Decentralized Multi-agent Filtering

TL;DR

This paper proposes an extension to the Bayes filter for decentralized multi-agent localization, incorporating greedy belief sharing to enhance local estimates in grid-world scenarios.

Contribution

It introduces a novel decentralized filtering approach with belief sharing, improving multi-agent localization accuracy in discrete state spaces.

Findings

Belief sharing improves localization accuracy.

The method is effective in grid-world multi-agent scenarios.

Code implementation is publicly available.

Abstract

This paper addresses the considerations that comes along with adopting decentralized communication for multi-agent localization applications in discrete state spaces. In this framework, we extend the original formulation of the Bayes filter, a foundational probabilistic tool for discrete state estimation, by appending a step of greedy belief sharing as a method to propagate information and improve local estimates' posteriors. We apply our work in a model-based multi-agent grid-world setting, where each agent maintains a belief distribution for every agents' state. Our results affirm the utility of our proposed extensions for decentralized collaborative tasks. The code base for this work is available in the following repo