The units proceed by
activation/deactivation of the behaviours. Units can be
built according to very different principles [6].
In the frame of this paper, we select the principle of a
task described by a state machine whose transitions
are controlled by a status vector and each state gives
rise to an activation vector. Status and activation
vectors refer to the signals from and to the behaviours
respectively (figure 2). Therefore, each unit is an
automaton. Because of this combination of behaviours
and tasks expressed as state machines we call the
architecture hybrid.The described behavioural architecture provides
advantages by its intrinsic features like concurrency of
several behaviours, simplicity of design, abstraction
hierarchy, time response hierarchy, a physically
grounded system [ 11.
VISION IN THE BEHAVIOURAL ARCHITECTURE
The integration of vision in the behavioural
architecture requires operations at all three levels:
defining vision devices at level 1, defining visionbased
behaviours at level 2 and, at level 3, defining the
tasks in term of behaviours, making best use of the
visual behaviours available. Let us consider the three
levels successively.
At level one, defining vision devices consists merely in
selecting a number of vision devices needed or useful
for the application. Vision devices that have been
considered so far in our investigations belong to active
and passive vision, landmark vision, laser range
imaging, sonar and infrared ranging.
At level two and of central concern is the definition of
vision-based behaviours. Each behaviour is defined
and developed as a widely autonomous unit
responsible for a stereotyped action the robot performs
under the control of a stimulus. In vision-based
behaviours the stimulus is a visual pattern. Upon
detection the stimulus initiates a robot action and
maintains it as long as it exists, building up a control
loop with feedback across the environment.
The sorts of behaviours to be implemented are selected
according to sensor capabilities and application
requirements. Typical behaviours considered for
vision-based navigation are: Going tou~nrifsG, oin8 d ong
right, Obstacle avoidance, Obstncle detection, Lnnilrnnrk
following, Wonder nrounii, Hornins, Self-yositionin<ye, tc.
Finally, tasks the robot has to perform are defined at
level three. Each task is packaged in a unit. Typicdl
tasks considered so far are: exploration, navigation,
postman task, tidying up chairs in a room [6][S].


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