The global COVID-19 pandemic has put a strain on the healthcare system, further compounded by the aging population and the staffing shortage. As a result, the demand for healthcare exceeds the available offer, and health professionals are forced to compensate the best they can. On the other hand, some tasks can easily be offloaded to robots. Some solutions already exist, but they are not necessarily scalable, and exhibit a very high price point. This paper outlines our vision of a new distributed robotics approach to mitigate these constraints. We propose a prototype of an autonomous robot to assist healthcare professionals (e.g., nurses) in their work – for instance, by gathering equipment and delivering water and food to give them more time for human-centric tasks. The solution incorporates the ideas of cloud-fog-edge robotics to enable AI-based control with digital twin notions to help with the training. Preliminary work is introduced on the robot platform and the framework to facilitate communication at every infrastructure layer.

Towards Smart Distributed Robotics Solution using Digital Twin

This study systematically examines the safety protocols in electro-mechanical systems, focusing on methodologies, trends, and innovations that ensure human and system safety. The primary objective is to bridge the gap between current practices and the evolving demands of these systems, considering technological advancements and regulatory compliance. A systematic literature review methodology was employed, utilizing databases like IEEE Xplore, ScienceDirect, and Google Scholar. The search strategy involved keywords related to safety protocols and electro-mechanical systems, adhering to strict inclusion and exclusion criteria to ensure a focused analysis. Key findings reveal the growing importance of integrating advanced sensing technologies, artificial intelligence, and human factors into safety management. The study highlights the shift from reactive to proactive safety measures, emphasizing the need for continuous monitoring and predictive maintenance. Collaborative approaches involving various stakeholders, including industry and academia, are identified as crucial for enhancing safety. The future landscape of safety in electro-mechanical systems is expected to be significantly influenced by emerging technologies, leading to more sophisticated safety protocols. The study recommends continuous updating of safety protocols in line with technological advancements and changing regulatory landscapes. It also suggests interdisciplinary research and industry-academia collaboration to address complex safety challenges. Finally, the evolution of safety protocols in electro-mechanical systems is marked by technological advancements and a deeper understanding of human-system interaction. The study underscores the necessity for continuous improvement and adaptation of safety protocols, ensuring their effectiveness in safeguarding human lives and system sustainability.

https://wjarr.com/sites/default/files/WJARR-2023-2688.pdf

Safety protocols in electro-mechanical installations: Understanding the key methodologies for ensuring human and system safety

With the development of Industry 4.0, mobile agents are deployed to coordinate with multi-loop control systems to perform manufacturing tasks, leading to heterogeneous Internet of Things (IoT). Due to shadow fading induced by the movement of mobile agents, the wireless channel closing the control loops is inherently unreliable, which can compromise the control system performance. This paper addresses the co-design problem of transmission scheduling and agents’ movement to ensure both control performance and energy efficiency. A generalized cyber-physical-agent framework is proposed to capture the coupling between IoT systems and a mobile agent through a state-dependent fading channel. Moreover, the movement of the mobile agent among areas exhibiting different levels of shadow effects is modeled by Markov decision process. To address such heterogeneous dynamics, the co-design problem is formulated into the optimization of a logic-based stochastic switched system by utilizing the semi-tensor product technique. Based on state mergence and $\mathcal {H}$ -representation methods, a tractable and effective algorithm is then proposed to design co-design policies, minimizing the average joint cost of communication and control. A parallel scheme is further developed to alleviate the computation burden of the algorithm. Theoretical guarantees are provided to ensure control system performance. Finally, simulation results are given to demonstrate effectiveness of the proposed method.

