Year: 2025
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Institution: University of Nevada, Reno
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G3398237449, G4689434848, EFRI BRAID: Resilient autonomous navigation inspired by the insect central complex and sensorimotor control motifs, AI Institute in Dynamic Systems, Kavli Institute for Theoretical Physics, The Functional Role and Neural Basis of Rapid Turns in Olfactory Search Behavior
OpenAlex ID: W4416235040
Cites:
Cited by:
Related to: