Cutting of sheep carcass using 3D point cloud with dual-robot system

Xiulan Bao, Junsong Leng, Jincheng Mao, Biyu Chen

Abstract


The precise and automatic cutting of sheep carcasses can improve the quality of mutton. Robots are widely used in meat processing because of their good repeatability and high precision. Two essential problems encountered in robot working of sheep carcass processing are robot calibration and cutting trajectory planning. A method of cutting sheep carcasses based on 3D point clouds with a dual-robot system was proposed in this study. The dual-robot system consists of a 3D scanning system, a fixing device for sheep carcasses, and a cutting robot. The calibration of the dual-robot system was completed by solving the matrix problem AXB=YCZ using the iterative method and the closed-form method. The 3D model of a sheep carcass was constructed using a 3D scanner. The cutting scheme of the cutting robot was planned based on the processed 3D point clouds. To show the feasibility of the proposed sheep carcass processing scheme, practical experiments were carried out. The results of the experiments show that the cutting robot can accurately perform the cutting actions according to the planned cutting scheme. The system proposed in this study can improve the efficiency and precision of sheep carcass cutting.
Keywords: sheep carcass, trajectory planning, point cloud, dual-robot system, 3D
DOI: 10.25165/j.ijabe.20221505.7161

Citation: Bao X L, Leng J S, Mao J C, Chen B Y. Cutting of sheep carcass using 3D point cloud with dual-robot system. Int J Agric & Biol Eng, 2022; 15(5): 163–171.

Keywords


sheep carcass, trajectory planning, point cloud, dual-robot system, 3D

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References


Williams H A, Jones M H, Nejati M, Seabright M J, Bell J, Penhall N D, et al. Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms. Biosystems Engineering, 2019; 181: 140–156.

Zhao D A, Lyu J, Ji W, Zhang Y, Chen Y. Design and control of an apple harvesting robot. Biosystems Engineering, 2011; 110(2): 112–122.

Du Z, Liang Y, Yan Z, Sun L, Chen W. Human-robot interaction control of a haptic master manipulator used in laparoscopic minimally invasive surgical robot system. Mechanism and Machine Theory, 2021; 156: 104132. doi: 10.1016/j.mechmachtheory.2020.104132.

Benedict J, Briggs H C. Application of robots in middle school math classes. In: Session: Advances in Aerospace Education I, AIAA, 2019; 2019-0070. doi: 10.2514/6.2019-0070.

Möller C, Schmidt H C, Koch P, Böhlmann C, Kothe S M, Wollnack J, et al. Machining of large scaled CFRP-Parts with mobile CNC-based robotic system in aerospace industry. Procedia manufacturing, 2017; 14: 17–29.

Wang G, Li W L, Jiang C, Zhu D H, Li Z W, Xu W, et al. Trajectory planning and optimization for robotic machining based on measured point cloud. IEEE Transactions on Robotics, 2021; 38(3): 1621–1637.

Misimi E, Øye E R, Eilertsen A, Mathiassen J R, Åsebø O B, Gjerstad T, et al. GRIBBOT-Robotic 3D vision-guided harvesting of chicken fillets. Computers and Electronics in Agriculture, 2016; 121: 84–100.

Hinrichsen L. Manufacturing technology in the Danish pig slaughter industry. Meat Science, 2010; 84(2): 271–275.

Templer R, Osborn A, Nanu A, Blenkinsopp K, Friedrich W. Innovative robotic applications for beef processing. In: Proceedings of Australasian Conference on Robotics and Automation, 2002, Auckland: ARAA, 2002; pp.43–47.

Singh J, Potgieter J, Xu W L. Ovine automation: robotic brisket cutting. Industrial Robot: An International Journal, 2012; 39(2): 191–196.

Park D I, Kim H, Park C, Kim D. Design and analysis of the dual arm manipulator for rescue robot. In: 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Munich: IEEE, 2017; pp.608–612. doi: 10.1109/AIM.2017.8014084.

Lippiello V, Fontanelli G A, Ruggiero F. Image-based visual-impedance control of a dual-arm aerial manipulator. IEEE Robotics and Automation, 2018; 3(3): 1856–1863.

Lehman A C, Berg K A, Dumpert J, Wood N A, Visty A Q, Rentschler M E, et al. Surgery with cooperative robots. Computer Aided Surgery, 2008; 13(2): 95–105.

Wang J, Ren H, Meng M Q-H. A preliminary study on surgical instrument tracking based on multiple modules of monocular pose estimation. In: The 4th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent, Hong Kong: IEEE, 2014; pp.146–151. doi: 10.1109/CYBER.2014.6917451.

