Structural Optimization of an Unmanned Ground Vehicle as Part of a Robotic Grazing System Design

被引:0
作者
Korunovic, Nikola [1 ]
Banic, Milan [1 ]
Pavlovic, Vukasin [1 ]
Nestorovic, Tamara [2 ]
机构
[1] Univ Nis, Fac Mech Engn, Nish 18000, Serbia
[2] Ruhr Univ Bochum, Inst Computat Engn, Fac Civil & Environm Engn, Mech Adapt Syst, Univ Str 150, D-44801 Bochum, Germany
关键词
topology optimization; structural optimization; substructuring; unmanned ground vehicle (UGV); agriculture robot; finite element analysis (FEA); TOPOLOGY OPTIMIZATION;
D O I
10.3390/machines12050323
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Unmanned ground vehicles (UGVs) have gained increased attention in different fields of application; therefore, their optimization requires special attention. Lowering the mass of a UGV is especially important to increase its autonomy, agility, and payload capacity and to reduce dynamic forces. This contribution deals with optimizing a UGV unit prototype that, when connected with similar units, forms a moving electric fence for animal grazing. Together, these units form a robotic system that is intended to solve the critical problem of lack of human capacity in herding and grazing. This approach employs topology optimization (TO) and finite element analysis (FEA) to lower the mass of a UGV unit and validate the design of its structural components. To our knowledge, no optimization of this type of UGV has been reported in the literature. Here, we present the results of a case study in which a set of four load cases served as a basis for the optimization of the UGV frame. Response surface analysis (RSA) was used to identify the worst load cases, while substructuring was used to allow for more detailed meshing of the frame portion that was subjected to TO. Thereby, we demonstrate that the prototype of the UGV unit can be built using standard parts and that TO and FEA can be efficiently used to optimize the load-carrying structure of such a specific vehicle.
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页数:25
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共 29 条
  • [1] Ansys I., 2023, Reference Guide, Release 2023R1
  • [2] Bani M., 2022, Facta Univ. Ser. Autom. Control Robot, V1, P187, DOI [10.22190/FUACR221121015B, DOI 10.22190/FUACR221121015B]
  • [3] Christensen PW, 2009, SOLID MECH APPL, V153, P1
  • [4] Cook RD, 2002, Concepts and applications of finite element analysis
  • [5] A survey of structural and multidisciplinary continuum topology optimization: post 2000
    Deaton, Joshua D.
    Grandhi, Ramana V.
    [J]. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2014, 49 (01) : 1 - 38
  • [6] Demir N., 2021, Int. J. 3d Print. Technol. Digit. Ind, V5, P210, DOI [10.46519/ij3dptdi.949781, DOI 10.46519/IJ3DPTDI.949781]
  • [7] Gadekar A., 2023, Cogn. Robot., V3, P23
  • [8] Gangurde Y., 2023, Predictive Analytics in Smart Agriculture, P275
  • [9] Topology and Response Surface Optimization of a Bicycle Crank Arm with Multiple Load Cases
    Ismail, Ahmad Yusuf
    Na, Gangta
    Koo, Bonyong
    [J]. APPLIED SCIENCES-BASEL, 2020, 10 (06):
  • [10] Topology optimization for additive manufacturing using a component of a humanoid robot
    Junk, Stefan
    Klerch, Benjamin
    Nasdala, Lutz
    Hochberg, Ulrich
    [J]. 28TH CIRP DESIGN CONFERENCE 2018, 2018, 70 : 102 - 107