Hierarchical Multi-Objective Optimization for Precise Performance Design of Closed-Chain Legged Mechanisms
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909531918827520 |
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| author | Guo, Long Zhang, Ying Qin, Qi Liu, Guanjun Chen, Hanyu Yao, Yan-an |
| author_facet | Guo, Long Zhang, Ying Qin, Qi Liu, Guanjun Chen, Hanyu Yao, Yan-an |
| contents | Over the past decades, the performance design of closed-chain legged mechanisms (CLMs) has not been adequately addressed. Most existing design methodologies have predominantly relied on trajectory synthesis, which inadvertently prioritizes less critical performance aspects. This study proposes a hierarchical multi-objective optimization strategy to address this limitation. First, the numerical performance-trajectory mapping is derived based on a foot-ground contact model, aiming to decouple the performance characteristics. Subsequently, a hierarchical optimization strategy is employed for two CLM design scenarios: In trajectory shape-constrained scenarios, a coarse-to-fine optimization process, integrating Fourier descriptors, refines the design from overall shape to local features. In scenarios without trajectory shape constraints, a stepwise optimization process is proposed for reconfigurable CLMs to transition from primary motion to auxiliary motion. The robustness of the proposed design strategy is validated across three configurations and seven algorithms. The effectiveness of the proposed design strategy is verified by comparison with other existing CLM design methods. The applicability of the proposed strategy is confirmed through simulation and prototype experiments. The results demonstrate that the hierarchical strategy effectively addresses the challenges of precise performance design in CLMs. Our work provides a general framework for the CLM design and offers insights for the optimization design of other closed-chain linkages. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06533 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Multi-Objective Optimization for Precise Performance Design of Closed-Chain Legged Mechanisms Guo, Long Zhang, Ying Qin, Qi Liu, Guanjun Chen, Hanyu Yao, Yan-an Computational Engineering, Finance, and Science Over the past decades, the performance design of closed-chain legged mechanisms (CLMs) has not been adequately addressed. Most existing design methodologies have predominantly relied on trajectory synthesis, which inadvertently prioritizes less critical performance aspects. This study proposes a hierarchical multi-objective optimization strategy to address this limitation. First, the numerical performance-trajectory mapping is derived based on a foot-ground contact model, aiming to decouple the performance characteristics. Subsequently, a hierarchical optimization strategy is employed for two CLM design scenarios: In trajectory shape-constrained scenarios, a coarse-to-fine optimization process, integrating Fourier descriptors, refines the design from overall shape to local features. In scenarios without trajectory shape constraints, a stepwise optimization process is proposed for reconfigurable CLMs to transition from primary motion to auxiliary motion. The robustness of the proposed design strategy is validated across three configurations and seven algorithms. The effectiveness of the proposed design strategy is verified by comparison with other existing CLM design methods. The applicability of the proposed strategy is confirmed through simulation and prototype experiments. The results demonstrate that the hierarchical strategy effectively addresses the challenges of precise performance design in CLMs. Our work provides a general framework for the CLM design and offers insights for the optimization design of other closed-chain linkages. |
| title | Hierarchical Multi-Objective Optimization for Precise Performance Design of Closed-Chain Legged Mechanisms |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2503.06533 |