Humanoid Left Arm Collaboration Empowered by IoT and Synchronization of Human Joints

Abstract

A robotic left arm has been developed in this research to assist individuals in safely navigating hazardous environments, utilizing Internet of Things (IoT) technology to synchronize its movements with those of a human operator in real-time. The primary objective of this work was to enhance the precision and responsiveness of the arm. Sensors and microcontrollers (ESP32, Arduino UNO) were integrated into the robotic arm to collect and transmit movement data. Advanced machine learning algorithms, including Inverse Kinematics, Reinforcement Learning, and Deep Learning, were employed to facilitate the arm's learning process and improve its actions. The system was evaluated in simulated hazardous scenarios to assess its performance. The results indicate that the robotic arm can adjust and move accurately, aligning with the human operator's arm movements. The robotic arm safely manages hazardous materials, demonstrating high precision in executing complex tasks. These findings underscore the effectiveness and safety enhancement achieved by integrating IoT and intelligent algorithms into robotic systems for high-risk occupations. This technology significantly enhances emergency response capabilities and critical operations, delivering dependable and precise robotic assistance. Overall, this research underscores the potential of IoT-enabled robotic arms to revolutionize human-robot interactions in demanding environments. By showcasing how real-time synchronization and advanced algorithms enhance the functionality and safety of robotic systems, this research presents a promising solution for strengthening the operational safety and efficiency in hazardous situations. The development and utilization of this technology represent substantial progress in fields necessitating precise and dependable robotic assistance, ultimately promoting improved collaboration between humans and robots in high-risk contexts.