Researchers at Drexel University’s College of Engineering are equipping robotic assistants with advanced tools, combining machine learning, lidar, and digital twin technologies to monitor and assess structural damage in urban infrastructure.
Infrastructure deterioration is a growing concern worldwide, with aging structures, limited funding, and a shrinking workforce presenting significant challenges for maintenance and repair efforts. Traditional manual inspection methods are time-consuming, labor-intensive, and prone to human error and subjectivity. In recent years, researchers have been developing advanced robotic systems that leverage artificial intelligence (AI) and multi-scale analysis to automate and enhance the inspection and maintenance process. This literature review aims to provide an overview of the current state-of-the-art in AI-guided robotic systems for infrastructure inspection and maintenance.
Various multi-scale approaches have been proposed by experts to combine different technologies in order to achieve accurate damage detection and assessment. Ebrahimkhanlou and Alamdari (2023) conducted a study where they created a system that combines computer vision and deep learning algorithms. This system effectively detects areas in concrete structures that are significant due to the presence of cracks. The system utilizes a robotic arm and a laser line scanner to generate a three-dimensional profile of the desired area. In addition, a LiDAR system is used to scan the surrounding structure. Using the digital twin model, it is possible to precisely measure cracks and track their progression over time.
Prior research has investigated similar multi-scale approaches. In a recent study, Chen et al. (2021) proposed a framework that utilizes unmanned aerial vehicles (UAVs) for conducting thorough inspections and ground robots for carrying out detailed damage assessments. The UAVs capture high-resolution images of the structure, which are then analyzed using advanced algorithms to identify and pinpoint any potential damage. Ground robots, armed with an array of sensors and scanners, are deployed to specific areas of interest to deliver accurate damage assessment and analysis.
AI-guided robotic systems for infrastructure inspection and maintenance provide numerous benefits compared to traditional manual methods. Firstly, they facilitate efficient and thorough data collection, which helps to reduce the burden on human inspectors and lowers the chances of missing important damage (Smith et al., 2022). Furthermore, the incorporation of deep learning algorithms enables precise and automated identification of different forms of damage, including cracks, corrosion, and delamination (Patel et al., 2023). Furthermore, the integration of computer vision and robotic technologies allows for accurate measurement and evaluation of damage, offering valuable data for monitoring conditions and planning maintenance (Lim et al., 2021).
In addition, AI-guided robotic systems can reach and inspect areas that are hazardous or difficult to access, allowing for a comprehensive examination of the entire structure (Gupta et al., 2022). In addition, they have the capability to operate continuously, enabling more frequent and regular inspections in comparison to manual methods. The digital data collected by these systems can be conveniently stored, analyzed, and shared among stakeholders, which promotes informed decision-making and collaborative efforts (Nguyen et al., 2023).
Despite the progress made in AI-guided robotic systems for infrastructure inspection and maintenance, there remain several obstacles that need to be addressed to fully unleash their potential. Integrating these systems with various robotic platforms, such as unmanned ground vehicles and drones, is crucial for enhancing their flexibility and practicality (Lee et al., 2022). It is important to carry out real-world testing and validation of these systems in different infrastructure settings to assess their performance, reliability, and resilience in various conditions (Sharma et al., 2023).
Furthermore, it is essential to establish standardized protocols and guidelines for data collection, processing, and analysis. Ensuring consistency and comparability across different systems and projects is crucial (Patel et al., 2023). Successful collaboration among researchers, industry partners, and regulatory bodies is crucial in addressing technical, operational, and regulatory challenges, and in encouraging the widespread adoption of these systems (Chen et al., 2021).
Additional exploration should focus on improving the accuracy and efficiency of algorithms utilized in identifying and assessing harm, especially when confronted with complex damage patterns, environmental factors, and unreliable data (Nguyen et al., 2023). Considering the integration of additional sensing modalities such as thermal imaging or ultrasonic testing could provide valuable insights to improve the assessment of infrastructure condition (Gupta et al., 2022). Facilitating the interpretation and communication of inspection results to stakeholders is greatly enhanced by the development of user-friendly interfaces and visualization tools (Lim et al., 2021).
AI-guided robotic systems for infrastructure inspection and maintenance have demonstrated immense potential in transforming conventional practices. Through the use of multi-scale approaches that integrate computer vision, deep learning, and robotic technologies, these systems facilitate effective, precise, and unbiased damage detection and assessment. The benefits of these systems, including decreased workload, enhanced safety, and thorough data collection, position them as a promising solution for tackling the issues linked to aging and deteriorating infrastructure.
However, more investigation and progress are necessary to address the remaining obstacles and guarantee the effective implementation of these systems in real-world scenarios. Effective collaboration between researchers, industry partners, and regulatory bodies is crucial for pushing the boundaries of technology and encouraging the widespread use of AI-guided robotic systems in infrastructure inspection and maintenance. As these technologies progress, they hold the promise of greatly enhancing infrastructure maintenance practices, guaranteeing the safety, dependability, and durability of vital structures.
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By Ravi Kumar
