Abstract
As a rapidly developing robot type, humanoid robots still have the problem of excessive mass of structural components. Most of the existing lightweight methods usually add dangerous load conditions in the robot movement to optimize, but ignore the load conditions at other moments in the movement process. In this paper, a new lightweight design method for humanoid robot is proposed by combining gait simulation and generative design. The walking motion simulation of the robot is conducted to obtain the load conditions of the robot calf during the movement. Then, load conditions at multiple moments are applied for generative design, and the optimized model of the calf is obtained. Furthermore, the calf model designed by this method is compared with the topology optimization model and the generative design models under dangerous conditions. Finally, the knee joint operation test is carried out. The results show that the calf model got from the generative design method based on gait simulation has better lightweight effect and mechanical performance than the other models. Therefore, the lightweight design method of humanoid robots combining generative design and gait simulation is effective and promising.
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References
Jin X, Fang Y, Zhang D, Zhang H (2020) Synthesis of 3-[P][S] parallel mechanism-inspired multimode dexterous hands with parallel finger structure. J Mech Design. https://doi.org/10.1115/1.4045506
Mahum R, Butt FS, Ayyub K, Islam S, Nawaz M, Abdullah D (2017) A review on humanoid robots. Int J Adv Appl Sci 4(2):83–90. https://doi.org/10.21833/ijaas.2017.02.015
Rasheed T, Long P, Caro S (2020) Wrench-feasible workspace of mobile cable-driven parallel robots. J Mech Robot. https://doi.org/10.1115/1.4045423
Briot S, Kaci L, Boudaud C, Llevat Pamiès D, Lafoux P, Martinet P (2020) Design of an accurate and stiff wooden industrial robot: first steps toward robot eco-sustainable mechanical design. J Mech Robot. https://doi.org/10.1115/1.4047726
Kato I, Ohteru S, Kobayashi H, Shirai K, Uchiyama A (1974) Information-power machine with senses and limbs. Springer, Vienna, pp 11–24. https://doi.org/10.1007/978-3-7091-2993-7_2
Hirose M, Ogawa K (2007) Honda humanoid robots development. Philos Trans R Soc A Math Phys Eng Sci 365(1850):11–19. https://doi.org/10.1098/rsta.2006.1917
Hirose R, Takenaka T (2001) Development of the humanoid robot ASIMO. Honda R&D Tech Rev 13:1–6. https://doi.org/10.7210/jrsj.22.1004
Chestnutt J, Lau M, Cheung G, Kuffner J, Hodgins J, Kanade T, IEEE (2005) Footstep planning for the Honda ASIMO humanoid. In: 2005 IEEE international conference on robotics and automation, IEEE, pp 629–634
Nishiwaki K, Kuffner J, Kagami S, Inaba M, Inoue H (2007) The experimental humanoid robot H7: a research platform for autonomous behaviour. Philos Trans R Soc Math Phys Eng Sci 365(1850):79–107. https://doi.org/10.1098/rsta.2006.1921
Feng SY, Xinjilefu X, Atkeson CG, Kim J,Ieee (2015) Optimization based controller design and implementation for the atlas robot in the DARPA robotics challenge finals. In: 2015 IEEE-Ras 15th international conference on humanoid robots, IEEE, pp 1028–1035
Kuindersma S, Deits R, Fallon M, Valenzuela A, Dai HK, Permenter F, Koolen T, Marion P, Tedrake R (2016) Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot. Auton Robot 40(3):429–455. https://doi.org/10.1007/s10514-015-9479-3
Lens T, von Stryk O, IEEE (2013) Design and dynamics model of a lightweight series elastic tendon-driven robot arm. In: 2013 IEEE international conference on robotics and automation, IEEE, pp 4512–4518
