Development and Experimental Validation of an Adaptive Low-Cost Robotic Manipulation System for Small and Medium Manufacturing Enterprises

Abstract

Small and Medium Enterprises (SMEs) frequently encounter challenges in adopting robotic automation because of high acquisition costs, complex programming requirements, and limited operational flexibility. This research presents the design, implementation, and experimental validation of a low-cost adaptive robotic manipulation system intended specifically for SME manufacturing environments. The proposed system integrates a 5-degree-of-freedom robotic arm, servo-based actuation, an ESP32 microcontroller, a vision-assisted object detection module, and adaptive motion planning to execute multiple material-handling tasks. The manipulator employs inverse kinematics and closed-loop feedback control to improve positioning accuracy while maintaining low implementation cost.

A prototype was developed using commercially available components and evaluated through pick-and-place, sorting, packaging, and repetitive handling experiments. Performance was measured using cycle time, positioning accuracy, repeatability, payload capability, power consumption, and operating cost. Experimental results indicate that the proposed manipulator achieves high positioning accuracy with satisfactory repeatability while reducing system cost significantly compared with conventional industrial robotic manipulators. The adaptive control algorithm improved task execution under varying object positions and operating conditions without requiring extensive reprogramming. The proposed robotic manipulation platform offers an economical automation solution suitable for SMEs seeking flexible manufacturing with minimal investment.