Design of a Modular Robotic Manipulator for Multi-Task Industrial Operations
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DOI:
https://doi.org/10.67228/30715725/IJIARE-V9I1P101Published 01-03-2026
Modular robotics, industrial manipulators, reconfigurable robots, multi-task automation, distributed control, Industry 4.0 Issue
Section
ArticlesAbstract
The speed of change in the modern manufacturing induced by Industry 4.0 is what has contributed to the significant growth of the flexible and adaptive yet cost-effective automation solutions demanding. The traditional industrial robotic manipulators are commonly programmed to the specific or narrow-focus tasks, which also leads to low reconfigurability, high systems costs, and low responsiveness to the changing production needs. By comparison, modular robotic manipulators present a hopeful solution to these problems, being capable of reconfiguring mechanical, electrical, and control elements to facilitate several tasks in heterogeneous industrial operations. This article reports the conceptual design and analysis of a modular robotic manipulator that is bound to be used in multi-task industrial work i.e. assembly, material handling, inspection and machine tending. The suggested structure is focused on a modular architecture that is standardized with interchangeable joint, link, actuation, and end-effector modules. The mechanical design principles, which guarantee structural rigidity, scalability of payloads, and repetition are described in detail. A distributed control architecture, which relies on a kinematics modularity concept and distributed computing is developed to facilitate a quick turnover of tasks and isolation of faults. Kinematic and dynamic mathematical modeling are obtained to show that they can be compatible with typical robotic controllers algorithms without compromising modular independence. Moreover, the approach incorporates plug and play communication protocols and standardized interfaces to ease the system expansion and maintenance. Proof experimentation is done simulating performance to analyze performance and confirming the prototype performance in real industry loads. Performance indicators such as positioning error, time of reconfig, ratio of payload to weight and ability to accommodate the task is evaluated and compared to the traditional monolithic manipulators. The findings show that the modular design can be very competitive in terms of accuracy and stability as well as heavily enhance flexibility and decreases downtime when switching tasks. The authors of the study conclude that modular robotic manipulators are a promising and scalable way to encoder the next generation of smart manufacturing system with significant benefits in terms of flexibility, lifecycle cost, and resilience of the system.
References
[1] J. J. Craig, Introduction to Robotics: Mechanics and Control, 3rd ed. Upper Saddle River, NJ, USA: Pearson Education, 2005.
[2] R. P. Paul, Robot Manipulators: Mathematics, Programming, and Control. Cambridge, MA, USA: MIT Press, 1981.
[3] M. H. Ang, O. Khatib, and B. Siciliano, “Special issue on experimental robotics,” Int. J. Robot. Res., vol. 30, no. 7, pp. 837–840, 2011.
[4] B. Siciliano and O. Khatib, Springer Handbook of Robotics, 2nd ed. Cham, Switzerland: Springer, 2016.
[5] M. Yim, W.-M. Shen, B. Salemi, et al., “Modular self-reconfigurable robot systems: Challenges and opportunities,” IEEE Robot. Autom. Mag., vol. 14, no. 1, pp. 43–52, Mar. 2007.
[6] A. Kamimura, H. Kurokawa, E. Yoshida, et al., “Automatic locomotion pattern generation for modular robots,” IEEE Trans. Robot., vol. 21, no. 3, pp. 491–500, Jun. 2005.
[7] S. Murata and H. Kurokawa, “Self-reconfigurable robots,” IEEE Robot. Autom. Mag., vol. 14, no. 1, pp. 71–78, Mar. 2007.
[8] J. Kotlarski, J. Ortmaier, and M. Hirzinger, “Mechanically reconfigurable robots: A review,” IEEE Int. Conf. Robot. Autom. (ICRA), pp. 5430–5437, 2010.
[9] M. N. Glauser, D. Trivun, and R. Siegwart, “Design and evaluation of modular robotic manipulators for industrial applications,” IEEE/ASME Trans. Mechatronics, vol. 23, no. 6, pp. 2806–2817, Dec. 2018.
[10] H. Asada and J.-J. Slotine, Robot Analysis and Control. New York, NY, USA: Wiley, 1986.
[11] S. Haddadin, A. Albu-Schäffer, and G. Hirzinger, “Safety evaluation of physical human–robot interaction,” Int. J. Robot. Res., vol. 27, no. 2, pp. 233–249, 2008.
[12] L. Wang, J. Váncza, and S. Kemény, “Toward adaptive manufacturing: Modeling and control of reconfigurable robotic systems,” CIRP Annals, vol. 58, no. 1, pp. 415–420, 2009.
[13] P. Corke, Robotics, Vision and Control: Fundamental Algorithms in MATLAB, 2nd ed. Cham, Switzerland: Springer, 2017.
[14] R. M. Murray, Z. Li, and S. S. Sastry, A Mathematical Introduction to Robotic Manipulation. Boca Raton, FL, USA: CRC Press, 1994.
[15] A. Bicchi and G. Tonietti, “Fast and ‘soft-arm’ tactics,” IEEE Robot. Autom. Mag., vol. 11, no. 2, pp. 22–33, Jun. 2004.
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