Reaching a Consensus with Limited Information

TL;DR

This paper investigates how autonomous agents can reach consensus when communication is limited to linear functions of their states rather than full state information, and develops algorithms for specific cases.

Contribution

It introduces algorithms for consensus with limited information transfer, where agents receive linear functions of neighbors' states instead of full state data.

Findings

Algorithms successfully achieve consensus in specified scenarios.

Proven correctness of the proposed algorithms.

Addresses the challenge of limited information exchange in networked systems.

Abstract

In its simplest form the well known consensus problem for a networked family of autonomous agents is to devise a set of protocols or update rules, one for each agent, which can enable all of the agents to adjust or tune their "agreement variable" to the same value by utilizing real-time information obtained from their "neighbors" within the network. The aim of this paper is to study the problem of achieving a consensus in the face of limited information transfer between agents. By this it is meant that instead of each agent receiving an agreement variable or real-valued state vector from each of its neighbors, it receives a linear function of each state instead. The specific problem of interest is formulated and provably correct algorithms are developed for a number of special cases of the problem.