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Language: search.filters.language.EnglishSubject: search.filters.subject.Industry 4.0

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    Towards a Distributed-Based Learning Robot from Scratch via Neuro-Evolutionary Computation
    (Springer Nature Switzerland, 2025)
    Ponce, Hiram  
    ;
    Martinez-Villaseñor, Lourdes  
    In the context of Industry 4.0, multi-robot systems (MRS) have become essential for enhancing the adaptability and efficiency of flexible manufacturing systems, enabling rapid responses to market demands through personalized customization. Effective collaboration among multiple robots requires advanced communication, shared goal alignment, and the ability to gather and process environmental data to execute coordinated actions with precision. Despite their advantages, improving efficiency of robot learning still remains a crucial challenge, particularly in flexible manufacturing multi-robot systems, where generalization across diverse scenarios is essential for effective deployment. In this work, we propose the use of neuro-evolutionary computation to solve the particular learning-from-scratch problem in a multi-robot system. This approach consists of an architecture of a decentralized MRS in which a local controller per robot is based on Artificial Hydrocarbon Networks machine learning model. Also, it includes a learning strategy via Wound Treatment Optimization algorithm. We implement the architecture in a simulated environment to solve the mountain car domain. Experimental results and a comparison with reinforcement learning validate the ability of this approach to learn a task from scratch without prior explicit data. We anticipate the applicability of this approach in smart factories or autonomous vehicles. ©The authors ©Springer.
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    Profile of the Business Science Professional for the Industry 4.0
    (2022)
    Rodríguez Aguilar, Román  
    The development of the fourth industrial revolution called Industry 4.0 has generated a significant boost in areas related to information technology. However, this development has permeated into other areas such as business sciences. Based on a systematic literature review, the main areas of development of business sciences were identified within the framework of industry 4.0, this, in turn, generates the need to update profiles and capacities for professionals. The large areas identified are auditing, finance, accounting, and planning, among others. The need for the comprehensive development of all areas of knowledge in the digital age is evident. Changes in the mode of production, trade, and interaction between individuals have permeated all areas and business science is no exception. © Springer Nature
    Scopus© Citations 2  9  2