Wu Q, Li M, Qi X, Hu Y, Li B, & Zhang J. Coordinated control of a dual-arm robot for surgical instrument sorting tasks. Robotics and Autonomous Systems, 2018; 112(2019): 1–12.

Bai H, Wen J T. Cooperative load transport: A formation-control perspective. IEEE Transactions on Robotics, 2010; 26(4): 742–750.

Ling X, Zhao Y, Gong L, Liu C, Wang T. Dual-arm cooperation and implementing for robotic harvesting tomato using binocular vision. Robotics and Autonomous Systems, 2019; 114: 134–143.

Qiao Y, Chen Y, Chen B, Xie J. A novel calibration method for multi-robots system utilizing calibration model without nominal kinematic parameters. Precision Engineering, 2017; 50: 211–221.

Zhao D, Bi Y, Ke Y. Kinematic modeling and base frame calibration of a dual-machine-based drilling and riveting system for aircraft panel assembly. The International Journal of Advanced Manufacturing Technology, 2017; 94(5–8): 1873–1884.

Zhu Q, Xie X, Li C, Xia G, Liu Q. Kinematic self-calibration method for dual-manipulators based on optical axis constraint. IEEE Access, 2019; 7: 7768–7782.

Zhu Q, Xie X, Li C. Dual manipulator system calibration based on virtual constraints. Bulletin of the Polish Academy of Sciences-Technical Sciences, 2019; 67(6): 1149–1158.

Wang J, Wang W, Wu C, Chen S L, Fu J, Lu G. A plane projection-based method for base frame calibration of cooperative manipulators. IEEE Transactions on Industrial Informatics, 2018; 15(3): 1688–1697.

Yan S J, Ong S K, Nee A Y C. Registration of a hybrid robot using the Degradation-Kronecker method and a purely nonlinear method. Robotica, 2015; 34(12): 2729–2740.

Wu L, Wang J, Qi L, Wu K, Ren H, Meng M.Q.-H. Simultaneous hand-eye, tool-flange, and robot-robot calibration for comanipulation by solving the AXB=YCZ problem. IEEE Transactions on Robotics, 2016; 32(2): 413–428.

Wang J, Wu L, Meng M.Q.-H., Ren H. Towards simultaneous coordinate calibrations for cooperative multiple robots. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago: IEEE, 2014; pp.410–415. doi: 10.1109/IROS.2014.6942592.

Ma Q, Goh Z, Ruan S, Chirikjian G S. Probabilistic approaches to the AXB=YCZ calibration problem in multi-robot systems. Autonomous Robots, 2018; 42(7): 1497–1520.

Wang G, Li W, Jiang C, Zhu D, Xie H, Liu X, et al. Simultaneous calibration of multicoordinates for a dual-robot system by solving the AXB=YCZ problem. IEEE Transactions on Robotics, 2021; 37(4): 1172–1185.

Mu S, Qin H B, Wei J, Wen Q K, Liu S H, Wang S C, et al. Robotic 3D vision-guided system for half-sheep cutting robot. Mathematical Problems in Engineering, 2020; 2020(Pt.35): 1520686. doi: 10.1155/2020/1520686.

Bondø M S, Mathiassen J R, Vebenstad P A, Misimi E, Bar E M S, Toldnes B, et al. An automated salmonid slaughter line using machine vision. Industrial Robot: An International Journal, 2011; 38(4): 399–405.

Guire G, Sabourin L, Gogu G, Lemoine E. Robotic cell for beef carcass primal cutting and pork ham boning in meat industry. Industrial Robot: An International Journal, 2010; 37(6): 532–541.

Cheng D, Wong C K, Lim P P K. Vision system for the automation of ovine carcass processing. In: Proceedings of Australasian Conference on Robotics and Automation, Melbourne: The University of Melbourne, 2014; Paper No. 110.

Liu Y, Cong M, Zheng H, Liu D. Porcine automation: Robotic abdomen cutting trajectory planning using machine vision techniques based on global optimization algorithm. Computers and Electronics in Agriculture, 2017; 143: 193–200.

Cong M, Wang Y H, Du Y, Liu D. Porcine abdomen cutting method using robot based on point cloud clustering and PCA. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2020; 11: 54–59. (in Chinese)

Cong M, Zhang J, Du Y, Wang Y, Yu X, Liu D. A porcine abdomen cutting robot system using binocular vision techniques based on kernel principal component analysis. Journal of Intelligent & Robotic Systems, 2021; 101(4): 1–10.

NY/T 1564-2007. Cutting technical specification of mutton, 2007. (in Chinese)




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