An Information Roadmap Method for Robotic Sensor Path Planning
Journal of Intelligent and Robotic Systems
Information-driven search strategies in the board game of CLUE®
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information-driven sensor path planning by approximate cell decomposition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Active learning and basis selection for kernel-based linear models: a Bayesian perspective
IEEE Transactions on Signal Processing
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A mobile electromagnetic-induction (EM I) sensor is considered for detection and characterization of buried conducting and/or ferrous targets. The sensor maybe placed on a robot and, here, we consider design of an optimal adaptive-search strategy. A frequency-dependent magnetic-dipole model is used to characterize the target at EMI frequencies. The goal of the search is accurate characterization of the dipole-model parameters, denoted by the vector Θ; the target position and orientation are a subset of Θ. The sensor position and operating frequency are denoted by the parameter vector p and a measurement is represented by the pair (p, O), where O denotes the observed data. The parameters p are fixed for a given measurement, but, in the context of a sequence of measurements p may be changed adaptively. In a locally optimal sequence of measurements, we desire the optimal sensor parameters, pN+1 for estimation of Θ, based on the previous measurements (pn, On)n=1,N. The search strategy is based on the theory of optimal experiments, as discussed in detail and demonstrated via several numerical examples.