Controlling rigid formations of mobile agents under inconsistent measurements

TL;DR

This paper introduces a robust, distributed gradient control method using local estimators to eliminate undesirable collective motions caused by measurement inconsistencies in rigid formations of autonomous agents.

Contribution

It develops a systematic estimator-based gradient control that enhances robustness against measurement inconsistencies in rigid formation control.

Findings

Guarantees exponential convergence of the formation control

Effectively eliminates inconsistency-induced collective motions

Validated through robotic experiments and simulations

Abstract

Despite the great success of using gradient-based controllers to stabilize rigid formations of autonomous agents in the past years, surprising yet intriguing undesirable collective motions have been reported recently when inconsistent measurements are used in the agents' local controllers. To make the existing gradient control robust against such measurement inconsistency, we exploit local estimators following the well known internal model principle for robust output regulation control. The new estimator-based gradient control is still distributed in nature and can be constructed systematically even when the number of agents in a rigid formation grows. We prove rigorously that the proposed control is able to guarantee exponential convergence and then demonstrate through robotic experiments and computer simulations that the reported inconsistency-induced orbits of collective movements are effectively eliminated.