Hagenah H, Bohma W, Breitsprecher T, Merklein M, Wartzack S (2013) Modelling, construction and manufacture of a lightweight robot arm. In: Teti R (ed) Eighth cirp conference on intelligent computation in manufacturing engineering. Elsevier, Amsterdam, pp 211–216. https://doi.org/10.1016/j.procir.2013.09.037
Sigmund O, Maute K (2013) Topology optimization approaches a comparative review. Struct Multidiscip Optim 48(6):1031–1055. https://doi.org/10.1007/s00158-013-0978-6
Deaton JD, Grandhi RV (2014) A survey of structural and multidisciplinary continuum topology optimization: post 2000. Struct Multidiscip Optim 49(1):1–38. https://doi.org/10.1007/s00158-013-0956-z
Albers A, Brudniok S, Ottnad J, Sauter C, Sedchaicharn K,Ieee (2006) Upper body of a new humanoid robot—the design of ARMAR III. In: 2006 6th IEEE-Ras international conference on humanoid robots, Vols/ 1 and 2, IEEE. https://doi.org/10.1109/ichr.2006.321289
Albers A, Ottnad J, Weiler H, Haeussler P, IEEE (2007) Methods for lightweight design of mechanical components in humanoid robots. In: Humanoids: 2007 7th IEEE-Ras international conference on humanoid robots, IEEE. https://doi.org/10.1109/ichr.2007.4813934
Lohmeier S, Buschmann T, Schwienbacher M, Ulbrich H, Pfeiffer F, IEEE (2006) Leg design for a humanoid walking robot. In 2006 6th IEEE-Ras international conference on humanoid robots, vols. 1 and 2, IEEE. https://doi.org/10.1109/ichr.2006.321325
Buschmann T, Lohmeier S, Ulbrich H (2009) Humanoid robot Lola: design and walking control. J Physiol Paris 103(3–5):141–148. https://doi.org/10.1016/j.jphysparis.2009.07.008
Kwon W, Kim HK, Park JK, Roh CH, Lee J, Park J, Kim WK, Roh K, IEEE (2007) Biped Humanoid Robot Mahru III. In: Humanoids: 2007 7th IEEE-Ras international conference on humanoid robots, IEEE, pp 583–588
Kim BJ, Yun DK, Lee SH, Jang GW (2016) Topology optimization of industrial robots for system-level stiffness maximization by using part-level metamodels. Struct Multidiscip Optim 54(4):1061–1071. https://doi.org/10.1007/s00158-016-1446-x
Guo X, Zhang WS, Zhong WL (2014) Doing topology optimization explicitly and geometrically—a new moving morphable components based framework. J Appl Mech Trans ASME 81(8):12. https://doi.org/10.1115/1.4027609
Zhang WS, Zhang J, Guo X (2016) Lagrangian description based topology optimization-a revival of shape optimization. J Appl Mech Trans ASME 83(4):16. https://doi.org/10.1115/1.4032432
Zhang WS, Yuan J, Zhang J, Guo X (2016) A new topology optimization approach based on moving morphable components (MMC) and the ersatz material model. Struct Multidiscip Optim 53(6):1243–1260. https://doi.org/10.1007/s00158-015-1372-3
Marinov M, Amagliani M, Barback T, Flower J, Barley S, Furuta S, Charrot P, Henley I, Santhanam N, Finnigan GT, Meshkat S, Hallet J, Sapun M, Wolski P (2019) Generative design conversion to editable and watertight boundary representation. Comput Aided Des 115:194–205. https://doi.org/10.1016/j.cad.2019.05.016
Kallioras NA, Lagaros ND (2020) DzAIℕ: deep learning based generative design. Procedia Manuf 44:591–598. https://doi.org/10.1016/j.promfg.2020.02.251
Singh V, Gu N (2012) Towards an integrated generative design framework. Des Stud 33(2):185–207. https://doi.org/10.1016/j.destud.2011.06.001
Janssen P, Frazer J, Tang MX (2002) Evolutionary design systems and generative processes. Appl Intell 16(2):119–128. https://doi.org/10.1023/a:1013618703385
Runions A, Fuhrer M, Lane B, Federl P, Rolland-Lagan AG, Prusinkiewicz P (2005) Modeling and visualization of leaf venation patterns. ACM Transactions on Graphics 24(3):702–711. https://doi.org/10.1145/1073204.1073251
