Edge computing is increasingly seen as the perfect enabler for distributed robotic applications requiring low latency and high adaptability. However, current approaches offload computational tasks exclusively to edge nodes, overlooking scenarios where onboard processing can meet performance tar-gets. Therefore, we propose a framework that unifies offloading decision and resource orchestration into a single algorithm that compares different allocation strategies under accuracy, latency, and energy goals. Results show that this approach admits more functions while maintaining strict performance targets, offering substantial gains in overall system utility.

5G mobile networks introduce a new dimension for connecting and operating mobile robots in outdoor environments, leveraging cloud-native and offloading features of 5G networks to enable fully flexible and collaborative cloud robot operations. However, the limited battery life of robots remains a significant obstacle to their effective adoption in real-world exploration scenarios. This paper explores, via field experiments, the potential energy-saving gains of OROS, a joint orchestration of 5G and Robot Operating System (ROS) that coordinates multiple 5G-connected robots both in terms of navigation and sensing, as well as optimizes their cloud-native service resource utilization while minimizing total resource and energy consumption on the robots based on real-time feedback. We designed, implemented and evaluated our proposed OROS in an experimental testbed composed of commercial off-the-shelf robots and a local 5G infrastructure deployed on a campus. The experimental results demonstrated that OROS significantly outperforms state-of-the-art approaches in terms of energy savings by offloading demanding computational tasks to the 5G edge infrastructure and dynamic energy management of on-board sensors (e.g., switching them off when they are not needed). This strategy achieves approximately ~15% energy savings on the robots, thereby extending battery life, which in turn allows for longer operating times and better resource utilization.

Wireless communication has profoundly transformed the way we experience the world. For instance, at most events, attendees commonly utilize their smartphones to document and share their experiences. This shift in user behavior largely stems from the cellular network’s capacity for communication. However, as networks become increasingly sophisticated, new opportunities arise to leverage the network for services beyond mere communication, collectively termed Beyond Communication Services (BCS). These services encompass joint communications and sensing, network as a service, and distributed computing. This paper presents examples of BCS and identifies the enablers necessary to facilitate their realization in sixth generation (6 G). These enablers encompass exposing data and network capabilities, optimizing protocols and procedures for BCS, optimizing compute offloading protocols and signalling, and employing application and device-driven optimization strategies.

This paper provides an extensive analysis of Reliable and Available Wireless (RAW) enhancements and solutions needed to manage industrial environments more effectively. Starting from the description of a representative industrial use case, an analysis of gaps and promising new extensions is performed. Namely, the need to (i) support multi-domain operation, at both technology and administrative levels; (ii) integrate RAW with edge architectures; and, (iii) increase the mobility support in RAW networks. The identified gaps are indeed not yet tackled by the relevant standardization development organizations, mainly the Internet Engineering Task Force (IETF), and are thus object of our future work.

Recent trends in robotic services propose offloading robot functionalities to the Edge to meet the strict latency requirements of networked robotics. However, the Edge is typically an expensive resource and sometimes the Cloud is also an option, thus, decreasing the cost. Following this idea, we propose Don’t Let Me Down! (DLMD), an algorithm that promotes offloading robot functions to the Cloud when possible to minimize the consumption of Edge resources. Additionally, DLMD takes the appropriate migration, traffic steering, and radio handover decisions to meet robotic service requirements as strict latency constraints. In the paper, we formulate the optimization problem that DLMD aims to solve, compare DLMD performance against the state of the art, and perform stress tests to assess DLMD performance in small & large networks. Results show that DLMD (i) always finds solutions in less than 30ms; (ii) is optimal in a local warehousing use case; and (iii) consumes only 5% of the Edge resources upon network stress.

