AI Enabled Robot for Data Collection in Unreachable and Extreme Environments: A Review
Aleena Francis · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2024
This paper presents a groundbreaking approach to data collection in hazardous or inaccessible environments, presenting the design, development, and implementation of an innovative autonomous robot. The robot is designed to navigate and collect valuable data from locations too dangerous or remote for human exploration, enabling scientific research and exploration in unprecedented ways. The AI-powered drone is equipped for precise human identification, controlled through a user-friendly mobile app. The software analyzes live drone footage to detect human presence using models like YOLO, with high accuracy in real-time human detection tasks. The robot is equipped with an array of sensors, including cameras, and uses image processing technology for processing images. GPS tracking technology is used for device tracking. The proposed autonomous robot promises to revolutionize data collection in unreachable environments, opening new avenues for scientific discovery, resource assessment, and environmental monitoring. Key Words: YOLOV8, UAV, Python, Flask, Computer vision, AI.