A robotic manipulator is often considered the most critical component of a robotic system. Ensuring its precise motion control is essential for applications in industrial automation, healthcare, home automation, and other real-life domains. However, achieving such precision requires detailed knowledge of link and joint measurements, as well as robust software support.

In this project, a 5-DoF mechanical robotic arm was utilized, and its Unified Robot Description Format (URDF) file was created entirely from scratch. The URDF facilitated the development of a fully functional robotic arm capable of maintaining accurate kinematic control. The precision of the arm was demonstrated through its deployment in a physical chess-playing setup. An end-to-end pipeline was implemented that integrated computer vision and deep learning with the Stockfish chess engine to predict the optimal chess move based on the player’s action, while the robotic manipulator executed the corresponding movements of the chess pieces.

A device was developed to enable real-time monitoring of dengue patients’ vital signs using specialized sensors and an embedded processing system that detects critical conditions such as rapid pulse pressure fall in Dengue Shock Syndrome (DSS). The device transmits alerts and live data to a Flutter-based mobile application for medical personnel, facilitating faster identification and prioritization of critical patients. This system enhances dengue patient management and reduces response time through the seamless integration of sensor technology and mobile communication.