Robust Distributed Control within a Curve Virtual Tube for a Robotic Swarm under Self-Localization Drift and Precise Relative Navigation

TL;DR

This paper presents a robust distributed control method for robotic swarms navigating within a curve virtual tube, effectively handling self-localization drift and navigation errors through flocking algorithms and a modified vector field controller, validated by simulations and experiments.

Contribution

It introduces a novel control approach that combines flocking and a modified vector field controller to mitigate localization drift effects in swarm navigation.

Findings

Flocking reduces the impact of position measurement drift.

Velocity alignment mitigates velocity measurement errors.

Numerical simulations and real experiments confirm effectiveness.

Abstract

To guide the movement of a robotic swarm in a corridor-like environment, a curve virtual tube with no obstacle inside is designed in our previous work. This paper generalizes the controller design to the condition that all robots have self-localization drifts and precise relative navigation, where the flocking algorithm is introduced to reduce the negative impact of the self-localization drift. It is shown that the cohesion behavior and the velocity alignment behavior are able to reduce the influence of the position measurement drift and the velocity measurement error, respectively. For the convenience in practical use, a modified vector field controller with five control terms is put forward. Finally, the effectiveness of the proposed method is validated by numerical simulations and real experiments.