Service robots in the healthcare sector

Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Ultrasensitive multimodal physicochemical sensing for autonomous robotic decision-making has numerous applications in agriculture, security, environmental protection, and public health. Previously reported robotic sensing technologies have primarily focused on monitoring physical parameters such as pressure and temperature. Integrating chemical sensors for autonomous dry-phase analyte detection on a robotic platform is rather extremely challenging and substantially underdeveloped. Here, we introduce an artificial intelligence-powered multimodal robotic sensing system (M-Bot) with an all-printed mass-producible soft electronic skin-based human-machine interface. A scalable inkjet printing technology with custom-developed nanomaterial inks was used to manufacture flexible physicochemical sensor arrays for electrophysiology recording, tactile perception, and robotic sensing of a wide range of hazardous materials including nitroaromatic explosives, pesticides, nerve agents, and infectious pathogens such as SARS-CoV-2. The M-Bot decodes the surface electromyography signals collected from the human body through machine learning algorithms for remote robotic control and can perform in situ threat compound detection in extreme or contaminated environments with user-interactive tactile and threat alarm feedback. The printed electronic skin-based robotic sensing technology can be further generalized and applied to other remote sensing platforms. Such diversity was validated on an intelligent multimodal robotic boat platform that can efficiently track the source of trace amounts of hazardous compounds through autonomous and intelligent decision-making algorithms. This fully printed human-machine interactive multimodal sensing technology could play a crucial role in designing future intelligent robotic systems and can be easily reconfigured toward numerous practical wearable and robotic applications.

All-printed soft human-machine interface for robotic physicochemical sensing

Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review

The COVID-19 pandemic has affected manufacturing companies and necessitated adaptations of firms’ operations. Despite the increasing interest in this subject, a scarcity of systematic analysis can be observed. The present study systematically reviews the existing research on the COVID-19 pandemic concerning the manufacturing industry. This paper aims to highlight the main impacts of the COVID-19 pandemic on the manufacturing sector from the operations management perspective, the practical adaptation actions, and future research opportunities. Open research questions and directions for further investigation are articulated and triangulated across organisational, process and technology perspectives. 

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Analysis of the COVID-19 pandemic’s impacts on manufacturing: a systematic literature review and future research agenda

As a result of the difficulties brought by COVID-19 and its associated lockdowns, many individuals and companies have turned to robots in order to overcome the challenges of the pandemic. Compared with traditional human labor, robotic and autonomous systems have advantages such as an intrinsic immunity to the virus and an inability for human-robot-human spread of any disease-causing pathogens, though there are still many technical hurdles for the robotics industry to overcome. This survey comprehensively reviews over 200 reports covering robotic systems which have emerged or have been repurposed during the past several months, to provide insights to both academia and industry. In each chapter, we cover both the advantages and the challenges for each robot, finding that robotics systems are overall apt solutions for dealing with many of the problems brought on by COVID-19, including: diagnosis, screening, disinfection, surgery, telehealth, care, logistics, manufacturing and broader interpersonal problems unique to the lockdowns of the pandemic. By discussing the potential new robot capabilities and fields they applied to, we expect the robotics industry to take a leap forward due to this unexpected pandemic.

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Robots Under COVID-19 Pandemic: A Comprehensive Survey

The head mechanism is a critical part of a humanoid healthcare robot for potential healthcare or elderly care. The head structure of a humanoid robot should provide both motion and perception capabilities to mimic head function (e.g. object tracking) for Human-robot interaction (HRI) at the head level. In this paper, an innovative hybrid head mechanism consisting of one rotational servo motor and two linear servos has been proposed. Structural design, kinematic analysis, and automation have been studied to advance locomotive capability as well as functionality. Additionally, a visual servoing object tracking system was implemented as a perception unit to achieve real-time object tracking function. For the structural design, a parallel configuration of two linear actuators was selected to simplify the kinematic analysis, which enables precise control of joint angles. For the perception part, a vision-based real-time Aruco maker tracking system with proportional-integral-derivative (PID) control was set as a test scenario to mimic human head function with joint control of 3 DOFs (yaw, pitch, and roll). The functionality of this head mechanism has been validated by tests on potential use case (visual servoing object tracking). This head mechanism can servo as a valuable platform for broader HRI applications with humanoid healthcare robots.

Hybrid Humanoid Robotic Head Mechanism: Design, Modeling, and Experiments with Object Tracking

The proportion of elderly people in society is predicted to continue to rise in the coming decades. Mobility is a key aspect of many daily activities, but falls become an increasingly significant health risk with age. With the COVID-19 pandemic, many elderly users prefer or require assistive devices, rather than human support, in walking and carrying out daily tasks. However, prior work has shown that when using passive assistive mobility devices, fall risks can actually increase. This presents an opportunity for assistive robots to help maintain and improve the mobility of elderly users, with an additional emphasis on safety, made possible through sensing capabilities. In this paper, we present a computer vision system that detects the eye blink and face angle patterns for exhibiting signs of tiredness. In addition to the frame-based detection, we also introduce a time-window collation with a machine learning classifier. The system proposed here is critical in monitoring the user, performing real-time detection, and recommending they take a break if tiredness is detected. The overall system architecture and algorithmic details are presented, then a series of experiments are conducted to validate the performance of the approach.

Chengkun Zhang

Papers

Robots Under COVID-19 Pandemic: A Comprehensive Survey

Hybrid Humanoid Robotic Head Mechanism: Design, Modeling, and Experiments with Object Tracking

Tiredness Detection for a Robotic Mobility Aid