Human-robot Collaboration in Decentralized Manufacturing Systems: An Approach for Simulation-based Evaluation of Future Intelligent Production

Bochmann, Lennart; Bänziger, Timo; Kunz, Andreas; Wegener, Konrad

doi:10.1016/j.procir.2016.06.021

Cited by 26 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, research in this field is still rare. For example, robotics application technologies help manufacturing systems handle repetitive jobs and enables continuous systems to achieve economies of scale and efficiency (Bochmann et al, 2017). All robotics equipment includes sensors, intelligence to make autonomous decisions, and collaborative operations with humans (cobots) for improved productivity, quality, and lead times (Pfeiffer, 2016).…”

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

Responsible digitalization through digital technologies and green practices

Cardinali

Giovanni

2022

Corp Soc Responsibility Env

View full text Add to dashboard Cite

Although digital technologies grant documented operational and economic benefits, they can activate unwanted consequences for the environment and society. This research investigates these issues by exploring how firms can create a portfolio of technologies to perform corporate social responsibility (CSR) goals and then achieving responsible digitalization. The latter is defined as the firms' capacity of adopting digital technologies without underperforming in terms of CSR. Accordingly, we investigate the relationships between digital technologies and CSR to discover the firms' capacity to achieve responsible digitalization targets. Furthermore, we explore how the use of some green practices allows firms to increase the chances of achieving responsible digitalization goals.

show abstract

Section: Literature Review and Hypothesis Developmentmentioning

confidence: 99%

Responsible digitalization through digital technologies and green practices

Cardinali

Giovanni

2022

Corp Soc Responsibility Env

View full text Add to dashboard Cite

show abstract

“…Adaptive task sheets 1) Adaptation to skills and experience of the operator through a task distribution strategy based on levels of automation [171] centralized manufacturing systems such as the relationships between the factory layout planning, production scheduling, and human-robot work distribution [168]. Another similar solution is based on developing an algorithmic safety set of measures for intelligent industrial collaborative robotics applications based on a modularized parallel controller structure to solve the motion planning and control problem in a human involved environment [169].…”

Section: Task and Operation Optimization Techniquementioning

confidence: 99%

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

et al. 2021

View full text Add to dashboard Cite

The demand for collaborative robots is growing in industrial environments due to their versatility and low prices. Thus, more collaborative solutions are emerging for industrial scenarios. However, implementing scenarios where robots work autonomously while synchronizing their operations in a safe industrial environment with shop-floor workers is not easy. To fill the gap existing in the safe implementation of industrial collaborative scenarios, this manuscript presents a review based on five identified challenges that gathers the primary vital aspects to bear in mind while developing applications for them. Thus, a four-level classification is proposed, which collects the identified challenges and the previous developments in the field of human-robot interaction. The five identified challenges pretends to be the missing enabling key for implementing industrial collaborative scenarios in modern industrial plants. Lastly, a discussion and conclusion are exposed to analyze the degree of development in the field and its potential growth.

show abstract

“…The use and capabilities of multi-agent systems have continued to increase, with a number of demonstrable applications in the manufacturing sector [37,38]. The variation in behaviour that distributed control enables places simulation as a critical tool in developing distributed control systems, as the use of simulation is a powerful tool enabling the design, evaluation and subsequent optimization of intelligent agent performance on representative and varied tasks in a repeatable environment [39]. A detailed overview of intelligent agents can be found in [40,41].…”

Section: Intelligent Manufacturingmentioning

confidence: 99%

Reinforcement learning for facilitating human-robot-interaction in manufacturing

Oliff

Liu

Kumar

et al. 2020

Journal of Manufacturing Systems

View full text Add to dashboard Cite

For many contemporary manufacturing processes, autonomous robotic operators have become ubiquitous. Despite this, the number of human operators within these processes remains high, and as a consequence, the number of interactions between humans and robots has increased in this context. This is a problem, as human beings introduce a source of disturbance and unpredictability into these processes in the form of performance variation. Despite the natural human aptitude for flexibility, their presence remains a source of disturbance within the system and make modelling and optimization of these systems considerably more challenging, and in many cases impossible. Improving the ability of robotic operators to adapt their behaviour to variations in human task performance is, therefore, a significant challenge to be overcome to enable many ideas in the larger intelligent manufacturing paradigm to be realised. This work presents the development of a methodology to effectively model these systems and a reinforcement learning agent capable of autonomous decision-making. This decision-making provides the robotic operators with greater adaptability, by enabling its behaviour to change based on observed information, both of its environment and human colleagues. The work extends theoretical knowledge on how learning methods can be implemented for robotic control, and how the capabilities that they enable may be leveraged to improve the interaction between robots and their human counterparts. The work further presents a novel methodology for the implementation of a reinforcement learning-based intelligent agent which enables a change in behavioural policy in robotic operators in response to performance variation in their human colleagues. The development and evaluation are supported by a generalized simulation model, which is parameterized to enable appropriate variation in human performance. The evaluation demonstrates that the reinforcement agent can effectively learn to make adjustments to its behaviour based on the knowledge extracted from observed information, and balance the task demands to optimise these adjustments.

show abstract

Human-robot Collaboration in Decentralized Manufacturing Systems: An Approach for Simulation-based Evaluation of Future Intelligent Production

Cited by 26 publications

References 21 publications

Responsible digitalization through digital technologies and green practices

Responsible digitalization through digital technologies and green practices

Human-Robot Interaction Review: Challenges and Solutions for Modern Industrial Environments

Reinforcement learning for facilitating human-robot-interaction in manufacturing

Contact Info

Product

Resources

About