Bio-Inspired Event-Based Visual Servoing for Ground Robots

TL;DR

This paper presents a bio-inspired, event-based visual servoing framework for ground robots using Dynamic Vision Sensors, enabling low-latency, efficient control without traditional state estimation.

Contribution

It introduces a novel 1D event-based visual servoing method leveraging biological principles, with theoretical bounds and active sensing strategies validated on a ground robot.

Findings

Demonstrated effective velocity and position-velocity estimation from event flux.

Validated low-latency, computationally efficient control on a scaled ground robot.

Established theoretical bounds for event rate estimators in structured environments.

Abstract

Biological sensory systems are inherently adaptive, filtering out constant stimuli and prioritizing relative changes, likely enhancing computational and metabolic efficiency. Inspired by active sensing behaviors across a wide range of animals, this paper introduces a principled 1D event-based visual servoing framework for ground robots operating in structured environments. Utilizing a Dynamic Vision Sensor (DVS), we demonstrate that by applying a fixed spatial kernel to the asynchronous event stream generated from structured logarithmic intensity-change patterns, the resulting net event flux analytically isolates specific combinations of kinematic states. We establish a generalized theoretical bound for this event rate estimator and show that linear and quadratic spatial profiles isolate the robot's velocity and position-velocity product, respectively. Leveraging these properties, we employ a multi-pattern stimulus to directly synthesize a nonlinear state feedback term entirely without traditional state estimation. To overcome the inescapable loss of linear observability at equilibrium inherent in event sensing, we propose a bio-inspired active sensing limit-cycle controller. Experimental validation on a 1/10-scale autonomous ground vehicle confirms the efficacy, extreme low-latency, and computational efficiency of the proposed direct-sensing approach.