Symmetry-Breaking and Fault-Tolerance Analysis of a Twelve-Legged Jansen Robot Using a Hybrid FEA-ANFIS Framework
SYMMETRY, cilt.18, sa.7, ss.1068-1105, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 18 Sayı: 7
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/sym18071068
- Dergi Adı: SYMMETRY
- Derginin Tarandığı İndeksler: Academic Search Ultimate (EBSCO), Scopus, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest), Science Citation Index Expanded (SCI-EXPANDED), INSPEC, zbMATH
- Sayfa Sayıları: ss.1068-1105
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- İstanbul Gelişim Üniversitesi Adresli: Evet
Özet
This study presents a comprehensive symmetry-breaking analysis framework for a twelvelegged Jansen walking robot, integrating finite element analysis (FEA) with adaptive neurofuzzy
inference system (ANFIS) surrogate modeling. A systematic dataset of 210 cases was generated by combining 21 single- and multi-leg failure scenarios across 10 load levels
(20–200 N) on the PLA-based 3D-printed prototype. Two novel dimensionless metrics are introduced: the Resilience Index (RI), quantifying the proportional stress increase relative
to the baseline, and the Asymmetry Index (AI), measuring leg-reaction force distribution imbalance. Results identify a clear fault-tolerance threshold between two- and four-leg
failures: single-leg failures remain at LOW risk (RI < 0.20), while three-leg asymmetric failures (S18) reach CRITICAL level (RI = 1.13, ~97% of PLA yield strength). A hybrid
machine learning framework is proposed, applying ANFIS to maximum stress (R2 = 0.817) and safety factor (R2 = 0.936) predictions, while reserving FEA tables for bimodal outputs.
The ANFIS surrogate achieves approximately 106× speedup over FEA (262.6 μs vs. 5–8 min), enabling real-time fault diagnosis and digital twin applications. The framework
is generalizable to other multi-legged robotic systems requiring fault-tolerance evaluation.