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

2026-03-25

AI Summary

This work addresses the challenges of real-time scheduling, collision avoidance, and congestion control in multi-robot transportation systems (T-MRS) operating under partial observability in smart factories. The authors propose a communication-driven online scheduling framework that fundamentally integrates scheduling-oriented machine-to-machine (M2M) communication design with multi-robot task allocation and path planning, thereby transcending the conventional paradigm of decoupled communication and control. By employing a retransmission-free multi-link mechanism to efficiently convey AGV intentions and sensory data, and combining it with a simulated annealing–based task allocation algorithm and a congestion-aware A* path planner, the framework significantly enhances scheduling efficiency under high workload and limited channel resources. Experimental results validate the critical role of purpose-built M2M communication in boosting T-MRS performance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• online scheduling
• partial observability
• multi-robot systems
• smart factory
• collision-free routing

Innovation

• communication-enabled scheduling
• multi-robot task assignment
• M2M networking
• partial observability
• congestion-aware routing