Multisensor Fusion and Integration
Published on Jan 07, 2020
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.
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.
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.
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
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.
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