# Wireless communication empowers online scheduling of partially-observable transportation multi-robot systems in a smart factory

arXiv: https://arxiv.org/abs/2603.23967 · HTML: https://mlsanity.com/paper/2603.23967/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [Integrated Task Assignment and Path Planning for Capacitated Multi-Agent Pickup and Delivery](https://mlsanity.com/paper/2110.14891/) (2021)
- [Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems](https://mlsanity.com/paper/2112.10948/) (2021)
- [Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities](https://mlsanity.com/paper/2412.14538/) (2024)
- [Multi-Goal Multi-Agent Pickup and Delivery](https://mlsanity.com/paper/2208.01223/) (2022)
- [A Survey on Integrated Sensing, Communication, and Computation](https://mlsanity.com/paper/2408.08074/) (2024)
- [IEEE 802.11be-Wi-Fi 7: New Challenges and Opportunities](https://mlsanity.com/paper/2007.13401/) (2020)
- [Federated Deep Reinforcement Learning for RIS-Assisted Indoor Multi-Robot Communication Systems](https://mlsanity.com/paper/2207.08056/) (2022)
- [Task-Oriented Communication Design at Scale](https://mlsanity.com/paper/2305.08481/) (2023)
- [Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots](https://mlsanity.com/paper/2011.11745/) (2020)

## Abstract (arXiv)

Achieving agile and reconfigurable production flows in smart factories depends on online multi-robot task assignment (MRTA), which requires online collision-free and congestion-free route scheduling of transportation multi-robot systems (T-MRS), e.g., collaborative automatic guided vehicles (AGVs). Due to the real-time operational requirements and dynamic interactions between T-MRS and production MRS, online scheduling under partial observability in dynamic factory environments remains a significant and under-explored challenge. This paper proposes a novel communication-enabled online scheduling framework that explicitly couples wireless machine-to-machine (M2M) networking with route scheduling, enabling AGVs to exchange intention information, e.g., planned routes, to overcome partial observations and assist complex computation of online scheduling. Specifically, we determine intelligent AGVs' intention and sensor data as new M2M traffic and tailor the retransmission-free multi-link transmission networking to meet real-time operation demands. This scheduling-oriented networking is then integrated with a simulated annealing-based MRTA scheme and a congestion-aware A*-based route scheduling method. The integrated communication and scheduling scheme allows AGVs to dynamically adjust collision-free and congestion-free routes with reduced computational overhead. Numerical experiments shows the impacts from wireless communication on the performance of T-MRS and suggest that the proposed integrated scheme significantly enhances scheduling efficiency compared to other baselines, even under high AGV load conditions and limited channel resources. Moreover, the results reveal that the scheduling-oriented wireless M2M communication design fundamentally differs from human-to-human communications, implying new technological opportunities in a wireless networked smart factory.