Bio-Inspired Event-Based Visual Servoing for Ground Robots
This work addresses visual servoing for ground robots by introducing a bio-inspired event-based approach, offering incremental improvements in efficiency and latency over traditional methods.
The paper tackled the problem of visual servoing for ground robots by developing an event-based framework using a Dynamic Vision Sensor, which directly synthesizes nonlinear state feedback without traditional state estimation, achieving extreme low-latency and computational efficiency in experiments on an autonomous ground vehicle.
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 presents a novel event-based visual servoing framework for ground robots. 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 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.