A comprehensive study on current and future trends towards the characteristics and enablers of industry 4.0

After the emergence of industry 4.0 and the continuous technological development, it became vital for industries to transfer mass production expertise into personalized products in small batches. Clients became more aware of their needs and start basing their decision on specific quality requirements, lower cost, and the shortest delivery date. This is where collaborative robots intervene, these structures can work hand in hand with operators and take charge of hard, long, or repetitive tasks in a fast, precise, and robust manner. Although these structures have great potential, they lack flexibility and adaptability, these aspects can only be found in humans. The workforce competencies and performance are the ultimate precursors to any proper industrial evolution. Performances and competencies workforce must go further than the standard definitions attributed to them. This paper addresses the scheduling problem, our proposition relies on the assumption that the final programs attributed to collaborative robots can be divided into standard sub- programs. Based on the similarities between sub-programs can help propose a schedule that reduces significantly wasted time developing new programs or going from one program to another. This paper will also address the dissociation between human and robots’ performances in a context where humans and robots work in very dependent proximity. Finally, we will also propose a new definition of workload performance while highlighting its dynamic aspect in terms of fatigue, motivation, and the correlation between repetition and the learning process.

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Optimal scheduling in a Collaborative robot environment and evaluating workforce dynamic performance

Picking with a robot colleague: A systematic literature review and evaluation of technology acceptance in human-robot collaborative warehouses

The control field in Industry 4.0 creates a twin of an object or a business process for managing it. An automated system is an optimization tool because the digital twin allows offline optimization, which improves the efficiency of the process. Thus, modeling and characterization become a fundamental part of control processes. However, determining the mathematical model for predicting behavior in an industrial environment implies a high level of physics, mathematics, system analysis, simulation, parameterization, and validation. On the other hand, Artificial Intelligence (AI) applications include supporting control theory methodologies and replacing classical techniques with alternatives that do not require a mathematical model or can determine it automatically. This chapter proposes an intelligent control assistant for Industry 4.0 capable of characterizing and controlling systems automatically. In addition, we implement the first stage for generating the Transfer Function (TF). The novelty in our proposal is that we define a multiobjective cost function that integrates the time-response parameters and the Mean Absolute Error (MAE) between the generated model and the plant response. Our approach uses the control theory formulas for extracting the time-response parameters while maintaining low dimensionality in the search space by optimizing only the required parameters in the TF general description. 

Colombet Emeric

Papers

Development of a new robotic programming support system for operators