This thesis proposes a selection of methods essential for deploying 5G-enabled robotic applications, particularly emphasizing time-critical applications and services dependent on communication system performance, such as cloud offloaded algorithms and radio-based positioning. The proposed methods constitute a comprehensive framework that spans 5G dynamic Quality of Service, rate adaptation, and co-design of the robotic application and 5G network, with emphasis on experimental evaluation conducted using aerial and ground robotic platforms. 

A central focus of this work involves evaluating how communication system Key Performance Indicators (KPIs) influence robotic-specific KPIs. As a characteristic use-case and a comparison basis, this research employs real-time trajectory control using computationally intensive Non-linear Model Predictive Control (NMPC) for Unmanned Aerial Vehicles (UAVs) offloaded to edge-cloud infrastructure. This paradigm effectively demonstrates the causal relationships between communication and robotics KPIs, facilitating the identification of critical communication-control co-design principles, bottlenecks, and challenges within complex 5G-enabled robotic systems. 

Additionally, various 5G architectures and their roles in enabling time-critical offloading of NMPC trajectory control for UAVs to the cloud were evaluated, along with the necessary underlying communication protocols and algorithms. This evaluation provides empirical performance benchmarks and baseline insights into trajectory-tracking tasks dependent on network offloading architectures and algorithms. 

Building on the communication requirements for the offloaded NMPC system, latency and Signal-to-Noise Ratio (SNR) metrics were connected to robotic KPIs and an appropriate fallback mechanism was developed. This mechanism dynamically transition control from the sophisticated offloaded NMPC to an onboard Proportional Integral Derivative (PID)-based controller, ensuring robust robot operation under adverse communication scenarios such as high network load leading to increasing end-to-end (e2e) latency and severe signal degradation that would affect stability. 

To compensate for the effects of bounded round-trip e2e latency on robot performance, estimators were developed that leverage system models and latency statistics derived from sensor data and control command transmission delays. 

Complementing latency compensation and fallback mechanisms, efforts were directed towards preventing unbounded latency, commonly caused by robots transmitting data beyond the possible throughput given their allocated network resources. 5G dynamic Quality of Service (QoS) techniques were applied to dynamically prioritize time-critical application traffic, differentiating critical data flows amid varying network loads generated by background users, thus maintaining control stability and safe operations. Queuing theory-based modeling of robotic data flows and their interaction with the 5G network underlies this approach, ensuring performance stability and reliability. The relationship between mission criticality and data flow prioritization in finite-resourced cellular networks is discussed in depth. 

Furthermore, advanced rate-adaptation techniques were integrated to facilitate low-latency transmission of high-bitrate 3D LiDAR sensor data. The proposed method is designed to link key robotics KPIs, such as distance errors produced by the data encoder, with the communication framework, targeting high-throughput and low-latency data transmission within confined error limits. The integration of dynamic QoS mechanisms within this method is also discussed. 

Lastly, beyond communication, the 5G network and its advanced radio-based positioning services were employed to tackle global frame localization challenges. An indoor 5G localization system, combined with GNSS-RTK (Global Navigation Satellite System-Real-Time Kinematic) for outdoor localization and onboard sensors, was integrated using Markov Decision Processes filtering module and an Extended Kalman Filter approach to provide consistent global localization estimates. This final element emphasizes the multi-modal capabilities of 5G networks, showcasing their extensive utility in supporting robotic applications.  

The entire thesis underscores a robust real-life experimental focus and stresses the critical interplay between communication systems and robotics through integrated co-design approaches.