Open AccessArticle

Development of a Learning System for Robot Modular Manipulators

Volume 11, Issue 5, Page No 1–7, 2026

King Mongkut’s University of Technology Thonburi, Institute of Field Robotics, Bangkok, 10140, Thailand
*whom correspondence should be addressed. E-mail: poramet.fuan@kmutt.ac.th

Adv. Sci. Technol. Eng. Syst. J. 11(5), 1–7 (2026); crossref symbol DOI: 10.25046/aj110501

Keywords: Modular robot, Serial manipulator, SCARA robot, Articulated robot, Simulation, Topology detection

Received: 3 September 2026, Revised: 7 October 2026, Accepted: 9 October 2026, Published Online: 26 October 2026
(This article belongs to Section Robotics (ROB))
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This paper presents a modular robot learning system for robotics education, comprising a reconfigurable physical manipulator and an automatically configuring 3D simulation framework. Unlike prior modular manipulator systems that require manual software re-parameterization after hardware reconfiguration, the proposed system detects the assembled topology in real time through Dynamixel motor ID sequencing and renders a synchronized digital twin without any user intervention. The hardware platform uses three interchangeable joint modules—Twisting, Rotational, and Linear—connected via N42 neodymium magnets and an RS485 daisy-chain bus, enabling tool-free reconfiguration between SCARA (RRP) and Articulated (RRR) configurations in less than two minutes. Connector structural adequacy was validated experimentally: the measured axial magnetic moment capacity and perpendicular moment capacity both exceed the maximum robot-induced connector moment, and joint torques remain within the Dynamixel MX-28R 2.5 Nm specification under payload conditions. Protocol topology detection achieved 100% accuracy across all six tested assembly configurations. The simulation rendered at 60 FPS across five robot topologies—Cartesian, Cylindrical, Spherical, SCARA, and Articulated—providing a single platform for comparative study of serial manipulator architectures.

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