Design and Development of a Mini Excavator as Innovative Learning Media - Abdul Tahir; Irdam Irdam; Jasman Jasman; Musakirawati Musakirawati (International Journal of Engineering, Science and Information Technology, 2026)
The rapid advancement of mechanical and electronic engineering, particularly in heavy equipment and construction automation, requires innovative learning media that effectively bridge theoretical knowledge and practical implementation. Conventional instructional methods often provide limited opportunities for students to develop hands-on experience with complex mechatronic systems. This study presents the design, development, and evaluation of a miniature battery-electric excavator intended as an educational platform for teaching construction robotics, automation, and sustainable engineering principles. The development process followed a structured methodology consisting of conceptual design, mechanical fabrication, electronic integration, simulation, and functional testing. Unlike conventional excavators that rely on hydraulic actuators, the proposed system employs electric motors to drive the boom, arm, and bucket mechanisms, demonstrating an energy-efficient and environmentally friendly alternative. Locomotion is achieved using DC gearbox motors for wheel movement, while an independent DC motor enables 90° rotational motion. The control architecture is based on an Arduino Mega microcontroller integrated with a wireless PS2 joystick, providing intuitive and precise user control. Lightweight aluminum was selected for the backhoe assembly to reduce overall weight, whereas the main chassis was constructed from mild steel to ensure structural strength and operational stability. Experimental evaluation confirmed that the prototype successfully performed essential excavation motions, achieving a maximum horizontal reach of 1,400 mm, a vertical reach of 1,420 mm, an average operating speed of 0.5 m/s, and a maximum speed of 1.0 m/s. With compact dimensions of 800 × 600 × 750 mm, the system is safe, portable, and suitable for laboratory-based education. Furthermore, the platform supports Deep Neural Network (DNN) data generation and reinforcement learning research, providing a cost-effective and safe environment for advancing student competencies in intelligent construction technologies