Rapid advancements in digitalization and artificial intelligence (AI) have catalyzed the adoption of digital twin technologies in the construction sector, enabling real-time synchronization between virtual models and physical systems. Simultaneously, on-site robotic automation has shown promise for reducing physical workloads, enhancing productivity, and contributing to sustainability goals that are key values of Industry 5.0. However, current digital twin implementations rarely incorporate multirobot construction systems, often relying on single-robot approaches or purely offline simulations. This gap hinders the realization of truly integrated construction environments that combine sensing, data analytics, wireless communications, and multirobot coordination. In response, this article proposes ConstrucTwin, a digital twin-driven multirobot construction framework designed to support complex construction tasks in real-world settings. By combining a 5G communication estimation-involved architecture and a cross-level planning strategy, ConstrucTwin streamlines interactions between physical robots and their digital counterparts. Essential tasks such as motion and task-level planning, as well as remote human-in-the-loop (HIL) oversight, are orchestrated within a single unified architecture. Through case studies involving rebar cage and brick wall construction, we demonstrate how an integrated approach to vision-based servoing and multirobot coordination enhances execution speed, precision, and scalability. The results underscore the system’s potential to advance human-centric, resilient, and sustainable construction, thereby aligning with the broader vision of Industry 5.0.

Motivated by the growing importance of flexible automation in industrial environments, this article investigates the impact of wireless solutions in collaborative robotics, toward which we provide a quality of control (QoC)-based abstraction and methodology that comprehensively captures the interplay between network-induced delays, reliability, and robotic workload parameters for wireless collaborative robotics (WCR). For such a setting, we formulate a joint control-communication co-design based optimization framework to maximize the QoC across all robots, for 5G resource dimensioning. This is crucial for identifying optimal co-design parameters maximizing the QoC for limited 5G bandwidth across different topologies of robotic connectivity, prior to the deployment of these WCRs, or when selecting appropriate connectivity priority levels. We compare the performance of our proposed algorithm to different state of the art schemes in the literature. Our simulation results highlight the latency-reliability tradeoff and its implications on the control performance. We also demonstrate that our abstraction can be utilized for control-communication co-design, identifying optimal latency-reliability points in conjunction with the maximum velocity the robots operate with, while highlighting the energy gains due to co-design as well.

The miniaturisation of sensors and processors, the advancements in connected edge intelligence, and the exponential interest in Artificial Intelligence are boosting the affirmation of autonomous nano-size drones in the Internet of Robotic Things ecosystem. However, achieving safe autonomous navigation and high-level tasks such as exploration and surveillance with these tiny platforms is extremely challenging due to their limited resources. This work focuses on enabling the safe and autonomous flight of a pocket-size, 30-gram platform called Crazyflie 2.1 in a partially known environment. We propose a novel AIaided, vision-based reactive planning method for obstacle avoidance under the ambit of Integrated Sensing, Computing and Communication paradigm. We deal with the constraints of the nano-drone by splitting the navigation task into two parts: a deep learning-based object detector runs on the edge (external hardware) while the planning algorithm is executed onboard. The results show the ability to command the drone at ∼8 frames-per-second and a model performance reaching a COCO mean-average-precision of 60.8. Field experiments demonstrate the feasibility of the solution with the drone flying at a top speed of 1m/s while steering away from an obstacle placed in an unknown position and reaching the target destination. The outcome highlights the compatibility of the communication delay and the model performance with the requirements of the realtime navigation task. We provide a feasible alternative to a fully onboard implementation that can be extended to autonomous exploration with nano-drones.

With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably.

The increasing demand for latency-sensitive applications has necessitated the development of sophisticated algorithms that efficiently manage packets with end-to-end delay targets traversing the networked infrastructure. Network components must consider minimizing the packets' end-to-end delay violation probabilities (DVP) as a guiding principle throughout the transmission path to ensure timely deliveries. Active queue management (AQM) schemes are commonly used to mitigate congestion by dropping packets and controlling queuing delay. Today's established AQM schemes are threshold-driven, identifying congestion and trigger packet dropping using a predefined criteria which is unaware of packets' DVPs. In this work, we propose a novel framework, Delta, that combines end-to-end delay characterization with AQM for minimizing DVP. In a queuing theoretic environment, we show that such a policy is feasible by utilizing a data-driven approach to predict the queued packets' DVPs. That enables Delta AQM to effectively handle links with arbitrary stationary service time processes. The implementation is described in detail, and its performance is evaluated and compared with state of the art AQM algorithms. Our results show the Delta outperforms current AQM schemes substantially, in particular in scenarios where high reliability, i.e. high quantiles of the tail latency distribution, are of interest.

