A digital twin for 3D path planning of large-span curved-arm gantry robot

被引:45
作者
Wang Wenna [1 ,2 ]
Ding Weili [1 ,2 ,5 ]
Hua Changchun [1 ,2 ]
Zhang Heng [1 ,2 ]
Feng Haibing [3 ]
Yao Yao [4 ]
机构
[1] Yanshan Univ, Sch Elect Engn, Qinhuangdao 066004, Peoples R China
[2] Minist Educ, Engn Res Ctr Intelligent Control Syst & Intelligen, Qinhuangdao 066004, Peoples R China
[3] Yanshan Univ, Sch Mech Engn, Qinhuangdao 066004, Peoples R China
[4] Nantong Yuetong CNC Equipment Co Ltd, Nantong 226000, Peoples R China
[5] Yanshan Univ, Qinhuangdao, Peoples R China
基金
中国国家自然科学基金;
关键词
3D path planning; Trajectory simulation; Digital twin; Gantry robot; NavMesh; OPTIMIZATION; ALGORITHM; FIELD;
D O I
10.1016/j.rcim.2022.102330
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Gantry robots enjoy important applications in unmanned factories and intelligent manufacturing tasks. Aiming at the automated production of customized wooden furniture, this paper proposes a digital twin for threedimensional (3D) path planning of a large-span curved-arm gantry robot. First, a digital twin platform is built to show the door panel loading and unloading process of the gantry robot. Then the 3D path planning problem and method are proposed based on the built platform. The core of the algorithm consists of a bilateral control method, a 3D mesh construction strategy, and multi-objective 3D path planning using assembly line scheduling. The bilateral control method is used to achieve the perception and control of the gantry robot in the physical space. The mesh construction strategy entails static point set construction, obstacle avoidance analysis, point set construction for leap obstacles, and convex plane construction analysis. Multi-objective 3D path planning is primarily based on the NavMesh algorithm, which leverages an assembly line scheduling model to complete the wooden door processing task. Experimental results show that the proposed method can ensure safety, improved production efficiency, and satisfactory real-time performance in controlling the real gantry robot.
引用
收藏
页数:19
相关论文
共 34 条
[21]   Real Time Voronoi-like Path Planning Using Flow Field and A☆ [J].
Sabbagh, Mark ;
Tanveer, M. Hassan ;
Thomas, Antony ;
Faile, Jacob ;
Salman, Muhammad .
2020 IEEE 17TH INTERNATIONAL CONFERENCE ON SMART COMMUNITIES: IMPROVING QUALITY OF LIFE USING ICT, IOT AND AI (IEEEHONET 2020), 2020, :103-107
[22]   Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs [J].
Samaniego, Franklin ;
Sanchis, Javier ;
Garcia-Nieto, Sergio ;
Simarro, Raul .
ELECTRONICS, 2019, 8 (03)
[23]  
Shen Y., 2014, MODULAR MACHINE TOOL, V1, P5, DOI [10.13462/j.cnki.mmtamt.2014.01.002, DOI 10.13462/J.CNKI.MMTAMT.2014.01.002]
[24]  
Song A., 2020, COMPUTER ENG SCI, P1
[25]   Tracking Control for a Cushion Robot Based on Fuzzy Path Planning With Safe Angular Velocity [J].
Sun, Ping ;
Yu, Zhuang .
IEEE-CAA JOURNAL OF AUTOMATICA SINICA, 2017, 4 (04) :610-619
[26]  
Taha M., 2020, 2020 4 INT S MULT ST, P1, DOI [10.1109/ISMSIT50672.2020.9254715, DOI 10.1109/ISMSIT50672.2020.9254715]
[27]  
Wang C., 2019, JOUNAL GUIYANG UNIVE, V14, P7, DOI [10.16856/j.cnki.52-1142/n.2019.04.003, DOI 10.16856/J.CNKI.52-1142/N.2019.04.003]
[28]   Reconnaissance Mission Conducted by UAV Swarms Based on Distributed PSO Path Planning Algorithms [J].
Wang, Yubing ;
Bai, Peng ;
Liang, Xiaolong ;
Wang, Weijia ;
Zhang, Jiaqiang ;
Fu, Qixi .
IEEE ACCESS, 2019, 7 :105086-105099
[29]  
Weiss B, 2006, PROCEEDINGS OF THE 2006 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT CONTROL, P491
[30]   ACO-A*: Ant Colony Optimization Plus A* for 3-D Traveling in Environments With Dense Obstacles [J].
Yu, Xue ;
Chen, Wei-Neng ;
Gu, Tianlong ;
Yuan, Huaqiang ;
Zhang, Huaxiang ;
Zhang, Jun .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2019, 23 (04) :617-631