Environmental Monitoring in the Oil and Gas Industry Using Machine Vision Associated with Robotics and AI

Abstract

Emerging technologies such as artificial intelligence and unmanned vehicle systems present promising opportunities for enhancing environmental monitoring in the oil and gas industry. Traditional methods often fall short in delivering real-time data, broad spatial coverage, and rapid response. This chapter focuses on AI-powered multi-drone systems designed to detect oil contamination in marine environments. To understand their function, the chapter first outlines the essential components of unmanned systems, highlighting recent advances in machine vision that have significantly improved detection, navigation, and obstacle avoidance in harsh settings. Deploying unmanned aerial vehicle(s) offers advantages such as broader area coverage, reduced mission duration, and increased reliability. However, coordinating these systems introduces complexities, especially in communication, data exchange, real-time decision-making, power management, and collision avoidance. For instance, as per communication, wireless telecommunication is emphasized as a cornerstone of successful multi-drone operations. The chapter presents relevant simulation scenarios alongside field experiment insights to validate not only the communication but also system capabilities. Given the intricacy of multi-drone operations, the integration of subsystems often involves tradeoffs, which necessitates the use of optimization techniques. Through a balanced exploration of system modeling, analytical methods, and hardware prototyping, the chapter addresses common misconceptions surrounding the effectiveness of AI-integrated drone/swarm drones. Ultimately, readers will gain a comprehensive understanding of how intelligent, cooperative unmanned systems can revolutionize environmental monitoring in demanding industrial environments.