Advances in Multimodal Data Fusion, AI, Machine Learning, and Robotics for Inspection, Fault Diagnosis, and Real-Time Solutions in Challenging Environments

Dear Colleagues,

In an era defined by rapid technological advancement, the integration of AI, machine learning, computer vision, and robotics has ushered in a new age of innovation, especially in challenging environments. This Special Issue is dedicated to the exploration of cutting-edge research and development in the areas of multimodal data fusion, AI, machine learning, computer vision, and robotics, with a particular focus on but not limited to their applications in subsea inspection, fault diagnosis, underwater mapping, navigation, object detection, semantic segmentation, behavioural analysis, 3D reconstruction, depth estimation, and real-time solutions.

Multimodal data fusion lies at the heart of our inquiry—a convergence of diverse sensor data sources including visual, acoustic, and environmental sensors. In a challenging environment such as the subsea environment, the synthesis of information from multiple sensors becomes pivotal, enabling autonomous vehicles, robots, and inspection systems to not only perceive but also comprehend the complexities of their surroundings. While AI and machine learning serve as the backbone of the systems driving this transformation, their application extends across the real-time decision-making processes of robots, predicting equipment failures in harsh conditions, and enabling dynamic, intelligent analysis. Additionally, the convergence of computer vision and robotics is instrumental in shaping the interaction between technology and the underwater world. They empower robots to perform tasks such as object detection, semantic segmentation, and behavioural analysis with remarkable precision.

The subsea domain demands not just intelligence but also real-time solutions. These environments often necessitate swift responses to unforeseen obstacles, the immediate detection of equipment faults, and the rapid creation of underwater topographical maps. Real-time capabilities are fundamental to the success of subsea missions, and we explore these capabilities in-depth.

Applications of these technologies extend far beyond subsea exploration. They are fundamental to domains such as simultaneous localisation and mapping (SLAM), autonomous vehicles, unmanned aerial vehicles (UAVs), medical and biomedical fields, smart agriculture, and smart cities. These technologies not only enrich our understanding of challenging environments but also offer potential solutions to critical problems in various domains. Whether it is the development of autonomous vehicles for land-based transportation, the advancement of 3D reconstruction techniques for urban planning, or the use of robotics in healthcare for surgical assistance, the implications are vast and transformative.

This Special Issue aims to bring together leading researchers, engineers, and experts from around the world to contribute their research articles, reviews, and pioneering insights. Our objective is to advance our comprehension of AI, machine learning, multimodal data fusion, computer vision, robotics, real-time solutions, and embedded systems in challenging environments.

We, the Guest Editors, invite you to join us in this exciting journey. Share your research, your perspectives, and your vision for the future of technology in challenging environments. Together, we will illuminate the depths, navigate the unknown, and redefine the boundaries of what is achievable.

Dr. Ali Rohan
Dr. Md Junayed Hasan
Dr. Kannan Somasundar
Guest Editors

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• multimodal data fusion
• AI in challenging environments
• robotics and autonomous systems
• subsea inspection
• fault diagnosis
• computer vision
• real-time solutions
• embedded systems
• 3D reconstruction
• depth estimation