Abstract
This work presents a planning model control for a 6-axis robot manipulator simulation assembling task. This work’s purpose is to plan trajectories for locking cable harnesses in palettes using nylon ties. This work is motivated by two biologically inspired approaches. The general \(\tau \)-\(\mathcal {J}\)erk theory for trajectory tracking and a recurrent bi-layer Hopfield artificial neural networks (HANN) for visual feedback of multiple palette’s elements. Equidistant Cartesian points describing free-collision paths between the robot and target positions are generated. Nonlinear regression-based 3th grade polynomials are obtained by multidimensional least squares as assembling trajectories. The Cartesian paths between robot and target position are chosen based on optimization with derivatives, where the path’s height is a criteria to minimize a route. This work validated the proposed method through computer simulations, which showed feasibility and effectiveness for assembling tasks.
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References
Alican D, Derya B (2021) Machine learning and data mining in manufacturing. Expert Syst Appl 166
Gameros A, Lowth S, Axinte D, Nagy-Sochacki A, Craig O, Siller HR (2017) State-of-the-art in fixture systems for the manufacture and assembly of rigid components: a review. Int J Mach Tools Manuf 123:1–21
Fang Y, Qi J, Hu J, Wang W, Peng Y (2020) An approach for jerk-continuous trajectory generation of robotic manipulators with kinematical constraints. Mech Mach Theory 153
Frisoli A, Loconsole C, Bartalucci R, Bergamasco M (2013) A new bounded jerk on-line trajectory planning for mimicking human movements in robot-aided neurorehabilitation. Rob Auton Syst 61(4):404–415
Song J, Chen Q, Li Z (2021) A peg-in-hole robot assembly system based on Gauss mixture model. Rob Comput Integr Manuf 67
Zhang M, Tian G, Zhang Y, Duan P (2021) Service skill improvement for home robots: autonomous generation of action sequence based on reinforcement learning. Knowl Based Syst 212
Gopinath V, Johansen K, Derelov M, Gustafsson A, Axelsson S (2021) Safe collaborative assembly on a continuously moving line with large industrial robots. Rob Comput Integr Manuf 67
Hsieh S (2003) Re-configurable dual-robot assembly system design, development and future directions. Ind Rob. ISSN 0143-991x (Emerald)
Pradhan S, Rajarajan K, Shetty AS (2018) Prototype, emulation, implementation and evaluation of SCARA robot in industrial environment. Procedia Comput Sci 133:331–337
Borangiu T, Ivanescu NA, Barad S (2003) Robotized flange assembling with line scan camera control. IFAC Proc Vol 36(23):119–124
Jiang J, Huang Z, Bi Z, Ma X, Yu G (2020) State-of-the-art control strategies for robotic PiH assembly. Rob Comput Integr Manuf 65
Lee DN (1976) A theory of visual control of braking based on information about time-to-collision. Perception 5(4):437–459
Lin H (2020) Design of an intelligent robotic precise assembly system for rapid teaching and admittance control. Rob Comput Integr Manuf 64
Tsuji T, Jazidie A, Kaneko M (1997) Distributed trajectory generation for cooperative multi-arm robots via virtual force interactions. IEEE Trans Syst Man Cybern Part B (Cybern) 27(5):862–867. https://doi.org/10.1109/3477.623238
Salari E, Zhang S (2003) Integrated recurrent neural network for image resolution enhancement from multiple image frames. IEEE Proc Vis Image Signal Process 150(5):299. https://doi.org/10.1049/ip-vis:20030524
Collins G (2000) Sophisticated image processing controls assembly robot. Ind Rob. ISSN 0143-991x
Wojciechowski J, Suszynski M (2017) Optical scanner assisted robotic assembly. Assembly Autom. ISSN 0144-5154
