arXiv subjects

Benjamin Cellini

Publications and source records attributed to Benjamin Cellini.

2 recordsLinked to original sources

arXiv2025

Discovering and exploiting active sensing motifs for estimation

From organisms to machines, autonomous systems rely on measured sensory cues to estimate unknown information about themselves or their environment. For nonlinear systems, strategic sensor motion can be leveraged to extract otherwise inaccessible information. This principle, known as active sensing, is widespread in biology yet difficult to study, and remains underutilized in engineered systems due to the challenge of systematically designing active sensing motifs. Here, we introduce the method ``BOUNDS: Bounding Observability for Uncertain Nonlinear Dynamic Systems", and Python package pybounds, which can discover movement motifs that increase the information encoded in sensory cues. To exploit sporadic estimates from bouts of active sensing, we further introduce the Augmented Information Kalman Filter (AI-KF). The AI-KF uses insight from BOUNDS to dynamically fuse neural network and model-based estimation. We demonstrate BOUNDS and the AI-KF on a flying agent model and experimental GPS-denied data from a quadcopter, revealing how specific active movements improve estimates of ground speed, altitude, and wind direction. Altogether, our work will prove useful for designing sensor-minimal autonomous systems and investigating active sensing in living organisms.

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arXiv2023

Empirical Individual State Observability

A dynamical system is observable if there is a one-to-one mapping from the system's measured outputs and inputs to all of the system's states. Analytical and empirical tools exist for quantifying the (full state) observability of linear and nonlinear systems; however, empirical tools for evaluating the observability of individual state variables are lacking. Here, a new empirical approach termed Empirical Individual State Observability (E-ISO) is developed to quantify the level of observability of individual state variables. E-ISO first builds an empirical observability matrix via simulation, then applies convex optimization to efficiently determine the subset of its rows required to estimate each state variable individually. Finally, (un)observability measures for these subsets are calculated to provide independent estimates of the observability of each state variable. Multiple example applications of E-ISO on linear and nonlinear systems are shown to be consistent with analytical results. Broadly, E-ISO will be an invaluable tool both for designing active sensing control laws or optimizing sensor placement to increase the observability of individual state variables for engineered systems, and analyzing the trajectory decisions made by organisms.

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