Project Overview

Modern multi-agent autonomous systems, particularly in defense scenarios involving heterogeneous drones (UAVs, USVs, UUVs), must operate effectively in communication-impaired and contested environments. In these scenarios, conflicts frequently arise from disagreements between agents about information and goals—a problem that standard consensus algorithms cannot solve. The Sheaf Enriched Autonomous Multi-Agent Networks (SEAMAN) project will address this critical challenge by developing a groundbreaking mathematical framework for robust task distribution and conflict resolution. The core of this approach is a tightly-integrated framework utilizing enriched categories to create a new mathematical syntax for tasks and preferences, and cellular sheaves to model task compatibility and ensure local-to-global consistency across the agent network. This will enable decentralized and asynchronous coordination even with intermittent or sparse communication. The project will culminate in a proof-of-concept demonstration, validating the developed algorithms on unmanned ground vehicles (UGVs) and benchmarking their performance against state-of-the-art multi-agent reinforcement learning (MARL) techniques.

Funding Info

Agency: Defense Advanced Research Projects Agency
Program Manager: Col. Nikesh Kapadia / Christopher Absil
Grant Number: HR0011-25-3-0235
My Role: Principal Investigator
Amount: $180,687 USD
Project Period: Aug 2025 - Aug 2026