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

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 (8)

• 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).
• 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.
• 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