Communication and Control Co-Design for Heterogeneous Industrial IoT: A Logic-Based Stochastic Switched System Approach

Modern algorithms allow robots to reach a greater level of autonomy and fulfill more challenging tasks. However, on-board limitations regarding computational and battery resources are hindering factors regarding the deployment of such algorithms particularly on mobile robots. Although offloading a majority of the algorithmic components to the edge or even cloud offers an attractive option to leverage massive computing power in robotics applications, safety and reliability remain critical issues. This paper presents a minimalistic safety fallback mechanism when offloading mobile robot navigation to the edge, that ensures safe and collision-free navigation even in the presence of failures in the connection between the on-board and edge-devices. We show the effectiveness of our approach through extensive testing in three different relevant scenarios in a simulated warehouse environment. Our experiments demonstrate the effects of different fallback strategies and show how our proposed approach is able to ensure safety while allowing the robot to continue its mission during an interrupted connection and thus avoiding unnecessary downtime.

Towards fault-tolerant deployment of mobile robot navigation in the edge: an experimental study

Mobile and autonomous robots are among the most critical technology applications requiring wireless connectivity with deterministic performance, including bounded latency with high reliability, even under congested network conditions. Emerging Wireless Time-Sensitive Networking (WTSN) capabilities over Wi-Fi and 5G can enable time synchronization and bounded low latency through time-aware scheduling mechanisms. Such Wireless TSN capabilities have been demonstrated in several industrial/robotic use cases, but under static conditions. Mobility introduces new challenges due to the roaming events and associated network outages owing to signaling between client devices and network infrastructure during these events. In this paper, we show how we can take advantage of the TSN redundancy capability (as defined in the IEEE 802.1CB standard) to eliminate outages or delays due to events like roaming and interference in a mobile robot use case enabled by Wi-Fi 6 TSN. We demonstrate the roaming performance with no delay impact on the applications though simulations of a mobile robot in a factory scenario and experimental results with a mobile robot connected via multiple Wi-Fi 6 radios in a warehouse environment.

Zero-Delay Roaming for Mobile Robots Enabled by Wireless TSN Redundancy

Mobile multi-robot systems are an integral component of highly automated factories of the future. Since mobile robots have limited on-board computing capability and battery capacity, there is increasing interest in exploring approaches that enable robots to effectively leverage wireless communications and Edge Computing solutions for perception, navigation, planning, coordination, and control. It is, however, a major challenge achieving precision, high-speed, co-ordinated actions between robots due to tight end-to-end latency, and safety requirements, especially while enabling time-sensitive data exchange over wireless networks and execution of computing workloads distributed across robots and the Edge system. The traditional approach of designing compute, communications, and control components in an Edge system as independent components, limits the capacity and scalability of computing and wireless resources and is therefore unsuitable to meet performance guarantees for energy and resource-efficient time-sensitive robotic applications. In this article, we discuss technical challenges in the context of two Edge Robotics use cases such as conveyer object pick-up and robot navigation, which are representative of time-critical control in IoT applications. We propose research directions grounded in an end-to-end system co-design paradigm and describe technology components such as virtualized robot functions, compute-communications-control co-design, Edge system co-simulation, safety and security aspects that are core to Edge Robotics. We also briefly outline future research directions that are necessary to pave the path toward factory-scale Edge Robotics systems.

Towards Factory-Scale Edge Robotic Systems: Challenges and Research Directions

In this work, we propose an advanced energy consumption model for an Autonomous Mobile Robots and validate the model on a real-world robot testbed. We show how our model can enable intelligent offloading of computationally heavy functions from the robot to an Edge server, over a wireless network, to minimize robot's energy consumption and maximize operating time on battery. Furthermore, we also present practical scenarios to show how our energy model can enable real-time adaptation of the Edge robotics system to constraints such as compute availability on the Edge server, available wireless network bandwidth, robot-specific camera frame rate requirements, robot navigation speeds and thereby improve robot energy efficiency. We show the benefits of our approach by offloading computationally heavy SLAM function from robot to the Edge server in simulation and through experiments on a real-world hardware testbed.

Adaptive Energy Optimization for Edge-Enabled Autonomous Mobile Robots

Achieving time-critical control in Edge Robotics Systems over 5G

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Towards Factory-Scale Edge Robotic Systems: Challenges and Research Directions

Adaptive Energy Optimization for Edge-Enabled Autonomous Mobile Robots

Achieving time-critical control in Edge Robotics Systems over 5G