Distributed filtering over sensor networks for autonomous navigation of UAVs
Intelligent Service Robotics
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This article examines the problem of sensorless control for nonlinear dynamical systems with the use of derivative-free Extended Information Filtering EIF. The system is first subject to a linearisation transformation and next state estimation is performed by applying the standard Kalman Filter to the linearised model. At a second level, the standard Information Filter is used to fuse the state estimates obtained from local derivative-free Kalman filters running at the local information processing nodes. This approach has significant advantages because unlike the EIF i is not based on local linearisation of the nonlinear dynamics ii does not assume truncation of higher order Taylor expansion terms thus preserving the accuracy and robustness of the performed estimation and iii does not require the computation of Jacobian matrices. As a case study a robotic manipulator is considered and a cameras network consisting of multiple vision nodes is assumed to provide the visual information to be used in the control loop. A derivative-free implementation of the EIF is used to produce the aggregate state vector of the robot by processing local state estimates coming from the distributed vision nodes. The performance of the considered sensorless control scheme is evaluated through simulation experiments.