AI-Based Self-Learning Robotic Arm Using Microcontroller

https://doi.org/10.46610/JCIE.2024.v010i01.004

Abstract

Robotic arms are essential components of day-to-day life likely manufacturing and logistics operations to provide more speed, more flexible decision-making and reduce the possibility of errors. The objective of this project is to make a robotic arm with machine learning features to automate the assembly by object and colour detection using openCV, and TensorFlow library. The basic arm is made of light material to provide maximum mobility which is controlled by an ATmega328P microcontroller with the aid of an Arduino development environment for hassle-free interfacing sensors. A webcam is used for sensing the surroundings. Feed from waveform is processed by openCV and TensorFlow library Using a Python environment the real-time coordinates data and colour information will be fed to the microcontroller unit that controls the arm servos accordingly to grip the appropriate object and place them in the predetermined location. The arm is also capable of learning trajectory which is taught to it using human hands. The use of easily available components reduces the cost but the same time leaves much room for future improvement. There are two approaches to machine learning used in robotic systems, the first one is a conventional approach to machine learning which is a complicated, expansive asset that is not suitable for several types of automation industries. A self-learning approach, called a behaviour-based robotic arm with a machine learning feature to automate the assembly by the object and colour detection using openCV, a python environment to be built to address the limitations of conventional robotics methodologies. This approach aims to enhance the capability of robotic platforms to offer more flexible decision-making, cost-effectiveness, reduced human error, and higher precision in processes.