Adam King

Bio: Adam King is an academic researcher. The author has contributed to research in topics: Conversation & Humanoid robot. The author has an hindex of 1, co-authored 2 publications receiving 41 citations.

Robots Under COVID-19 Pandemic: A Comprehensive Survey

Abstract: 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.

Identification and Engagement of Passive Subjects in Multiparty Conversations by a Humanoid Robot

Abstract: In this work, we present a novel human-robot interaction (HRI) method to detect and engage passive subjects in multiparty conversations using a humanoid robot. Voice activity detection and speaker localization are combined with facial recognition to detect and identify non-participating subjects. Once a non-participating individual is identified, the robot addresses the subject with a fact related to the topic of the conversation, with the goal of promoting the subject to join the conversation. To prompt sentences related to the topic of the conversation, automatic speech recognition and natural language processing techniques are employed. Preliminary experiments demonstrate that the method successfully identifies and engages passive subjects in a conversation.

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

Abstract: 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.

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

Abstract: 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.

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

Abstract: 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.