Algorithmic Workspace Programming of the Collaborative Multi-Robots

Year 2022, Volume: 5 Issue: 1, 325 - 341, 08.03.2022

Haydar Şahin

https://doi.org/10.47495/okufbed.1030575

Abstract

In the present study, the Controllable Instantaneous Screw Axes (C-ISA) 1 and C-ISA 2 shape variable angles are modified independently to realize various rule-based work spaces and trajectories for multi collaborative robot control. The toolbox developed previously is used to obtain the algorithm of the workspaces for 2-RR collaborative multi-robots herein. Six collaborative multi-robots are exploited to design the intersecting workspaces with generated trajectories. The classifications of the workspaces are unveiling the boundaries of the collaborations for the six multi-robots of the 2-RR (Revolute Revolute). The recent developments are showing that the multi-robots are embedding into the automation systems to achieve the novel requirements of the challenging technology. Therefore, the workspace algorithms developed herein are prepared to be utilized by these automation systems.

Keywords

Workspace, Collaborative robots, Modular mechanism

İşbirlikçi Çoklu Robotların Algoritmik Çalışma Alanı Programlaması

Abstract

Bu çalışmada, Kontrol Edilebilir Anlık Vida Eksenleri (C-ISA) 1 ve C-ISA 2 şekil değişken açıları, çoklu işbirlikçi robot kontrolü için çeşitli kural tabanlı çalışma alanları ve yörüngeleri gerçekleştirmek için bağımsız olarak değiştirilir. Daha önce geliştirilen araç kutusu, buradaki 2-RR işbirlikçi çoklu robotlar için çalışma alanlarının algoritmasını elde etmek için kullanılmaktadır. Oluşturulan yörüngelerle kesişen çalışma alanlarını tasarlamak için altı işbirlikçi çoklu robottan yararlanılır. Çalışma alanlarının sınıflandırmaları, 2-RR'nin (Revolute Revolute) altı çoklu robotu için işbirliklerinin sınırlarını ortaya koyuyor. Son gelişmeler, zorlu teknolojinin yeni gereksinimlerini karşılamak için çoklu robotların otomasyon sistemlerine yerleştirildiğini gösteriyor. Bu nedenle burada geliştirilen çalışma alanı algoritmaları bu otomasyon sistemlerinde kullanılmak üzere hazırlanmıştır.

