Decentralized Multi-agent Filtering
This work addresses localization challenges for multi-agent systems with decentralized communication, but it appears incremental as it builds on existing Bayes filter methods.
The paper tackles the problem of decentralized multi-agent localization in discrete state spaces by extending the Bayes filter with greedy belief sharing to improve local estimates, and demonstrates its utility in a model-based grid-world setting.
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