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.

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Several decades of development in the fields of robotics and automation has resulted in human-robot-interaction being commonplace, and the subject of intense study. These interactions are particularly prevalent in manufacturing, where human operators have been employed in a number of robotics and automation tasks. The presence of human operators continues to be a source of uncertainty in such systems, despite the study of human factors, in an attempt to better understand these variations in performance. Concurrent developments in intelligent manufacturing present opportunities for adaptability within robotic control. This work examines relevant human factors and develops a framework for integrating the necessary elements of intelligent control and data processing to provide appropriate adaptability to robotic elements, consequently improving collaborative interaction with human colleagues. A neural network-based learning approach is used to predict the influence on human task performance and use these predictions to make informed changes to programmed behaviour, and a methodology developed to further explore the application of learning techniques to this area. The work is supported by an example case-study, in which a simulation model is used to explore the application of the developed system, and its performance in a real-world production scenario. The simulation results reveal that adaptability can be realised with some relatively simple techniques and models if applied in the right manner and that such adaptability is helpful to tackle the issue of performance disparity in manufacturing operations. NTP: This paper presents research into the application of intelligent methodologies to this problem and builds a framework to describe how this information can be captured, generated and used, within manufacturing production processes. This framework helps identify which areas require further research and serves as a basis for the development of a methodology, by which a control system may enable adaptable behaviour to reduce the impact of human performance variation and improve human-machine-interaction. The paper also presents a simulation-based case study, to support the development and evaluate the presented control system on a representative real-world problem. The methodology makes use of a machine learning approach to identify the complex influence of a number of identified human factors on human performance. This knowledge can be used to adjust the robotic behaviour to match the predicted performance of a number of different operators over a number of scenarios. The adaptability reduces performance disparity, reducing idle times and enabling leaner production through WIP reduction. Future work

The implementation of automation has become a common occurrence in recent years, and automated robotic systems are actively used in many manufacturing processes. However, fully automated manufacturing systems are far less common, and human operators remain prevalent. The resulting scenario is one where human and robotic operators work in close proximity, and directly affect the behavior of one another. Conversely to their robotic counterparts, human beings do not share the same level of repeatability or accuracy, and as such can be a source of uncertainty in such processes. Concurrently, the emergence of intelligent manufacturing has presented opportunities for adaptability within robotic control. This work examines relevant human factors and develops a learning model to examine how to utilize this knowledge and provide appropriate adaptability to robotic elements, with the intention of improving collaborative interaction with human colleagues, and optimized performance. The work is supported by an example case-study, which explores the application of such a control system, and its performance in a real-world production scenario.