Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

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A systematic literature review of decision-making and control systems for autonomous and social robots

In the last years, considerable research has been carried out to develop robots that can improve our quality of life during tedious and challenging tasks. In these contexts, robots operating without human supervision open many possibilities to assist people in their daily activities. When autonomous robots collaborate with humans, social skills are necessary for adequate communication and cooperation. Considering these facts, endowing autonomous and social robots with decision-making and control models is critical for appropriately fulfiling their initial goals. This manuscript presents a systematic review of the evolution of decision-making systems and control architectures for autonomous and social robots in the last three decades. These architectures have been incorporating new methods based on biologically inspired models and Machine Learning to enhance these systems’ possibilities to developed societies. The review explores the most novel advances in each application area, comparing their most essential features. Additionally, we describe the current challenges of software architecture devoted to action selection, an analysis not provided in similar reviews of behavioural models for autonomous and social robots. Finally, we present the future directions that these systems can take in the future.The research leading to these results has received funding from the projects: Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), RTI2018-096338-B-I00, funded by the Ministerio de Ciencia, Innovación y Universidades; Robots sociales para mitigar la soledad y el aislamiento en mayores (SOROLI), PID2021-123941OA-I00, funded by Agencia Estatal de Investigación (AEI), Spanish Ministerio de Ciencia e Innovación. This publication is part of the R&D&I project PLEC2021-007819 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Neurophysiological models of gaze control in Humanoid Robotics

This work present a robotic implementation of a neurophysiological model of rapid orienting gaze shifts in humans, with the final goal of model parameters validation and tuning. The quantitative assessment of robot performance confirmed a good ability to foveate the target with low residual errors around the desired target position. Furthermore, the ability to maintain the desired position was good and the gaze fixation after the saccadic movement was executed with only few oscillations of the head and eye. This is because the model required a very high dynamic. 9.1. Robotic point of view The head and eye residual oscillations increase linearly with increasing amplitude. In Fig. 16 is evident that the residual gaze oscillation is less than head. This is explained with the compensation introduced by the eye oscillations which compensate the gaze which becomes more stable. We explain these findings by observing that the accelerations required to execute (or stopand-invert) the movement are very high especially for the eye movement. Even if the robotic head was designed to match the human performances (in terms of angle and velocities) in its present configuration it is still not capable produce such accelerations. This is particularly evident for the movement of the eye because the motor has to invert its rotation when the fixation point is first achieved. With respect to the timing of the movement it has been found that the results of the experiments are in close accordance to the data available on humans (Goossens and Van Opstal, 1997). The same conclusion may be drawn for the shapes of the coordinated movement that can be directly compared to the typical examples reported in Fig. 14. Figure 16, 17 show that the model is capable of providing inadequate control of the redundant platform. The system response is very fast, due to the robotic head platform design. TGst time take into account the problem of eye-head coordination and the very high acceleration. The head is voluntarily delayed less than 30 millisecond after eye movement, according to human physiology, by means of Ph block (Goossens and Van Opstal ,1997). 9.2. Neurophysiological point of view A typical robotic eye-head movement is shows in Fig. 14

The BioS4You European project: An innovative way to effectively engage Z-generation students in STEM disciplines

In this contribution, we present the BioS4You project and analyse the results obtained in the first 18 months of its activity. The “Bio-Inspired STEM topics for engaging young generations” (BioS4You) Erasmus+ KA2 Innovation project aims to bridge the gap between STEM national curricula (which include Science, Technology, Engineering, and Mathematics) and the needs of Z-generation students, uninterested to basic themes, but enthusiastic in issues related to environmental, social, and health concerns. The BioS4You project engages young learners in STEM subjects, starting with current issues of interest for them, as the social and environmental impact of new technologies, connecting STEM concepts to real-world technologies that are supporting on facing environmental, social, and health current challenges. Novel fields such as Bioengineering, Bioscience, Biotechnology can be implemented into classroom teaching, integrating academic disciplines, and stimulating the academic and social growth of young people. The knowledge of new STEM contents makes the students feel an active part of the technological innovation (and not just passive users) and help them to build a better future, bringing them closer to the STEM world and enabling them to make more informed choices for their future careers

Artificial intelligence within the interplay between natural and artificial computation:Advances in data science, trends and applications

Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.BMS - Pfizer(U01 AG024904). Spanish Ministry of Science, projects: TIN2017-85827-P, RTI2018-098913-B-I00, PSI2015-65848-R, PGC2018-098813-B-C31, PGC2018-098813-B-C32, RTI2018-101114-B-I, TIN2017-90135-R, RTI2018-098743-B-I00 and RTI2018-094645-B-I00; the FPU program (FPU15/06512, FPU17/04154) and Juan de la Cierva (FJCI-2017–33022). Autonomous Government of Andalusia (Spain) projects: UMA18-FEDERJA-084. Consellería de Cultura, Educación e Ordenación Universitaria of Galicia: ED431C2017/12, accreditation 2016–2019, ED431G/08, ED431C2018/29, Comunidad de Madrid, Y2018/EMT-5062 and grant ED431F2018/02. PPMI – a public – private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc

Swarm Intelligence

Swarm Intelligence has emerged as one of the most studied artificial intelligence branches during the last decade, constituting the fastest growing stream in the bio-inspired computation community. A clear trend can be deduced analyzing some of the most renowned scientific databases available, showing that the interest aroused by this branch has increased at a notable pace in the last years. This book describes the prominent theories and recent developments of Swarm Intelligence methods, and their application in all fields covered by engineering. This book unleashes a great opportunity for researchers, lecturers, and practitioners interested in Swarm Intelligence, optimization problems, and artificial intelligence

The BioS4You European Project: An Innovative Way to Effectively Engage Z-Generation Students in STEM Disciplines

In this contribution, we present the BioS4You project and analyse the results obtained in the first 18 months of its activity. The “Bio-Inspired STEM topics for engaging young generations” (BioS4You) Erasmus+ KA2 Innovation project aims to bridge the gap between STEM national curricula (which include Science, Technology, Engineering, and Mathematics) and the needs of Z-generation students, uninterested to basic themes, but enthusiastic in issues related to environmental, social, and health concerns. The BioS4You project engages young learners in STEM subjects, starting with current issues of interest for them, as the social and environmental impact of new technologies, connecting STEM concepts to real-world technologies that are supporting on facing environmental, social, and health current challenges. Novel fields such as Bioengineering, Bioscience, Biotechnology can be implemented into classroom teaching, integrating academic disciplines, and stimulating the academic and social growth of young people. The knowledge of new STEM contents makes the students feel an active part of the technological innovation (and not just passive users) and help them to build a better future, bringing them closer to the STEM world and enabling them to make more informed choices for their future careers