This paper presents the design, implementation and evaluation of waveSLAM, a low-cost mobile robot system that uses the millimetre wave (mmWave) communication devices to enhance the indoor mapping process targeting environments with reduced visibility or glass/mirror walls. A unique feature of waveSLAM is that it only leverages existing Commercial-Off-The-Shelf (COTS) hardware (Lidar and mmWave radios) that are mounted on mobile robots to improve the accurate indoor mapping achieved with optical sensors. The key intuition behind the waveSLAM design is that while the mobile robots moves freely, the mmWave radios can periodically exchange angle and distance estimates between themselves (self-sensing) by bouncing the signal from the environment, thus enabling accurate estimates of the target object/material surface. Our experiments verify that waveSLAM can archive cm-level accuracy with errors below 22 cm and 20° in angle orientation which is compatible with Lidar when building indoor maps.

Wireless communications represent a game changer for future manufacturing plants, enabling flexible production chains, as machinery and other components not to be restricted to a location by the rigid wired connections on the factory floor. However, the presence of electromagnetic interference in the wireless spectrum may result in packet loss and delay, making it a challenging environment to meet the extreme reliability requirements of industrial applications. In such conditions, achieving real-time remote control, either from the Edge or Cloud, becomes complex. In this paper, we investigate a forecast-based recovery mechanism for real-time remote control of robotic manipulators (FoReCo) that uses Machine Learning (ML) to infer lost commands caused by interference in the wireless channel. FoReCo is evaluated through both simulation and experimentation in interference prone IEEE 802.11 wireless links, and using a commercial research robot that performs pick-and-place tasks. Results show that upon interference FoReCo reduces the trajectory error by more than a 34.35% in both simulation, and experimentation. We also show that FoReCo is sufficiently lightweight to be deployed in existing hardware.

Thanks to network slicing, mobile networks can now support multiple and diverse services, each requiring different key performance indicators (KPIs). In this new scenario, it is critical to allocate network and computing resources efficiently and in such a way that all KPIs targeted by a service are met. Accounting for all sorts of KPIs (e.g., availability and reliability, besides the more traditional throughput and latency) is an aspect that has been scarcely addressed so far and that requires tailored models and solution strategies. We address this issue by proposing a novel methodology and resource orchestration scheme, named OKpi, which provides high-quality decisions on VNF (Virtual Network Function) placement and data routing, including the selection of radio points of attachment. Importantly, OKpi has polynomial computational complexity and accounts forallKPIs required by each service, and for any resource available from the fog to the cloud. We prove several properties of OKpi and demonstrate that it performs very closely to the optimum under real-world scenarios. We also implement OKpi in a testbed supporting a robot-based, smart factory service, and we present some field tests that further confirm the ability of OKpi to make high-quality decisions.

Digital Twin (DT) is a pivotal application under the industrial digital transformation envisaged by the fourth industrial revolution (Industry 4.0). DT defines intelligent and real-time faithful reflections of physical entities such as industrial robots, thus allowing their remote control. Relying on the latest advances in Information and Communication Technologies (ICT), namely Network Function Virtualization (NFV) and Edge-computing, DT can be deployed as an on-demand service in the factories close proximity and offered leveraging radio access technologies. However, with the purpose of achieving the well-known scalability, flexibility, availability and performance guarantees benefits foreseen by the latest ICT, it is steadily required to experimentally profile and assess DT as a Service (DTaaS) solutions. Moreover, the dependencies between the resources claimed by the service and the relative demand and work loads require to be investigated.In this work, an Edge-based Digital Twin solution for remote control of robotic arms is deployed in an experimental testbed where, in compliance with the NFV paradigm, the service has been segmented in virtual network functions. Our research has primarily the objective to evaluate the entanglement among overall service performance and VNFs resource requirements, and the number of robots consuming the service varies. Experimental profiles show the most critical DT features to be the inverse kinematics and trajectory computations. Moreover, the same analysis has been carried out as a function of the industrial processes, namely based on the commands imposed on the robots, and particularly of their abstraction-level, resulting in a novel trade-off between computing and time resources requirements and trajectory guarantees. The derived results provide crucial insights for the design of network service scaling and resource orchestration frameworks dealing with DTaaS applications. Finally, we empirically prove LTE shortage to accommodate the minimum DT latency requirements.