The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Review of Recent Works

Abstract

A collaborative robot, or cobot, enables users to work closely with it through direct communication without the use of traditional barricades. Cobots eliminate the gap that has historically existed between industrial robots and humans while they work within fences. Cobots can be used for a variety of tasks, from communication robots in public areas and logistic or supply chain robots that move materials inside a building, to articulated or industrial robots that assist in automating tasks which are not ergonomically sound, such as assisting individuals in carrying large parts, or assembly lines. Human faith in collaboration has increased through human–robot collaboration applications built with dependability and safety in mind, which also enhances employee performance and working circumstances. Artificial intelligence and cobots are becoming more accessible due to advanced technology and new processor generations. Cobots are now being changed from science fiction to science through machine learning. They can quickly respond to change, decrease expenses, and enhance user experience. In order to identify the existing and potential expanding role of artificial intelligence in cobots for industrial applications, this paper provides a systematic literature review of the latest research publications between 2018 and 2022. It concludes by discussing various difficulties in current industrial collaborative robots and provides direction for future research.

Human Skills in the Era of Industry 4.0 in the Indian Automotive Industry

“…Erol et al (2016) advocate that employees shift towards creative and communicative activities amid routine task automation. Industry 4.0 facilitates flexible work arrangements and collaboration with robots, necessitating skills in communication, adaptability and collaboration (Borboni et al, 2023). Workers must adeptly communicate across platforms, collaborate with diverse colleagues and continually learn and adapt to new technologies (Rainie & Anderson, 2017).…”

Task Complexity and the Skills Dilemma in the Programming and Control of Collaborative Robots for Manufacturing

“…For instance, Reinforcement Learning has been effectively used to train robots to conduct complex tasks; while modifying their behaviour in response to changes in their environments, which they continuously monitor. In contrast, Deep Learning (DL) could restrict dynamic response behaviour due to time-inefficient data processing requirements [87]. According to [87], DL may not be suitable for differentiating objects of similar appearance, which may be the case with some electronic components used in assembly processes.…”

“…In contrast, Deep Learning (DL) could restrict dynamic response behaviour due to time-inefficient data processing requirements [87]. According to [87], DL may not be suitable for differentiating objects of similar appearance, which may be the case with some electronic components used in assembly processes. In addition, DL is a complex area of AI that requires significant experience and skills to implement [88], a notion that opposes the objective of the current study.…”

Analysis of quality standards for industrial collaborative robots based on user‐centered design framework

“…However, even if the specifications of collaborative robots satisfy a high level of performance, these system‐centered standard clauses may cause problems with user trust and acceptance of new technologies in the actual commercialization stage of collaborative robots. In particular, collaborative robots equipped with artificial intelligence technology capable of knowledge‐based behavior will enable more complex and diverse interactions (Borboni et al, 2023). A gap between the intentions of technology developers and the needs of users can lead to improper interactions that not only reduce user satisfaction but also lead to safety issues.…”

“…At this level, collaborative robots focus on improving Human–robot interaction (HRI) and should be designed to be more flexible and adaptable to work in dynamic environments where tasks and goals can change quickly (Dmytriyev et al, 2021). Although several researchers are working on efficient HRI (Kokotinis et al, 2023; Tzavara et al, 2021), numerous obstacles remain to be solved before collaborative robots can be commercialized (Borboni et al, 2023). At the collaboration level, robots and humans work together equally to share knowledge and technology, and accomplish a common goal (Chowdhury et al, 2020).…”