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

Yaxin Liao; Qimei Cui; Kwang-Cheng Chen; Xiong Li; Jinlian Chen; Xiyu Zhao; Xiaofeng Tao; Ping Zhang

cs.LGMar 25, 2026

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

Authors: Yaxin Liao, Qimei Cui, Kwang-Cheng Chen, Xiong Li, Jinlian Chen, Xiyu Zhao, Xiaofeng Tao, Ping Zhang

Organizations: National Engineering Research Center of Mobile Network Technologies, Beijing University of Posts and Telecommunications, Beijing 100876, China · Department of Electrical Engineering, University of South Florida, Tampa, FL 33620 USA · School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China

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.

Figures & tables

Figure 1: Online scheduling for MRS in a smart factory. The edge server performs MRTA to the production MRS and thus T-MRS. (① $\rightarrow$ ②). Without wireless networking, multi-AGV operating in a distributed manner with partial observations lead to collisions and congestion. With wireless networking, AGVs collaboratively execute pick-up and delivery tasks on time by real-time adjusting routes (③), which significantly relies on AI operational intention and sensor data exchange via wireless networking (④).

Notation	Definition	Notation	Definition
$\mathcal{K}$	The set of AGVs	$\mathcal{R}_{k}$ , $\mathcal{P}_{k}$	Task routes, and navigation routes of AGV $k$
$C$ , $S$ , $D$	The number of available channels, selected channels, and communication interval	B	Decision matrix of MRTA
$\widehat{o}_{k}^{t}$ , $\tilde{o}_{k}^{t}$ , $o_{k}^{t}$ , $s^{t}$	Local partial observation, additional observation, enhanced fusion observation, and global state	$\widehat{W}$	Makespan based on local observations
$x_{m_{+}}$ , $x_{m_{-}}$ , $d_{m_{+}}^{t}$ , $d_{m_{-}}^{t}$ , $t_{m}^{ddl}$ , ${\mu}_{{\lambda}_{m}}$	Task nodes for pick-up and delivery, quantity of production at task nodes, task’s due time, task’s priority	$M^{t}$	the number of new tasks at time $t$
$\widehat{AT}_{k,e}$	Reach time of AGV $k$ at task node	$\widehat{\textbf{A}}$	Route decision matrix
$\widehat{WT}_{k,e}$	waiting time of AGV $k$ at task node	$\widehat{PT}_{k,e}$	preparation time at task node

Table 1: Main Notations

Figure 2: The decomposition of the online scheduling problem and the coupling between the MRTA and MRRSP algorithms.

Figure 3: The proposed communication-enabled online scheduling framework.

Panel of Figure 4 — Figure 4: Comparison of the performances of transmission probability and throughput under different numbers of AGVs. (a) Throughput vs. the number of AGVs for different D values when $C=S=1$ ; (b) Transmission success probability vs. the number of AGVs for different selected number of channels S when $D=2$ ; (c) Throughput vs. the number of AGVs for different selected number of channels S when $D=2$ .

Figure 5: The number of AGVs vs. makespan

Figure 6: Communication interval vs. makespan

Figure 7: Makespan $W$ vs. the number of selected channels $S$ and the number of AGVs $K$

Figure 8: Makespan $W$ vs. the available number of channels $C$ and the number of AGVs $K$

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MDGAM-Based Cooperative Task Scheduling for Communication-Constrained Distributed Multi-Agent Systems

Licheng Wang, Mingtao Huang, Yuan Shen

Department of Electronic Engineering, and Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China

Graph Attention Networks Multi-Agent Systems Multi-Robot Task Allocation Multi-Agent Communication Cooperative MARL

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Cooperative task scheduling in communication-constrained distributed multi-agent systems is challenging because each agent must make decisions from partial and dynamic observations while satisfying complex practical constraints. Existing heuristics rely on handcrafted bidding rules and repeated consensus, whereas many learning-based methods assume global observations and lack explicit communication-based coordination. To address these limitations, this paper proposes a neural scheduling framework for distributed multi-robot task allocation (MRTA), consisting of a multi-decoder graph attention model (MDGAM) policy model and a critic-free group relative multi-agent policy gradient (GRMAPG) training algorithm. MDGAM uses an extended graph attention mechanism to jointly update node and edge features, and employs multiple decoders to generate task-selection decisions and communication messages. GRMAPG constructs group-relative advantages from equivalent task-planning instances to replace the critic network used in conventional MARL algorithms, thereby reducing training difficulty and improving convergence performance. Experiments under different problem scales and communication ranges show that the proposed method improves task-completion performance over existing heuristic and learning-based methods, while ablation, complexity, and generalization tests further validate the proposed innovations.

Abstract

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