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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 provides the basis for planning, decision making, and control of autonomous and intelligent machines. Data fusion techniques combine data from multiple sensors, and related information from associated databases, to achieve improved accuracies and more specific inferences than could be achieved by the use of a single sensor alone. The concept of multisensor data fusion is hardly new. Humans and animals have evolved the capability to use multiple senses to improve their ability to survive. For example, it may not be possible to assess the quality of an edible substance based solely on the sense of vision or touch, but evaluation of edibility maybe achieved using a combination of sight, touch, smell, and taste. Similarly, while one is unable to see around comers or through vegetation, the sense of hearing can provide advanced warning of impending dangers. Thus multisensory data fusion is naturally performed by animals and humans to achieve more accurate assessment of the surrounding environment and identification of threats, thereby improving their chances of survival.
2.0 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. Nevertheless, 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.
3.0 SENSOR AND SENSOR TECHNOLOGY
IN THE PAST
The earliest example of sensors is 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 feasible. 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.
4.0 SENSORS PRINCIPLES
A sensor is defined as a measurement device which can detect characteristics of an object through some form of interaction with them. It is a device that measures a physical quantity and converts it into a signal which can be read by an observer or by an instrument. For example, a mercury-in-glass thermometer converts the measured temperature into expansion and contraction of a liquid which can be read on a calibrated glass tube. A thermocouple converts temperature to an output voltage which can be read by a voltmeter. For accuracy, all sensors need to be calibrated against known standards.
A good sensor obeys the following rules:
Â¢ Is sensitive to the measured property
Â¢ Is insensitive to any other property
Â¢ Does not influence the measured property
Ideal sensors are designed to be linear. The output signal of such a sensor is linearly proportional to the value of the measured property. The sensitivity is then defined as the ratio between output signal and measured property. For example, if a sensor measures temperature and has a voltage output, the sensitivity is a constant with the unit [V/K]; this sensor is linear because the ratio is constant at all points of measurement.
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.
5.0 IDEAL SENSOR
An ideal sensor should possess the following properties
1. Continuous operation without effecting the measurand.
2. Appropriate sensitivity and selectivity.
3. Fast and predictable response.
4. Reversible behavior.
5. High signal to noise ratio.
7. Immunity to environment.
8. Easy to calibrate.
5.1 SENSOR DEVIATIONS
If the sensor is not ideal, several types of deviations can be observed:
Â¢ The sensitivity may in practice differ from the value specified. This is called a sensitivity error, but the sensor is still linear.
Â¢ Since the range of the output signal is always limited, the output signal will eventually reach a minimum or maximum when the measured property exceeds the limits. The full scale range defines the maximum and minimum values of the measured property.
Â¢ If the output signal is not zero when the measured property is zero, the sensor has an offset or bias. This is defined as the output of the sensor at zero input.
Â¢ If the sensitivity is not constant over the range of the sensor, this is called nonlinearity. Usually this is defined by the amount the output differs from ideal behavior over the full range of the sensor, often noted as a percentage of the full range.
Â¢ If the deviation is caused by a rapid change of the measured property over time, there is a dynamic error. Often, this behavior is described with a bode plot showing sensitivity error and phase shift as function of the frequency of a periodic input signal.
Â¢ If the output signal slowly changes independent of the measured property, this is defined as drift (telecommunication).
Â¢ Long term drift usually indicates a slow degradation of sensor properties over a long period of time.
Â¢ Noise is a random deviation of the signal that varies in time.
Â¢ Hysteresis is an error caused by when the measured property reverses direction, but there is some finite lag in time for the sensor to respond, creating a different offset error in one direction than in the other.
Â¢ If the sensor has a digital output, the output is essentially an approximation of the measured property. The approximation error is also called digitization error.
Â¢ If the signal is monitored digitally, limitation of the sampling frequency also can cause a dynamic error.
Â¢ The sensor may to some extent be sensitive to properties other than the property being measured. For example, most sensors are influenced by the temperature of their environment.
All these deviations can be classified as systematic errors or random errors. Systematic errors can sometimes be compensated for by means of some kind of calibration strategy. Noise is a random error that can be reduced by signal processing, such as filtering, usually at the expense of the dynamic behavior of the sensor.
The resolution of a sensor is the smallest change it can detect in the quantity that it is measuring. Often in a digital display, the least significant digit will fluctuate, indicating that changes of that magnitude are only just resolved. The resolution is related to the precision with which the measurement is made. For example, a scanning tunneling probe (a fine tip near a surface collects an electron tunnelling current) can resolve atoms and molecules.
6.0 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.Multi-sensor integration is still a matter of research in many areas. A major aim was to create a system, which is open for any kind and any number of sensors while providing a uni.ed programming interface. Manipulation primitives constitute such an interface that enables programmers to specify sensor-guided and sensorguarded motion commands in an intuitive way.The complete system has been implemented as prototype for industrial use, i.e. all results are practical ones and not only simulation results.
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.Sensor fusion is the combining of sensory data or data derived from sensory data from disparate sources such that the resulting information is in some sense better than would be possible when these sources were used individually. The term better in that case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, such as stereoscopic vision (calculation of depth information by combining two-dimensional images from two cameras at slightly different viewpoints).
The data sources for a fusion process are not specified to originate from identical sensors. One can distinguish direct fusion, indirect fusion and fusion of the outputs of the former two. Direct fusion is the fusion of sensor data from a set of heterogeneous or homogeneous sensors, soft sensors, and history values of sensor data, while indirect fusion uses information sources like a priori knowledge about the environment and human input.
6.1 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.
6.2 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.
7.0 APPLICATIONS OF MULTISENSOR FUSION AND INTEGRATION
In recent years, benefits of multisensor fusion have motivated research in a variety of application area as followsÂ¦.
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.
Fig 1: Honda humanoid robot
Fig 2: Anthrobot five-fingered robotic hand holding an object in the field of-view of a fixed camera.
Fig 3:MARGE mobile robot with a variety of sensors
7.2 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.
7.3 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.
7.4 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.
7.5 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.
Table 1: Defense applications
Table 2: Non Defense applications
8.0 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.
8.1 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.
8.2 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.
8.3 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
8.4 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.
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.
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
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. It requires interdisciplinary knowledge in control theory, signal processing, artificial intelligence, probability and statistics, etc. The advantages gained through the use of redundant, complementary, or more timely information in a system can provide more reliable and accurate information. This paper provides an overview of current sensor technologies and describes the paradigm of multisensor fusion and integration as well as fusion techniques at different fusion levels. Applications of multisensor fusion in robotics, biomedical system, equipment monitoring, remote sensing, and transportation system are also discussed. Finally, future research directions of multisensor fusion technology including microsensors, smart sensors, and adaptive fusion techniques are presented
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