This paper presents ExPECA, an edge computing and wireless communication research testbed designed to tackle two pressing challenges: comprehensive end-to-end experimentation and high levels of experimental reproducibility. Leveraging OpenStack-based Chameleon Infrastructure (CHI) framework for its proven flexibility and ease of operation, ExPECA is located in a unique, isolated underground facility, providing a highly controlled setting for wireless experiments. The testbed is engineered to facilitate integrated studies of both communication and computation, offering a diverse array of Software-Defined Radios (SDR) and Commercial Off-The-Shelf (COTS) wireless and wired links, as well as containerized computational environments. We exemplify the experimental possibilities of the testbed using OpenRTiST, a latency-sensitive, bandwidthintensive application, and analyze its performance. Lastly, we highlight an array of research domains and experimental setups that stand to gain from ExPECA's features, including closed-loop applications and time-sensitive networking.

End-to-end learning for wireless communications has recently attracted much interest in the community, owing to the emergence of deep learning-based architectures for the physical layer. Neural network-based autoencoders have been proposed as potential replacements of traditional model-based transmitter and receiver structures. Such a replacement primarily provides an unprecedented level of flexibility, allowing to tune such emerging physical layer network stacks in many different directions. The semantic relevance of the transmitted messages is one of those directions. In this paper, we leverage a specific semantic relationship between the occurrence of a message (the source), and the channel statistics. Such a scenario could be illustrated for instance, in vehicular communications where the distance is to be conveyed between a leader and a follower. We study two autoencoder approaches where these special circumstances are exploited. We then evaluate our autoencoders, showing through the simulations that the semantic optimization can achieve significant improvements in the BLERs (up till 93.6%) and RMSEs (up till 87.3%) for vehicular communications leading to considerably reduced risks and needs for message retransmissions.

As the number of cyber-physical systems rises, it becomes increasingly crucial to study Networked Control Systems (NCSs) combining control communication co-design. This nature of NCSs has led to task-specific approaches to research, creating a dearth of generalizable, repeatable, and scalable experimentation. Further, with the advent of edge computing solutions, it is of paramount importance to explore its relevance in such applications. In this work, we present CLEAVE, a novel, completely software-based framework for repeatable and scalable experimentation in edge native NCSs. Our approach is based on the emulation of physical plants communicating over a real network with software-based controllers. CLEAVE is designed and built for the edge, using Python3 and with full compatibility with industry-standard containerization solutions. Although designed for single-loop emulations, the flexibility afforded by the aforementioned characteristics allow our framework to be adapted to a multitude of complex scenarios.

We validate CLEAVE using an initial implementation of an inverted pendulum NCS. Our results showcase the utility of the tool as a repeatable, extensible, and scalable solution to NCS performance evaluation and benchmarking on the Edge.

This paper presents a wireless mesh network-based solution utilizing stationary and mobile ground/UAV nodes for setting up a post-disaster communication infrastructure. The network is ad hoc and establishes connectivity over a large area without the use of a cellular network or the internet. We propose a complete end-to-end framework, where we deploy an android application on smartphones at the user-end, the wireless mesh network as the backbone of communication, and a graphical user interface (GUI) at a base station for facilitating situational awareness. The software stack is based on the Robot Operating System for broadcasting user information to the base station, and also for controlling the UAVs remotely. The GUI also incorporates a node recommendation strategy for selecting the optimal nodes. The framework is evaluated using UAVs and a semi-autonomous car, and the experimental results show that the proposed system could be indispensable in providing large-scale connectivity in post-disaster management.

Person Re-Identification (PRID) has been one of the most challenging tasks in intelligent video surveillance. Most existing PRID based surveillance methods rely on a single camera mounted on emplacements. This approach assumes that any scrutinized person appears again in the field of view of that cam-era, which is unreliable in real-world settings. In this paper, we propose a system wherein we explore on-board re-identification of persons using cameras mounted on Unmanned Aerial Vehicles (UAVs). The captured images are dynamically shared between multiple UAVs by virtue of wireless mesh networking, also making use of the Robot Operating System (ROS) for on-ground control of the UAVs from a control station, facilitating the exchange of images between them. We demonstrate our approach on an institutional dataset we created, and our experimental results show that the system could be indispensable in aiding airborne surveillance operations.