Telerobotics is a key foundation in autonomous Industrial Cyber-Physical Systems (ICPS), enabling remote operations across various domains. However, conventional cloud-based telerobotics suffers from latency, reliability, scalability, and resilience issues, hindering real-time performance in critical applications. Cloud-Fog Telerobotics (CFTel) builds on the Cloud-Fog Automation (CFA) paradigm to address these limitations by leveraging a distributed Cloud-Edge-Robotics computing architecture, enabling deterministic connectivity, deterministic connected intelligence, and deterministic networked computing. This paper synthesizes recent advancements in CFTel, aiming to highlight its role in facilitating scalable, low-latency, autonomous, and AI-driven telerobotics. We analyze architectural frameworks and technologies that enable them, including 5G Ultra-Reliable Low-Latency Communication, Edge Intelligence, Embodied AI, and Digital Twins. The study demonstrates that CFTel has the potential to enhance real-time control, scalability, and autonomy while supporting service-oriented solutions. We also discuss practical challenges, including latency constraints, cybersecurity risks, interoperability issues, and standardization efforts. This work serves as a foundational reference for researchers, stakeholders, and industry practitioners in future telerobotics research.

Due to the limitations of traditional laser processing technology, which is confined to two-dimensional planes, it is difficult to meet the demands of complex curved surface laser processing. This study proposes a laser processing robot digital twin system for complex curved surfaces. Two surface trajectory generation strategies are introduced: the surface projection algorithm based on a scanning model supports the generation of unknown surface trajectories, and the STEP model analysis method based on OCC(Opencascade) enables trajectory generation for user-defined surfaces. Furthermore, a multi-task digital twin system is developed, integrating dynamic simulation of surface trajectories and real-time mapping of laser processing. Experimental verification shows that the system successfully realizes the full-process monitoring of graphene wire preparation by laser induction and arc additive manufacturing on complex curved surfaces.

Currently, robots face significant challenges in independently completing tasks within dynamic and unstructured environments. Teleoperation systems that utilize exoskeletons as input devices present an effective solution to this issue. This paper introduces an ergonomic 7-degree-of-freedom (7-DOF) exoskeleton device and develops an immersive teleoperation system integrated with a virtual reality (VR) head-mounted display (HMD). In this system, the operator, serving as the master side, dons the exoskeleton to issue control commands to the slave-side robot while leveraging feedback from both the exoskeleton and the VR HMD for cognitive decision-making. This closed-loop teleoperation system provides a multi-sensory feedback experience that integrates visual and haptic sensations, significantly enhancing operational stability and accuracy. Furthermore, for force feedback control, we propose a strategy based on environmental parameter estimation in conjunction with Weber’s law, allowing for self-adaptive adjustments of force feedback mapping in response to varying environmental conditions. Experimental results indicate that operators experience a high level of immersion with this system and successfully complete tasks such as remote ultrasound detection. The system demonstrates superior performance in terms of stability, accuracy, and user adaptability, highlighting its potential for complex remote operations in dynamic and unstructured environments.

Tele-homecare has become a promising approach to meet the growing demand for elderly and disability care. In such a context, ensuring human-robot interaction safety during teleoperation poses a critical challenge. Existing teleoperation control approaches focus solely on the robot’s end-effector trajectory, failing to handle inevitable or even desirable contacts on other robot links. This paper proposes a teleoperated shared-control strategy to deal with this challenge. A lightweight exoskeleton is developed to teleoperate the robot and give force feedback to the operator. Additionally, an exoskeleton-based shared-control strategy is proposed to integrate operator commands with real-time proximity sensing information, allowing the robot to avoid collisions while executing tasks. To react to inevitable contact, the force feedback function is incorporated into the proposed strategy to enable the operator to experience intuitive contact. Comparative experiments and a demonstration are designed to evaluate the feasibility and reliability of the proposed strategy in a tele-homecare scenario. Compared to the traditional teleoperation strategy, the proposed method can greatly reduce the contact forces on the robot’s links, indicating the potential of the proposed strategy in advancing safety in tele-homecare systems.

Performing unstructured grasping tasks in cluttered or obstacle-rich food processing environments is a key challenge in robotic systems. This work presents a task-adaptive grasping approach for a dual-arm anthropomorphic robot, named Herdsman, developed for the food industry. With an articulated torso, Herdsman is able to perform human-like reaching motions for more flexible grasping operations. To recognize the target object and extract the features for grasping, a vision pipeline, including a lightweight network GDC-YOLO for real-time object detection and a U-ReSENet network for grasping detection enhancement, is designed based on convolutional neural networks. After the detection comes the grasp execution, where a task-adaptive grasping strategy that works with the articulated torso is put forward to carry out grasping tasks in unstructured environments. Comparative experiments are designed to evaluate the detection performance between the proposed network and other popular networks for object detection and grasping detection. In addition, the task-adaptive grasp strategy for the Herdsman robot is experimentally validated by grasping the objects at different heights. The results have shown that the task-adaptive grasping solution exhibits robustness against variations in the target object position, which could be a promising approach for its application in unstructured environments requiring autonomous grasping.

The development of wireless communication has indeed promoted cloud-fog automation in the industry. It also introduces new issues of reliability and latency for control systems. This study sought to investigate the impacts of the commonly used industrial wireless network on the control performance parameters using a ball-and-beam (BB) time-critical balancing control system. An internal model control-inspired latency-aware wireless control framework (IMC-LA) is presented and employed in the BB system to handle the time delays and instability-creating elements introduced by wireless communication. A preliminary control assessment of the BB system under two new-generation wireless technologies, Wi-Fi 6 and 5G, was delivered. The correlation between network performance and control performance was analyzed statistically compared to the wired Ethernet condition. Test results show a dramatic decrease in position error after utilizing the proposed latency-aware wireless control framework. This study provides practical insights into the potential impacts of industrial wireless networks on control systems with a workable latency-aware wireless control approach. The methodology presented in this work has the potential to expedite the adoption of wireless communication in time-critical control. Note to Practitioners—Many factory automation processes experience undesirable latencies when transitioning from classic architecture to cloud automation architecture, particularly with the implementation of wireless communication. This paper aims to address the potential instability problem introduced by practical wireless networks and proposes a latency-aware control framework using the concept of internal model control. Meanwhile, this work also provides a way for practical operators to explore the relationship between critical parameters of communication networks and control performance parameters. The purpose of this paper is not to judge which wireless technology is better, instead we only want to demonstrate the effectiveness of the proposed latency-aware control in improving control performance under various wireless scenarios. The proposed framework is verified using the BB system for the commonly used cascaded PID control under two advanced wireless networks, 5G and Wi-Fi 6. It is also applicable to other industrial wireless networks with millisecond-class latency in the other regulatory control cases. The proposed IMC-LA wireless control framework reduces the significant effort and cost associated with constructing and tuning the controller in a real control system.

The increasing prevalence of electric vehicles de-mands efficient and sustainable management of end-of-life lithium-ion batteries. This paper examines the use of Pretrained Foundation Models and Cloud-Fog Automation to improve robotic disassembly of these batteries. We evaluate the performance of two Vision Transformer Models, in tasks involving deformed, rusty, contaminated, and worn batteries. Our proposed architecture, utilizing cloud and fog computing, balances performance with resource efficiency, providing a scalable solution for electric vehicles battery recycling.