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Modular design automation of intelligent robot systems /

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...

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Bibliographic Details
Main Author: Fan, Zhun
Other Authors: Wang, Zhaojun, Li, Wenji, Zhu, Guijie, Zhuang, Jiafan
Format: eBook
Language:English
Published: Hoboken, New Jersey : John Wiley & Sons, 2026.
Subjects:
Robots > Design and construction.
Mechanical engineering.
Robotics.
Technology.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Acronyms ix
  • 1 Introduction to Design Automation and Intelligent Robotic Systems 1
  • 1.1 Background of Design Automation 1
  • 1.1.1 Historical Evolution 1
  • 1.1.2 Importance in Modern Engineering 3
  • 1.2 Intelligent Robotic Systems 3
  • 1.2.1 Definition and Components 3
  • 1.2.2 Challenges in Design and Optimization 4
  • 1.3 Overview of MODENA 5
  • 1.4 Objectives and Significance of the Book 6
  • 2 Foundations of MODENA 9
  • 2.1 Principles of Modular Design Automation 9
  • 2.2 Evolutionary Computation in MODENA 11
  • 2.2.1 Fundamentals 11
  • 2.2.2 Application in Design Automation 18
  • 2.3 Neural Architecture Search 29
  • 2.3.1 Theory and Algorithms 30
  • 2.3.2 Application in Design Automation 31
  • 2.4 Causal Discovery 35
  • 2.5 Comparison with Traditional and LLM-based Methods 38
  • 2.5.1 Contrast with Traditional Approaches 38
  • 2.5.2 Differences from LLM-based Methods 39
  • 3 Optimization Methods in MODENA 41
  • 3.1 General Framework of PPS 42
  • 3.2 Pps-moea/d 46
  • 3.2.1 Basic Description of PPS-MOEA/D 46
  • 3.2.2 Experimental Study 53
  • 3.2.3 Conclusion 63
  • 3.3 Pps-m2m 63
  • 3.3.1 Basic Description of M2M Strategy 64
  • 3.3.2 PPS Search Strategy 64
  • 3.3.3 Combination of PPS with M2M 66
  • 3.3.4 Key Differences Between Two PPS-based Algorithms 69
  • 3.3.5 Experiment Results 69
  • 3.3.6 Conclusion 84
  • 3.4 Sa-pps 85
  • 3.4.1 Proposed Method 85
  • 3.4.2 Push Search Stage 88
  • 3.4.3 Pull Search Stage 89
  • 3.4.4 BatchBALDS 95
  • 3.4.5 Experimental Study 98
  • 3.4.6 Conclusion 104
  • 3.5 Genetic U-Net 105
  • 3.5.1 Search Space and Encoding Mechanism 106
  • 3.5.2 Evolutionary Algorithm 110
  • 3.5.3 Experimental Setup 117
  • 3.5.4 Results and Analysis 119
  • 3.5.5 Conclusion 123
  • 3.6 Evolving Hybrid Bond Graph Using GP 123
  • 3.6.1 Bond Graph and GP 126
  • 3.6.2 Basic Primitives 128
  • 3.6.3 Genetic Operators 133
  • 3.6.4 Conclusion 137
  • 4 Application of MODENA 139
  • 4.1 Morphology Design Automation 139
  • 4.1.1 Teaching Manipulator Design 140
  • 4.1.2 Optimal Design of Drive Mechanism for Electric Typewriter 153
  • 4.2 Controller Design Automation 164
  • 4.2.1 Discrete Controller Design for Hybrid Mechatronic Systems 164
  • 4.2.2 Evolution Design for Vehicle Suspension System 185
  • 4.3 Vision System Design Automation 197
  • 4.3.1 Optimization Design for the Retinal Vascular System 197
  • 4.3.2 Causal Feature Selection for Strabismus Diagnosis Using GNN 210
  • 5 MODENA in Swarm Robotics 217
  • 5.1 Introduction 217
  • 5.2 Automated Swarm Pattern Generation for Swarm Robots 218
  • 5.2.1 Background and Problem Formulation 218
  • 5.2.2 Automated GRN Model Structure Design 221
  • 5.2.3 Experimental Results 228
  • 5.2.4 Conclusion 240
  • 5.3 Swarm Control in Communication-denied Environments 240
  • 5.3.1 Method Architecture of VG-Swarm 241
  • 5.3.2 Experimental Setup 249
  • 5.3.3 Experimental Results 251
  • 5.3.4 Conclusion 254
  • 5.4 Vision-based Distributed Multi-UAV Collision Avoidance 255
  • 5.4.1 Related Work 256
  • 5.4.2 Proposed Method 257
  • 5.4.3 Experiments and Results 262
  • 5.4.4 Conclusion 268
  • 5.5 Multi-UAV Collision Avoidance via Causal Representation Learning 268
  • 5.5.1 Related Work 268
  • 5.5.2 Proposed Method 271
  • 5.5.3 Experiment Results and Analysis 274
  • 5.5.4 Conclusion 278
  • 6 Conclusions and Future Directions 279
  • 6.1 Conclusions 279
  • 6.2 Research Prospects 279
  • References 283
  • Index 319.