Containing a balance of theory and practical information, this title explores developing scalable, decentralized estimation and control algorithms for linear and nonlinear multisensor systems. It helps remove limitations within decentralized data fusion algorithms and extend the principle to problems involving local control and actuation.
Decentralized Estimation and Control for
Multisensor Systems explores the problem of developing scalable, decentralized estimation and control algorithms for linear and nonlinear multisensor systems. Such algorithms have extensive applications in modular robotics and complex or large scale systems, including the Mars Rover, the Mir station, and Space Shuttle Columbia.
Most existing algorithms use some form of hierarchical or centralized structure for data gathering and processing. In contrast, in a fully decentralized system, all information is processed locally. A decentralized data fusion system includes a network of sensor nodes - each with its own processing facility, which together do not require any central processing or central communication facility. Only node-to-node communication and local system knowledge are permitted.
Algorithms for decentralized data fusion systems based on the linear information filter have been developed, obtaining decentrally the same results as those in a conventional centralized data fusion system. However, these algorithms are limited, indicating that existing decentralized data fusion algorithms have limited scalability and are wasteful of communications and computation resources.
Decentralized Estimation and Control for
Multisensor Systems aims to remove current limitations in decentralized data fusion algorithms and to extend the decentralized principle to problems involving local control and actuation.
The text discusses:
Generalizing the linear Information filter to the problem of estimation for nonlinear systems
Developing a decentralized form of the algorithm
Solving the problem of fully connected topologies by using generalized model distribution where the nodal system involves only locally relevant states
Reducing computational requirements by using smaller local model sizes
Defining internodal communication
Developing estimation algorithms for different models
Applying the decentralized algorithms to the problem of decentralized control
Demonstrating the theory to a modular wheeled mobile robot, a vehicle system with nonlinear kinematics and distributed means of acquiring information
Extending the applications to other robotic systems and large scale systems
Decentralized Estimation and Control for
Multisensor Systems addresses how decentralized estimation and control systems are rapidly becoming indispensable tools in a diverse range of applications - such as process control systems, aerospace, and mobile robotics - providing a self-contained, dynamic resource concerning electrical and mechanical engineering.
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