Modular Design Automation of Intelligent Robot Systems

Modular Design Automation of Intelligent Robot Systems book cover

Modular Design Automation of Intelligent Robot Systems

Author(s): Zhun Fan (Author), Zhaojun Wang (Author), Wenji Li (Author), Guijie Zhu (Author), Jiafan Zhuang (Author)

  • Publisher finelybook 出版社: Wiley-IEEE Press
  • Publication Date 出版日期: February 10, 2026
  • Edition 版本: 1st
  • Language 语言: English
  • Print length 页数: 336 pages
  • ISBN-10: 1394318499
  • ISBN-13: 9781394318490

Book Description

Comprehensive guide to streamlining the design optimization process of intelligent robots using MODENA (MOdular DEsigN Automation)

Modular Design Automation of Intelligent Robot Systems introduces MODENA (MOdular DEsigN Automation) as a new approach to the design of intelligent robots by harnessing computational intelligence techniques like genetic programming and constrained multi-objective evolutionary algorithms. It also covers different aspects of robot design, including physical structure, control, and vision systems. Case studies are included throughout the text to aid in the practical application of concepts.

The book also provides information on:

  • Methods in the design automation of electronic systems, micro-electro-mechanical systems (MEMS), complex mechatronic systems, and intelligent robotic systems
  • MODENA’s advantage in avoiding the frequent trial-and-error processes inherent in traditional design methods, instead promoting the automatic discovery of innovative designs
  • MODENA as a more interpretable alternative to supplement ChatGPT and other generative AI based on LLMs
  • Automated design of intelligent robotic systems like teaching manipulators, mechanical printers, and quarter-car suspensions as well as swarm robotic systems

Modular Design Automation of Intelligent Robot Systems is an excellent reference on the subject for professionals, researchers, and students in robotics, automation, and artificial intelligence.

From the Back Cover

Comprehensive guide to streamlining the design optimization process of intelligent robots using MODENA (MOdular DEsigN Automation)

Modular Design Automation of Intelligent Robot Systems introduces MODENA (MOdular DEsigN Automation) as a new approach to the design of intelligent robots by harnessing computational intelligence techniques like genetic programming and constrained multi-objective evolutionary algorithms. It also covers different aspects of robot design, including physical structure, control, and vision systems. Case studies are included throughout the text to aid in the practical application of concepts.

The book also provides information on:

  • Methods in the design automation of electronic systems, micro-electro-mechanical systems (MEMS), complex mechatronic systems, and intelligent robotic systems
  • MODENA’s advantage in avoiding the frequent trial-and-error processes inherent in traditional design methods, instead promoting the automatic discovery of innovative designs
  • MODENA as a more interpretable alternative to supplement ChatGPT and other generative AI based on LLMs
  • Automated design of intelligent robotic systems like teaching manipulators, mechanical printers, and quarter-car suspensions as well as swarm robotic systems

Modular Design Automation of Intelligent Robot Systems is an excellent reference on the subject for professionals, researchers, and students in robotics, automation, and artificial intelligence.

About the Author

Zhun Fan, PhD, is a Full Professor at the Shenzhen Institute for Advanced Study, University of Electronic Science and Technology, China.

Zhaojun Wang is currently pursuing a PhD degree with the Department of Civil Engineering, College of Engineering at Shantou University.

Wenji Li, PhD, is a Lecturer in the Department of Electronics at the College of Engineering, Shantou University.

Guijie Zhu is currently pursuing a PhD degree with the Department of Civil Engineering, College of Engineering at Shantou University.

Jiafan Zhuang, PhD, joined Shantou University as a lecturer in September of 2022, and currently leads a computer vision research group.

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