Dynamic Models Of Energy Robotic And Biological Systems
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Dynamic Models of Energy, Robotic, and Biological Systems
Author: Jose de Jesus Rubio
language: en
Publisher: Springer Nature
Release Date: 2025-05-30
Dynamic models are essential for understanding the system dynamics. It is of importance because one mistake in experiments could cause accidents or damages, while one mistake in the simulation of dynamic models could cause nothing. Each system has a different dynamic model; hence, this book presents the designs of 10 dynamic models which are mainly classified in two ways. The first kind of dynamic models are mainly obtained by the Euler Lagrange method and described by differential equations. The second kind of dynamic models are mainly obtained by the neural networks and described by difference equations. Topics and features: Contains the dynamic models of energy systems Derives dynamic models of energy systems by the Euler Lagrange method Includes the dynamic models of robotic systems Contains the dynamic models of biological systems Derives dynamic models of robotic systems by the Euler Lagrange method Obtains dynamic models of biological systems by neural networks This book is expected to be used primary by researchers and secondary by students and in the areas of control, robotics, energy, biological, mechanical, mechatronics, and computing systems. Jose de Jesus Rubio, Alejandro Zacarias, and Jaime Pacheco are full Professors affiliated with the ESIME Azcapotzalco, Instituto Politécnico Nacional, Sección de Estudios de Posgrado e Investigación, Ciudad de México, México.
Robots and Biological Systems: Towards a New Bionics?
Author: Paolo Dario
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Bionics evolved in the 1960s as a framework to pursue the development of artificial systems based on the study of biological systems. Numerous disciplines and technologies, including artificial intelligence and learningdevices, information processing, systems architecture and control, perception, sensory mechanisms, and bioenergetics, contributed to bionics research. This volume is based on a NATO Advanced Research Workshop within the Special Programme on Sensory Systems for Robotic Control, held in Il Ciocco, Italy, in June 1989. A consensus emerged at the workshop, and is reflected in the book, on the value of learning from nature in order to derive guidelines for the design of intelligent machines which operate in unstructured environments. The papers in the book are grouped into seven chapters: vision and dynamic systems, hands and tactile perception, locomotion, intelligent motor control, design technologies, interfacing robots to nervous systems, and robot societies and self-organization.
Bio A.I. - From Embodied Cognition to Enactive Robotics
Even before the deep learning revolution, the landscape of artificial intelligence (AI) was already changing drastically in the 90s. Embodied intelligence, it was proposed, must play a crucial role in the design of intelligent machines. This new wave was inspired by what is today known as Embodied and Enactive Cognitive Science or E-Cognition, which considers that cognitive activity does not reduce to the intellectual capacities of agents being able to represent their environments. E-cognition set AI and robotics in a new direction, in which intelligent machines are required to interact with the environment, and where this interaction does not reduce to explicit representations or prespecified algorithms. These ideas revolutionized the way we think about intelligent machines and cognition, but these theoretical advances are only partially reflected in modern approaches to AI and machine learning (ML). Despite deeply impressive achievements, AI/ML still struggles to recapitulate the kinds of intelligence we find in natural systems, whether we are considering individual insects (e.g. simultaneous localization and mapping), or swarm behaviour (e.g. forum sensing and ensemble inferences), and especially the kinds of flexibility and high-level reasoning characteristic of human cognition.