Kazi RH, Grossman T, Cheong H, Hashemi A, Fitzmaurice G, ACM (2017) DreamSketch: early stage 3D design explorations with sketching and generative design. Assoc Computing Machinery, New York. https://doi.org/10.1145/3126594.3126662
Khan S, Awan MJ (2018) A generative design technique for exploring shape variations. Adv Eng Inform 38:712–724. https://doi.org/10.1016/j.aei.2018.10.005
Leary M (2020) Generative design. Elsevier, AMsterdam, pp 203–222. https://doi.org/10.1016/b978-0-12-816721-2.00007-5
Plocher J, Panesar A (2019) Review on design and structural optimisation in additive manufacturing: towards next-generation lightweight structures. Mater Des 183:20. https://doi.org/10.1016/j.matdes.2019.108164
Krish S (2011) A practical generative design method. Comput Aided Des 43(1):88–100. https://doi.org/10.1016/j.cad.2010.09.009
Olson RS, Bartley N, Urbanowicz RJ, Moore JH, ACM (2016) Evaluation of a tree-based pipeline optimization tool for automating data science. Assoc Computing Machinery, New York. https://doi.org/10.1145/2908812.2908918
McKnight M (2017) Generative design what it is? How is it being used? Why it’s a game changer. KnE Eng 2:176. https://doi.org/10.18502/keg.v2i2.612
Zhang Y, Wang Z, Zhang Y, Gomes S, Bernard A (2020) Bio-inspired generative design for support structure generation and optimization in additive manufacturing (AM). CIRP Ann 69(1):117–120. https://doi.org/10.1016/j.cirp.2020.04.091
Wu J, Li M, Chen Z, Chen W, Wu X, Xi Y (2020) Generative design of the roller seat of the wind turbine blade turnover machine based on cloud computing. In: 2020 11th international conference on mechanical and aerospace engineering (ICMAE), 2020, pp 212–217
Frazer J (2002) Chapter 9—creative design and the generative evolutionary paradigm. In: Bentley PJ, Corne DW (eds) Creative evolutionary systems. Morgan Kaufmann, Burlington, pp 253–274. https://doi.org/10.1016/B978-155860673-9/50047-1
Kalyuzhnaya AV, Nikitin NO, Hvatov A, Maslyaev M, Yachmenkov M, Boukhanovsky A (2021) Towards generative design of computationally efficient mathematical models with evolutionary learning. Entropy 23(1):26. https://doi.org/10.3390/e23010028
Zhang J, Yuan Z, Yuan W, Dong S (2020) Lightweight design and modal analysis of calf structure of hydraulic biped robot. In: 2020 10th institute of electrical and electronics engineers international conference on cyber technology in automation, control, and intelligent systems (CYBER), pp 146–151
Alfayad S, Tayba AM, Ouezdou FB, Namoun F (2016) Kinematic synthesis and modeling of a three degrees-of-freedom hybrid mechanism for shoulder and hip modules of humanoid robots. J Mech Robot 8(4):1. https://doi.org/10.1115/1.4033157
Ren H, Shang W, Li N, Wu X (2020) A fast parameterized gait planning method for a lower-limb exoskeleton robot. Int J Adv Rob Syst. https://doi.org/10.1177/1729881419893221
Ye DS, Sun SM, Chen J, Luo MZ, IEEE (2014) The lightweight design of the humanoid robot frameworks based on evolutionary structural optimization. IEEE, New York
Acknowledgements
The authors would like to thank the reviewers for the careful reading and constructive feedback on the material presented in this article.
Funding
The research is supported by the Key R&D Program of Zhejiang Province, China (Grant No. 2021C01067), and Ten thousand people plan project of Zhejiang Province.
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Communicated by Rogério Sales Gonçalves.
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Sun, S., Ge, H., Gu, D. et al. Generative design of a calf structure for a humanoid robot based on gait simulation. J Braz. Soc. Mech. Sci. Eng. 45, 405 (2023). https://doi.org/10.1007/s40430-023-04322-7
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DOI: https://doi.org/10.1007/s40430-023-04322-7