Optimization of Benching Dragon Motion Trajectory Based on Area Collision Detection and Trust Region Method
DOI:
https://doi.org/10.54097/kxpyw314Keywords:
Area collision detection, Trust region algorithm, Isometric spirals.Abstract
"Dragon on Benches" constitutes an activity entailing the end-to-end connection of multiple benches to shape a sinuous and tortuous dragon-like structure. This activity not merely exhibits rich cultural connotations but also accentuates the skills and aesthetic aspects of the performance. In this paper, the study proposes zero in on the collision prediction during the movement of the Dragon on Benches. A collision detection model founded on the area method is put forward ,and ascertain that the terminal time of the dragon's entry into the helix is 432.18 seconds. Under the given pitch condition, a minimum path model is built and the trust-region algorithm is employed to optimize the turning path. The shortest turning path fulfilling all geometric conditions is discovered to be 13.6212 cm. The research outcomes not only enrich the cultural connotations of the movement of the Dragon on Benches but also offer a novel perspective for collision prediction and path optimization in similar dynamic structures.
Downloads
References
[1] Zhang W, Wan W, Wang W. The Evolution and Review of the Cultural Ecology of Village Sports Performances: A Study on the “Bench Dragon” in Chongren[J]. Academic Journal of Humanities & Social Sciences, 2024, 7(8): 18-23.
[2] Pan D, Sirisuk M. Collective Memory Construction and Educational Inheritance of Ritual Practices of Bench Dragon Performance in Pujiang, China: Educational Inheritance of Ritual Practices of Bench Dragon Performance[J]. International Journal of Curriculum and Instruction, 2023, 15(3): 2232-2250.
[3] Lv H, Liu L, Gao Y, et al. A compound planning algorithm considering both collision detection and obstacle avoidance for intelligent demolition robots[J]. Robotics and Autonomous Systems, 2024, 181: 104781.
[4] Huang H, Wei X, Zhou Y. An overview on twin support vector regression[J]. Neurocomputing, 2022, 490: 80-92.
[5] Birgin E G, Martínez J M, Raydan M. Spectral projected gradient methods[M]//Encyclopedia of optimization. Cham: Springer International Publishing, 2024: 1-9.
[6] Gielis J, Caratelli D, Shi P, et al. A note on spirals and curvature[J]. Growth and form, 2020, 1(1): 1-8.
[7] Katsampiris-Salgado K, Haninger K, Gkrizis C, et al. Collision detection for collaborative assembly operations on high-payload robots[J]. Robotics and Computer-Integrated Manufacturing, 2024, 87: 102708.
[8] Cormen T H, Leiserson C E, Rivest R L, et al. Introduction to algorithms [M]. MIT press, 2022.
[9] Sangwin C. On Heron’s formula for the area of a plane triangle [J]. The College Mathematics Journal, 2024: 1-10.
[10] Yuan Y. Recent advances in trust region algorithms [J]. Mathematical Programming, 2015, 151: 249-281.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Highlights in Science, Engineering and Technology

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.







