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• W1569405867 abstract "State estimation over communication networks is in use by many robotic applications in industry, in defense systems, as well as in several exploration and surveillance tasks. The incorporation of a communication network in the control loop has enabled to perform multi-sensor fusion and distributed information processing, thus improving significantly the autonomy and reliability of robotic systems (Medeiros et al., 2008), (Olfati-Saber, 2006), (Watanabe & Tzafestas, 1992). It has been shown that scalable distributed state estimation can be achieved for robotic models, when the measurements are linear functions of the state and the associated process andmeasurement noise models follow aGaussian distribution (Mahler, 2007), (Nettleton et al., 2003). The results have been also extended to the case of nonlinear non-Gaussian dynamical systems (Rigatos, 2010a), (Makarenko & Durrant-Whyte, 2006). An issue which is associated to the implementation of such networked control systems is how to compensate for randomdelays and packet losses so as to enhance the accuracy of estimation and consequently to improve the stability of the control loop. The idea of incorporating delayed measurements within a Kalman Filter framework is a possible solution for the compensation of network-induced delays and packet losses, and is also known as update with out-of-sequence measurements (Bar Shalom, 2002). The solution proposed in (Bar Shalom, 2002) is optimal under the assumption that the delayed measurement was processed within the last sampling interval (one-step-lag problem). There have been also some attempts to extend these results to nonlinear state estimation (Golapalakrishnan et al., 2011), (Jia et al., 2008). More recently there has been research effort in the redesign of distributed Kalman Filtering algorithms for linear systems so as to eliminate the effects of delays in measurement transmissions and packet drops, while also alleviating the one-step-lag assumption (Xia et al., 2009). This chapter presents an approach to distributed state estimation-based control of nonlinear systems, capable of incorporating delayed measurements in the estimation algorithm while being also robust to packet losses. First, the chapter examines the problem of distributed nonlinear filtering over a communication/sensors network, and the use of the estimated state vector in a control loop. As a possible filtering approach, the Extended Information Filter is proposed (Rigatos, 2010a). In the Extended Information Filter the local filters do not exchange rawmeasurements but send to an aggregation filter their local information matrices (local inverse covariance matrices which can be also associated to Fisher Information Matrices) and their associated Distributed Nonlinear Filtering Under Packet Drops and Variable Delays for Robotic Visual Servoing 5" @default.
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• W1569405867 date "2011-06-09" @default.
• W1569405867 modified "2023-10-18" @default.
• W1569405867 title "Distributed Nonlinear Filtering Under Packet Drops and Variable Delays for Robotic Visual Servoing" @default.
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• W1569405867 doi "https://doi.org/10.5772/17460" @default.
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