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.