Abstract:In layup of woven composite on double curvature surface, the manual layup is of low efficiency and poor quality uniformity, while research findings on robotic layup are not accurate enough to describe manual layup skills. Therefore, a method of acquisition, representation and recurrence of woven composite layup skills is proposed based on imitation learning. Firstly, the dragging demonstration method is used to obtain trajectory information of woven layup, and supervised trajectory segmentation is carried out based on pressure threshold. After that, probabilistic models of layup trajectory are learned by Gaussian mixture model (GMM) and Gaussian mixture regression (GMR). On this basis, the normal orientation of the endeffector is calculated taking demonstration orientation and surface norm as reference. Then, another demonstration is carried out to obtain normal pressure information, and probabilistic models of layup pressure are learned though GMM/GMR. For the establishment process of contact status, a pre-control quantity exit mechanism is proposed within the framework of hybrid force/motion control to accomplish non-impact switching of the controllers during the establishment of contact status. Finally, an experiment platform is built based on UR5e robot, ATI six-dimensional force/torque sensor and ROS (robot operating system). The result shows that the proposed skills acquisition and description method can realize accurate extraction and expression of manual layup experience, and the maximum pressure error during contact status establishment is 1.8 N when using the proposed pre-control quantity exit mechanism, which is less than 2.0 N, the root mean square error of layup pressure of all 16 trajectories on the curvature surface mould. The finished layer has no visible defects such as bulge and folding. At about 97.3% of points in the woven point cloud measured by depth camera, their distances from the mould point cloud are less than 1.0 mm.
[1] 官威,李文晓,戴瑛,等.纺织复合材料预制体变形研究综述[J].航空制造技术, 2021, 64(Z1):22-37.Guan W, Li W X, Dai Y, et al.A review of study on deformation of textile composite preforms[J].Aeronautical Manufacturing Technology, 2021, 64(Z1):22-37. [2] Elkington M, Bloom L D, Ward C, et al.On prepreg properties and manufacturability[C]//19th International Conference on Composite Materials.Montreal, Canada:Concordia Centre for Composites, 2013:4397-4409. [3] Ward C, Hazra K, Potter K.Development of the manufacture of complex composite panels[J].International Journal of Materials and Product Technology, 2011, 42(3-4):131-155. [4] Such M, Ward C, Hutabarat W, et al.Intelligent composite layup by the application of low cost tracking and projection technologies[J].Procedia CIRP, 2014, 25:122-131. [5] Elkington M P, Bloom D, Ward C, et at.Understanding the lamination process[C]//19th International Conference on Composite Materials.Montreal, Canada:Concordia Centre for Composites, 2013:4385-4396. [6] Prabhu V A, Elkington M, Crowley D, et al.Digitisation of manual composite layup task knowledge using gaming technology[J].Composites, Part B:Engineering, 2017, 112:314-326. [7] Crowley D, Elkington M, Ward C, et al.Hand lay-up of complex geometries-Prediction, capture and feedback[C]//International SAMPE Technical Conference.Diamond Bar, USA:Society for the Advancement of Material and Process Engineering, 2016. [8] Malhan R K, Kabir A M, Shembekar A V, et al.Hybrid cells for multi-layer prepreg composite sheet layup[C]//IEEE 14th International Conference on Automation Science and Engineering.Piscataway, USA:IEEE, 2018:1466-1472. [9] Malhan R K, Shembekar A V, Kabir A M, et al.Automated planning for robotic layup of composite prepreg[J].Robotics and Computer-Integrated Manufacturing, 2021, 67.DOI:10.1016/j.rcim.2020.102020. [10] 黄艳龙,徐德,谭民.机器人运动轨迹的模仿学习综述[J].自动化学报, 2022, 48(2):315-334.Huang Y L, Xu D, Tan M.On imitation learning of robot movement trajectories:A survey[J].Acta Automatica Sinica, 2022, 48(2):315-334. [11] Gao X, Ling J, Xiao X H, et al.Learning force-relevant skills from human demonstration[J].Complexity, 2019.DOI:10.1155/2019/5262859. [12] Wang Y, Beltran-Hernandez C C, Wan W W, et al.Hybrid trajectory and force learning of complex assembly tasks:A combined learning framework[J].IEEE Access, 2021, 9:60175-60186. [13] Wang N, Chen C Z, di Nuovo A.A framework of hybrid force/motion skills learning for robots[J].IEEE Transactions on Cognitive and Developmental Systems, 2021, 13(1):162-170. [14] 周欣宇.一种基于轨迹模仿学习和任务规划的机械臂演示学习方法研究[D].哈尔滨:哈尔滨工业大学, 2019.Zhou X Y.A research on robotic-arm learning method by demonstration based on trajectory imitation learning and task planning[D].Harbin:Harbin Institute of Technology, 2019. [15] 李琳,肖佳栋,张铁,等.基于自适应迭代的机器人曲面恒力跟踪[J].北京航空航天大学学报, 2019, 45(4):641-649.Li L, Xiao J D, Zhang T, et al.Constant-force curved-surfacetracking with robotic manipulator based on adaptive iterative algorithm[J].Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(4):641-649. [16] 高培阳.基于力传感器的工业机器人恒力磨抛系统研究[D].武汉:华中科技大学, 2019.Gao P Y.Research on force control grinding system of industrial robot based on force sensor[D].Wuhan:Huazhong University of Science and Technology, 2019. [17] 连学军.面向大型风电叶片的机器人阻抗控制顺应打磨研究[D].武汉:华中科技大学, 2017.Lian X J.The research of robot adaptable grinding large wind blade by impedance control[D].Wuhan:Huazhong University of Science and Technology, 2017. [18] Yamane K.Admittance control with unknown location of interaction[J].IEEE Robotics and Automation Letters, 2021, 6(2):4079-4086. [19] 迟明善,姚玉峰,刘亚欣.模仿学习示教轨迹自动分割方法的研究进展[J].控制与决策, 2019, 34(7):1345-1354.Chi M S, Yao Y F, Liu Y X.Recent advances on automatic segmentation method of teaching trajectory for imitation learning[J].Control and Decision, 2019, 34(7):1345-1354. [20] 李航.统计学习方法[M].北京:清华大学出版社, 2012:162-166.Li H.Statistical learning method[M].Beijing:Tsinghua University Press, 2012:162-166. [21] Calinon S, Guenter F, Billard A.On learning, representing, and generalizing a task in a humanoid robot[J].IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, 2007, 32(2):286-298. [22] 熊有伦,李文龙,陈文斌,等.机器人学:建模、控制与视觉[M].武汉:华中科技大学出版社, 2018:267-269.Xiong Y L, Chen W L, Chen W B, et al.Robotics:Modeling, control and vision[M].Wuhan:Huazhong University of Science and Technology Press, 2018:267-269.