Hand Gestures Controlled Robotic Car using Object Detection, Computer Vision and AI

Abstract

The primary aim is to create a versatile robotic system capable of accurately detecting and classifying various objects while autonomously navigating different environments. By employing state-of-the-art machine learning techniques, especially deep learning approaches, the robot is equipped with an advanced object detection model that facilitates real-time processing and decision-making. To enhance the robot's functionality in dynamic settings, the project incorporates a modular design that integrates sensors, cameras, and processing units. A diverse dataset will be compiled for model training to improve accuracy and reliability across various operational scenarios. The ongoing development phase will involve thorough testing and iterative enhancements of both software and hardware components to ensure optimal performance. The potential applications of this robotic system span multiple industries, including logistics, industrial automation, security, and healthcare, highlighting its versatility and relevance in real-world applications. Beyond contributing to the field of robotics, this initiative seeks to enrich the growing body of research in artificial intelligence and autonomous systems. Ultimately, the successful implementation of this AI-driven robot will pave the way for further innovations in intelligent automation, thereby enhancing productivity and security across diverse sectors.