Semeniuta O, Dransfeld S, Martinsen K, Falkman P (2018) Towards increased intelligence and automatic improvement in industrial vision systems. Procedia CIRP 67:256–261. https://doi.org/10.1016/j.procir.2017.12.209
Jin L, Li S, Yu J, He J (2018) Robot manipulator control using neural networks: a survey. Neurocomputing 285:23–34
Urban JP, Buessler JL, Kihl H (1996) Parallel neural processing for the visual servoing of a robot arm. In: 1996 IEEE International conference on systems, man and cybernetics, vol 3. Information Intelligence and Systems, Beijing, China, pp 1806–1811. https://doi.org/10.1109/ICSMC.1996.565382
Gasparetto A, Zanotto V (2008) A technique for time-jerk optimal planning of robot trajectories. Rob Comput Integr Manuf 24(3):415–426
Huang J, Hu P, Wu K, Zeng M (2018) Optimal time-jerk trajectory planning for industrial robots. Mech Mach Theory 121(3):530–544
Rout A, Dileep M, Mohanta GB, Deepak BBVL, Biswal BB (2018) Optimal time-jerk trajectory planning of 6 axis welding robot using TLBO method. Procedia Comput Sci 133:537–544
Huang L, Jiang R (2013) A new method of inverse kinematics solution for industrial 7DOF robot. In: Proceedings of the 32nd Chinese control conference, Xi’an, pp 6063–6065
Zhang Z, Yang X (2019) Bio-inspired motion planning for reaching movement of a manipulator based on intrinsic tau jerk guidance. Adv Manuf 7:315–325. https://doi.org/10.1007/s40436-019-00268-z
Incremona GP, De Felici G, Ferrara A, Bassi E (2015) A supervisory sliding mode control approach for cooperative robotic system of systems. IEEE Syst J 9(1):263–272. https://doi.org/10.1109/JSYST.2013.2286509
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Networks 61:85–117
Naik PR, Samantaray J, Roy BK, Pattanayak SK (2015) 2-DOF robot manipulator control using fuzzy PD control with SimMechanics and sliding mode control: a comparative study. In: 2015 International conference on energy, power and environment: towards sustainable growth (ICEPE), Shillong, pp 1–6. https://doi.org/10.1109/EPETSG.2015.7510101
Wang R, Wu A, Chen X, Wang J (2020) A point and distance constraint based 6R robot calibration method through machine vision. Rob Comput Integr Manuf 65(10)
Young SS, Scott PD, Nasrabadi NM (2020) Object recognition using multilayer Hopfield neural network. Ind Rob
Wei Yao Z, Huang Q, Ji Z, Li X, Bi Q (2021) Deep learning-based prediction of piled-up status and payload distribution of bulk material. Autom Constr 121
Lee DN (2009) General tau theory: evolution to date. Perception 38(6):837
Song R, Li F, Quan W, Yang X, Zhao J (2021) Skill learning for robotic assembly based on visual perspectives and force sensing. Rob Auton Syst 135
Kofman J, Wu X, Luu TJ, Verma S (2005) Teleoperation of a robot manipulator using a vision-based human-robot interface. IEEE Trans Ind Electron 52(5):1206–1219. https://doi.org/10.1109/TIE.2005.855696
Yamamoto Y, Maekawa N, Hida M, Yang X, Aoyama K, Kataoka T, He Y, Tatsuno K (2012) Task performance tests on inserting the bolts by an experimental system for power distribution line maintenance—grope action under compliance control. In: 2012 Proceedings of international symposium
Luo RC, Kuo C (2016) Intelligent seven-DoF robot with dynamic obstacle avoidance and 3-D object recognition for industrial cyber-physical systems in manufacturing automation. Proc IEEE 104(5):1102–1113. https://doi.org/10.1109/JPROC.2015.2508598
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Carvajal, I., Martínez-García, E.A., Torres-Córdoba, R., Carrillo-Saucedo, V.M. (2021). Bioinspired Robotic Arm Planning by \(\tau \)-Jerk Theory and Recurrent Multilayered ANN. In: Koubaa, A., Azar, A.T. (eds) Deep Learning for Unmanned Systems. Studies in Computational Intelligence, vol 984. Springer, Cham. https://doi.org/10.1007/978-3-030-77939-9_10
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