Discovering and exploiting active sensing motifs for estimation

TL;DR

This paper introduces BOUNDS and AI-KF, novel methods for discovering and exploiting active sensing motifs in nonlinear systems, demonstrated on a quadcopter to improve environmental and state estimates.

Contribution

The paper presents BOUNDS, a systematic method for designing active sensing movements, and AI-KF, a dynamic fusion estimator, advancing active sensing in autonomous systems.

Findings

Active movements improve ground speed, altitude, and wind direction estimates.

BOUNDS effectively discovers movement motifs that enhance sensory information.

AI-KF dynamically fuses neural network and model-based estimates.

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.