Edge Computing Architectures for Latency-Critical Teleoperation: Conceptual Design and Performance Modeling

Abstract

Teleoperation of unmanned systems in remote or hazardous environments faces fundamental latency constraints that degrade operator performance and system safety, particularly when relying on centralized cloud computing for perception and decision support. This paper presents a comprehensive conceptual design framework for edge computing architectures that minimize end-to-end latency in teleoperation systems spanning aerial, ground, and underwater domains. We systematically analyze the latency budget decomposition across sensing, computation, communication, and actuation stages, identifying bottlenecks in conventional architectures. The proposed framework introduces a hierarchical edge computing topology with three tiers: on-vehicle edge nodes for time-critical processing (perception, obstacle avoidance), local edge servers for operator assistance functions (augmented reality overlays, predictive displays), and regional cloud resources for computationally intensive tasks (mission planning, data analytics). We develop analytical performance models characterizing latency, throughput, and reliability for each architectural variant, incorporating realistic network conditions including variable bandwidth, packet loss, and handover delays in mobile scenarios. The conceptual design addresses critical challenges including dynamic task offloading decisions that balance latency minimization with energy consumption, state synchronization across distributed computing nodes, and graceful degradation under edge node failures. We examine containerization and microservices architectures that enable flexible deployment of teleoperation functions across heterogeneous edge infrastructure. Particular attention is devoted to shared autonomy paradigms where edge computing enables real-time blending of operator commands with autonomous safety behaviors, analyzing the latency requirements for effective human-machine collaboration. The paper presents performance modeling results comparing edge architectures against cloud-only and on-vehicle-only baselines across representative teleoperation scenarios: surgical manipulation with haptic feedback, explosive ordnance disposal, and subsea infrastructure inspection. Modeling reveals that hybrid edge architectures achieve 60-80% latency reduction compared to cloud-based approaches while maintaining computational capabilities for advanced perception. We investigate emerging technologies including 5G network slicing for guaranteed latency, neuromorphic processors for ultra-low-latency perception, and predictive edge caching strategies. The study concludes with implementation guidelines and identifies research gaps in formal latency guarantees and security considerations for edge-based teleoperation.