Joint Communication and Mission Planning: The Real-world Challenges of Factory 5G

Abstract

The fourth industrial revolution envisions flexible manufacturing in which autonomous mobile robots collaborate over shared wireless networks. Fifth-generation (5G) cellular technology—and its successors Beyond 5G and 6G—has been positioned as the enabling infrastructure, promising network slicing, quality-of-service differentiation, and mobile edge computing that could untether industrial robots from cables without sacrificing control fidelity. The concurrent rise of physical AI and the AI-native radio access network paradigm, which converges AI workloads with network infrastructure, further amplify both the promise and the complexity of this integration. This thesis investigates whether current research is equipped to exploit that infrastructure—and identifies several structural misalignments that the field must address. 

The work proceeds through four phases. A systematic mapping study of edge computing for cyber-physical systems finds that industrial manufacturing is the single largest application driver, yet mobile edge computing in the 5G sense and systematic treatment of trustworthiness attributes are largely absent from the literature. A focused survey of what this thesis terms communication-aware motion planning reveals a field that is terminologically fragmented, methodologically confined to motion-level optimization with channel-centric metrics, and almost entirely disconnected from real telecommunications infrastructure. The survey yields a taxonomy and evaluation criteria that provide a structured vocabulary for characterizing and comparing approaches across the robotics and telecommunications communities. 

In response, a planning framework called RoboPlan5G demonstrates joint communication and mission planning by formulating multi-robot coordination in the Planning Domain Definition Language, treating 5G physical resource blocks as explicit planner decision variables alongside task allocation and sequencing. Incorporating network resource allocation into the mission planner reduces spectrum requirements by fifty percent while satisfying plan constraints within acceptable computation time. 

An empirical measurement campaign on a private 5G industrial testbed then challenges a central assumption shared by most existing communication-aware planning approaches: that favorable channel conditions translate reliably into end-to-end performance. A commercial ray-tracing simulator predicted signal-to-interference-plus-noise ratio with reasonable accuracy yet systematically over-predicted throughput, because multiple-input multiple-output spatial rank adaptation—the dominant source of prediction error—lies beyond the reach of channel-centric models. A data-driven Gaussian process regression model reduced prediction error by approximately two-thirds and eliminated systematic bias. The testbed also did not support the radio access network-level slicing that both the planning framework and the broader literature presuppose, revealing a gap between standardized interfaces and deployed capabilities. 

The thesis culminates not in a single superior framework but in a harder-won insight: the field's algorithmic sophistication obscures a disconnect from industrial reality operating at multiple levels simultaneously—conceptual, modeling, infrastructure, and evaluation. At the conceptual level, motion planners are extended with signal-level metrics when mission planners should be extended with service-level network abstractions. At the modeling level, planners optimize for channel quality while the phenomena that determine throughput in modern cellular systems lie beyond channel-centric models. At the infrastructure level, the network capabilities assumed in the literature remain incompletely realized in commercial equipment. The contribution of this work lies in exposing these disconnects systematically and in offering both analytical tools and a constructive artifact that together chart a more grounded path forward. Achieving the smart factory vision requires not merely better algorithms but a reorientation of assumptions, formalisms, and evaluation practices—from motion to mission planning, from channel metrics to network-service abstractions, and from simulated validation to measurement-grounded evaluation.

Link to DiVA