IJSDIP | International Journal of System Design & Info Processing

Robotic Arm Design Using Reinforcement Learning for Precision Assembly: Enhancing automation in manufacturing with AI-driven kinematics

Paper ID:

IJSDIP_2026_VOLUME 14_ISSUE 2_1-22-27

Author's Name:

Dr. Thota Balaji, Dr. Navaneethakannan Nandakumar, Dala Rahulji, Mrs M Pratibha

Volume:

Issue:

Issue 2

Year:

2026

Pages:

22-27

Abstract

Precision assembly in the current manufacturing systems has been greatly improved with the introduction of the Reinforcement Learning (RL) in the design of robotic arms. In this paper, an AI-based kinematic framework is introduced, which allows robotic arms to learn the best motion strategies through the continuous interaction with dynamic environments. In contrast to the traditional rule-based or pre-programmed robots, the proposed RL-based framework is flexible to changes in uncertainties like object positioning, assembly tolerances, and environmental disturbances. The algorithm uses a reward mechanism of learning in which the robotic arm continuously re-optimizes its trajectory planning, joint coordination, and gripping accuracy. It has been proven experimentally that there is a significant increase in positioning accuracy, task turnaround time and adaptability under experimental conditions as compared to traditional automation methods. Nonetheless, real-world constraints like excessive computational expense, training duration, and reliance on simulation platforms are recognized. The practical implementation issues such as sensor noise and hardware limitations are also covered. The future directions of research are hybrid models of learning, real-time optimization and transfer learning methods to address the gap between simulation and physical implementation. All in all, this paper has outlined the prospects of RL-controlled robotic systems to transform the world of precision manufacturing with intelligent and adaptive automation.

Keywords

Reinforcement Learning, Robotic Arm, Precision Assembly, AI-based Kinematics, Automation, Smart Manufacturing, Adaptive Control.

References

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DOI: https://doi.org/10.64971/j.cph.ijsdip.v14.i2.4.2026