Ensuring the structural safety of bridges, tunnels, construction machinery, and other critical infrastructure is essential for public safety, economic stability, and environmental protection. Traditional inspection methods—mainly relying on manual visual checks—are time-consuming, expensive, and often dangerous, especially in high-altitude, underwater, or hazardous environments. They are also prone to human error and often fail to detect early-stage defects, leading to unexpected structural failures and costly accidents.
Intelligent inspection robots have emerged as a powerful alternative, offering high efficiency, precision, and safety. These robots integrate advanced sensor technologies (e.g., high-resolution cameras, LiDAR, ultrasonic sensors, and infrared thermal imagers) with artificial intelligence algorithms, enabling them to autonomously navigate complex environments and identify structural defects in real time.
The review systematically categorizes four main types of inspection robots:
- Ground Mobile Robots – Designed for stable movement across complex terrains, these robots can perform long-duration, high-accuracy inspections of bridge decks, wind turbine blades, and roadways.
- Wall-Crawling Robots – Equipped with magnetic or suction systems, these robots scale vertical surfaces to detect cracks, corrosion, and structural deformation in bridge piers, high-rise buildings, and ship hulls.
- Aerial Robots – Offering rapid deployment and wide coverage, drones can inspect high and hard-to-reach areas such as bridge superstructures and crane components, capturing high-resolution images and 3D models even in challenging conditions.
- Underwater Robots – Adapted to harsh aquatic environments, these robots conduct precise inspections of submerged structures like bridge piers, dams, and offshore platforms.
The paper also highlights the integration of sensor fusion, deep learning-based data analysis, and autonomous navigation technologies such as SLAM (Simultaneous Localization and Mapping) and Ultra-Wideband (UWB) positioning to improve detection accuracy and reliability in GPS-denied environments.
Despite their promise, intelligent inspection robots face technical challenges, including maintaining stability in complex environments, processing large-scale multi-source data in real time, and achieving fully autonomous decision-making. The authors suggest future research directions such as deeper integration of machine learning, optimization of multi-robot collaboration, and improvements in energy efficiency and lightweight design.
The author emphasizes: "By combining robotics, advanced sensors, and artificial intelligence, inspection robots are reshaping the future of structural health monitoring. These technologies will help prevent catastrophic accidents, reduce maintenance costs, and extend the lifespan of critical infrastructure."
