Resilient and Low-Latency Networks
for Situation Awareness in the Factory of the Future
Timely and accurate situation awareness (e.g., knowing the state of agents such as people, objects, and vehicles) is a key enabler for automating the factory of the future (FoF). The Industry 4.0 paradigm foresees a myriad of heterogeneous agents that robustly and reliably communicate and coordinate their actions. FoF environments manifest harsh conditions for sensing and communication, resulting in information latency and data uncertainty. This may impair current state-of-the-art localization and decision-making procedures and calls for techniques that are robust to outdated and inaccurate information in FoF networks. The project will establish a new framework that revolutionizes the approach to localization and decision making by designing latency-aware and latency-resilient techniques for FoF environments.
This project includes two main aspects: latency-aware network localization and latency-resilient decision making in multi-agent FoF networks. First, the team of researchers is developing a framework for the design of latency-aware localization techniques. These techniques are robust to disruptive events by adapting accuracy-latency tradeoffs and by fusing data obtained from heterogeneous devices. Second, the team of researchers is developing latency-resilient decision-making algorithms. These algorithms are resilient to outdated information by efficiently allocating network resources and by coordinating the agents for accelerated learning. This project will serve as a foundation for large-scale multi-agent FoF networks that are capable of operating in highly dynamic environments in the presence of latency. The methodologies developed in this project are robust to outdated and inaccurate information, paving the way to fully automated industries in the next decade.