Volume 50 Issue 8
Aug.  2024
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HE T Y,DONG Y,ZHOU S M,et al. Performance analysis and optimization of buffering/walking integrated lunar probe[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(8):2547-2556 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0634
Citation: HE T Y,DONG Y,ZHOU S M,et al. Performance analysis and optimization of buffering/walking integrated lunar probe[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(8):2547-2556 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0634

Performance analysis and optimization of buffering/walking integrated lunar probe

doi: 10.13700/j.bh.1001-5965.2022.0634
Funds:  National Natural Science Foundation of China (U2037602)
More Information
  • Corresponding author: E-mail:zhaojunpeng@buaa.edu.cn
  • Received Date: 20 Jul 2022
  • Accepted Date: 04 Oct 2022
  • Available Online: 14 Nov 2022
  • Publish Date: 11 Nov 2022
  • Taking into account the buffering and walking characteristics, a performance analysis and landing leg configuration optimization method is proposed to address the issue of the lunar probe integrating landing buffer and lunar walking functions, which causes challenges in its design optimization. According to the functional requirements of the landing legs of the lunar probe, a redundant DOF parallel mechanism with RUP-2RUPS configuration is proposed, and its parametric model is constructed. The mechanism can realize the configuration switching of buffering and walking functions by activating and deactivating the kinematic pairs. Combined with the full factor experimental method, the kinematic and dynamic characteristics of the lunar probe with two functions are analyzed. The objective function and constraint conditions of comprehensive optimization are given based on the intricate landing and walking conditions on the lunar surface. Based on the sensitivity analysis, the configuration parameters of the landing leg of the lunar probe are optimized by using the full-condition response surface model updated with the optimization process and the non-inferior sorting genetic algorithm. This optimization method not only improves the operation efficiency, but also ensures that each round of optimization can select the limit value of the worst working condition of the current configuration. After optimization, the maximum effective working space is increased by 8.7%, the minimum anti-overturning performance is increased by 4.0%, and the minimum anti-bottom-touchdown performance is increased by 0.2%. The overall performance is better.

     

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