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The goal of this textbook is to equip readers with as structured knowledge of soft robotics as possible. Seeking to overcome the limitations of conventional robots by making them more flexible, gentle and adaptable, soft robotics has become one of the most active fields over the last decade. Soft robotics is also highly interdisciplinary, bringing together robotics, computer science, material science, biology, etc.After the introduction, the content is divided into three parts: Design of Soft Robots; Soft Materials; and Autonomous Soft Robots. Part I addresses soft mechanisms, biological mechanisms, and soft manipulation & locomotion. In Part II, the basics of polymer, biological materials, flexible & stretchable sensors, and soft actuators are discussed from a materials science standpoint. In turn, Part III focuses on modeling & control of continuum bodies, material intelligence, and information processing using soft body dynamics. In addition, the latest research resultsand cutting-edge research are highlighted throughout the book.Written by a team of researchers from highly diverse fields, the work offers a valuable textbook or technical guide for all students, engineers and researchers who are interested in soft robotics.

This book offers a practical introduction to the use of artificial intelligence (AI) techniques to improve and optimise the various phases of the software development process, from the initial project planning to the latest deployment. All chapters were written by leading experts in the field and include practical and reproducible examples. Following the introductory chapter, Chapters 2-9 respectively apply AI techniques to the classic phases of the software development process: project management, requirement engineering, analysis and design, coding, cloud deployment, unit and system testing, and maintenance. Subsequently, Chapters 10 and 11 provide foundational tutorials on the AI techniques used in the preceding chapters: metaheuristics and machine learning. Given its scope and focus, the book represents a valuable resource for researchers, practitioners and students with a basic grasp of software engineering.

This book provides a basic yet unified overview of theory and methodologies for evolutionary developmental systems. Based on the author¿s extensive research into the synergies between various approaches to artificial intelligence including evolutionary computation, artificial neural networks, and systems biology, it also examines the inherent links between biological intelligence and artificial intelligence. The book begins with an introduction to computational algorithms used to understand and simulate biological evolution and development, including evolutionary algorithms, gene regulatory network models, multi-cellular models for neural and morphological development, and computational models of neural plasticity. Chap. 2 discusses important properties of biological gene regulatory systems, including network motifs, network connectivity, robustness and evolvability. Going a step further, Chap. 3 presents methods for synthesizing regulatory motifs from scratch and creating more complex regulatory dynamics by combining basic regulatory motifs using evolutionary algorithms. Multi-cellular growth models, which can be used to simulate either neural or morphological development, are presented in Chapters 4 and 5. Chap. 6 examines the synergies and coupling between neural and morphological evolution and development. In turn, Chap. 7 provides preliminary yet promising examples of how evolutionary developmental systems can help in self-organized pattern generation, referred to as morphogenetic self-organization, highlighting the great potentials of evolutionary developmental systems. Finally, Chap. 8 rounds out the book, stressing the importance and promise of the evolutionary developmental approach to artificial intelligence.Featuring a wealth of diagrams, graphs and charts to aid in comprehension, this book offers a valuable asset for graduate students, researchers and practitioners who are interested in pursuing a different approach to artificial intelligence.