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An algorithm is presented for the automated analysis of rotating probe multifrequency eddy current data obtained from nuclear power plant steam generator tubes (SGT). The algorithm consists of four steps, namely, a preprocessing stage for conditioning the data, a decision tree based feature extraction stage for identifying relevant features for analysis, a neural network based classification stage for identifying signals from various defect types and benign structures, and finally a blind deconvolution based characterization stage for accurately estimating the size and orientation of the detected defects. This algorithm is optimized to maximize the probability of detection (POD), while keeping the number of false alarms (PFA) at a minimum. Initial results presented in this paper look very promising and demonstrate the effectiveness of the proposed algorithm.

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In this paper, we present a solution to the problem of coordinating multiple robots across a communication channel that experiences delays. The proposed approach leverages control barrier functions in order to ensure that the multi-robot system remains dissipative. This is achieved by encoding the dissipativity-preserving condition as a set invariance constraint. This constraint is then included in an optimization problem, whose objective is that of modifying, in a minimally invasive fashion, the nominal input to the robots. The formulated optimization problem is decentralized in the sense that, in order to be solved, it does not require the individual robots to have access to global information. Moreover, owing to its convexity, each robot can solve it using fast and efficient algorithms. The effectiveness of the proposed control framework is demonstrated through the implementation of a formation control algorithm in presence of delays on a team of mobile robots.

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