Keywords

Çalışma alanı, İşbirlikçi robotlar, Modüler mekanizma

References

• Byner C., Matthias B., Ding H. Dynamic speed and separation monitoring for collaborative robot applications – Concepts and performance, Robotics and Computer Integrated Manufacturing 2019; 58: 239-252. https://doi.org/10.1016/j.rcim.2018.11.002
• Clark AB., Rojas N. Design and workspace characterisation of malleable robots, 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020;9021-9027, doi: 10.1109/ICRA40945.2020.9197439
• Dilibal, S., Sahin, H., Danquah JO., Faruk EMO., Choi JW. Additively manufactured custom soft gripper with embedded soft force sensors for an ındustrial robot. International Journal of Precision Engineering and Manufacturing 2021; 22 (4): 709–718. https://doi.org/10.1007/s12541-021-00479-0
• Elbanhawi M., Simic M. Sampling-based robot motion planning: a review. IEEE Access 2014; 2:56- 77. doi: 10.1109/ACCESS.2014.2302442
• Feng Z., Hu G., Sun Y., Soon J. An overview of collaborative robotic manipulation in multi-robot systems. Annual Reviews in Control 2020; 49: 113–127. https://doi.org/10.1016/j.arcontrol.2020.02.002 Henderson AMT., Prazenica RJ. Trajectory generation for a multibody robotic system using the product of exponentials formulation. American Institute of Aeronautics and Astronautics. 2021- 2016. 4 Jan 2021 Forum. https://doi.org/10.2514/6.2021-2016
• International Organization for Standardization, ISO/TS15066:2016 – Robots and Robotic Devices – Collaborative Robots, 2016.
• Krizmancic M., Arbanas B., Petrovic T., Petric F., Bogdan S. Cooperative aerial-ground multi-robot system for automated construction tasks. in IEEE Robotics and Automation Letters 2020; 5( 2): 798-805. doi: 10.1109/LRA.2020.2965855
• Lakshmanan AK., Mohan RE., Ramalingam B., Anh VL., Veerajagadeshwar P., Tiwarb K., Ilyasa M. Complete coverage path planning using reinforcement learning for Tetromino based cleaning and maintenance robot. Automation in Construction 2020; 112: 1-11. https://doi.org/10.1016/j.autcon.2020.103078
• Le A. V., Nhan NHK., Mohan RE. Evolutionary algorithm-based complete coverage path planning for tetriamond tiling robots. Sensors 2020; 20(2): 1-14. doi:10.3390/s20020445
• Martínez O., Campa R. Comparing methods using homogeneous transformation matrices for kinematics modeling of robot manipulators. In: Pucheta M., Cardona A., Preidikman S., Hecker R. (eds) Multibody Mechatronic Systems. MuSMe 2021. Mechanisms and Machine Science, 94. Springer, Cham, 2021. https://doi.org/10.1007/978-3-030-60372-4_13
• Marvel AJ., Norcross R. Implementing speed and separation monitoring in collaborative robot workcells. Robotics and Computer-Integrated Manufacturing 2017; 44: 144–155. http://dx.doi.org/10.1016/j.rcim.2016.08.001
• Moe S., Pettersen KY., Gravdahl JT. Set-based collision avoidance applications to robotic systems. Mechatronics 2020; 69: 1-19. https://doi.org/10.1016/j.mechatronics.2020.102399
• Olesen AS., Gergaly BB., Ryberg EA., Thomsen M.R., Chrysostomou D. A collaborative robot cell for random bin-picking based on deeplearning policies and a multi-gripper switching strategy. Procedia Manufacturing 2020; 51: 3–10. https://doi.org/10.1016/j.promfg.2020.10.002.
• Queralta J. P. et al. Collaborative multi-robot search and rescue: planning, coordination, perception, and active vision. in IEEE Access 2020; 8: 191617-191643. doi: 10.1109/ACCESS.2020.3030190
• Rajesh KM., Anandu R., Sakthiprasad KM. Comparison of planned path and travelled path using ros navigation stack. 2020 International Conference for Emerging Technology (INCET) 1-6, 2020.
• Sahin H. The modular nonoverlapping grasp workspaces and dynamics for the grippers using the micro and macro C-Manifold design. Journal of Scientific & Industrial Research 2021; 9: 766-776. http://nopr.niscair.res.in/handle/123456789/58142
• Su H., Liu S., Zheng, B., Zhou X., Zheng K.A survey of trajectory distance measures and performance evaluation. The International Journal on Very Large Data Bases 2020; 29: 3–32. https://doi.org/10.1007/s00778-019-00574-9.
• Thalamy P., Piranda B., Bourgeois J. A survey of autonomous self-reconfiguration methods for robot- based programmable matter. Robotics and Autonomous Systems 2019; 120: 1-17.
• Wang J., Meng MQH. Optimal path planning using generalized voronoi graph and multiple potential functions. IEEE transactions on industrial electronics 2020; 67(12):10621- 10630, DECEMBER. 10.1109/TIE.2019.2962425
• Wang X., Liu X., Chen L., Hu H. Deep-learning damped least squares method for inverse kinematics of redundant robots. Measurement 2021; 171: 108821, ISSN 0263-2241. https://doi.org/10.1016/j.measurement.2020.108821.
• Xiao F., Li G., Jiang D., Xie Y., Yun J., Liu Y., Huang L., Fang Z. An effective and unified method to derive the inverse kinematics formulas of general six-DOF manipulator with simple geometry. Mechanism and Machine Theory 2021; 159: 1-14. https://doi.org/10.1016/j.mechmachtheory.2021.104265.
• Yang S., Wen H., Hu Y., Jin D., Coordinated motion control of a dual-arm space robot for assembling modular parts. Acta Astronautica. Acta Astronautica 2020; 177: 627–638. https://doi.org/10.1016/j.actaastro.2020.08.006