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Probabilistic tracking and real-time assessment of physical systems

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https://ir.library.oregonstate.edu/concern/defaults/8049gd75w

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  • In order for long-range autonomous robots to be successful they must be able to maintain accurate information about their location, available resources, and the state of critical components. We propose here a methodology which incorporates traditional, sensor-based tracking methods with discrete probabilistic representations of system state. Further, we extend the use of the Gaussian distribution to include a richer set of mathematical descriptions of system performance under specific failure conditions. The extended representations are then used to statistically test for these failure conditions by predicting the most likely values for observable parameters given the system state. This technique is then combined with first-order extended Kalman filtering to yield a probabilistic framework for tracking and fault detection in domains with non-linear dynamics.
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