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1. Robots in Inspection and Monitoring of Buildings and Infrastructure: A Systematic Review

   https://doi.org/10.3390/app13042304  

   Halder, Srijeet; Afsari, Kereshmeh (February 2023, Applied Sciences)

   Regular inspection and monitoring of buildings and infrastructure, that is collectively called the built environment in this paper, is critical. The built environment includes commercial and residential buildings, roads, bridges, tunnels, and pipelines. Automation and robotics can aid in reducing errors and increasing the efficiency of inspection tasks. As a result, robotic inspection and monitoring of the built environment has become a significant research topic in recent years. This review paper presents an in-depth qualitative content analysis of 269 papers on the use of robots for the inspection and monitoring of buildings and infrastructure. The review found nine different types of robotic systems, with unmanned aerial vehicles (UAVs) being the most common, followed by unmanned ground vehicles (UGVs). The study also found five different applications of robots in inspection and monitoring, namely, maintenance inspection, construction quality inspection, construction progress monitoring, as-built modeling, and safety inspection. Common research areas investigated by researchers include autonomous navigation, knowledge extraction, motion control systems, sensing, multi-robot collaboration, safety implications, and data transmission. The findings of this study provide insight into the recent research and developments in the field of robotic inspection and monitoring of the built environment and will benefit researchers, and construction and facility managers, in developing and implementing new robotic solutions. 

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2. Timber Construction Automation Using Industrial Robotic Arm Integrated with an Interactive Rail System

   https://doi.org/10.1061/9780784485248.121  

   Yang, Fan; Zhang, Jiansong (January 2024, American Society of Civil Engineers)

   Challenged by the prevalent workforce shortage, the construction industry is picking up interest in using robotic arms in construction operations, especially in the context of modular construction and prefabrication. However, the lack of systematic investigations into integrating robotic arms with mobile systems to enhance mobility and operational range has been identified as one main research gap. Stationary robotic arms have inherent limitations in their range, making mobility a critical need. To address that issue, in this paper, the authors proposed a mobile construction robotic system to facilitate their use in the automation of timber frame assembly operation. The authors simulated the system to assess the interactions and coordination among its various components, and to identify potential areas for improvement. This study showcased the effectiveness of the new system design in improving the timber construction automation process and reveals its potential for further exploration in the realm of mobile construction robotics. 

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3. On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)

   https://doi.org/10.1609/icaps.v34i1.31503  

   Pallagani, Vishal; Muppasani, Bharath Chandra; Roy, Kaushik; Fabiano, Francesco; Loreggia, Andrea; Murugesan, Keerthiram; Srivastava, Biplav; Rossi, Francesca; Horesh, Lior; Sheth, Amit (May 2024, Proceedings of the International Conference on Automated Planning and Scheduling)

   Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity. Based on a comprehensive review of 126 papers, this paper investigates eight categories based on the unique applications of LLMs in addressing various aspects of planning problems: language translation, plan generation, model construction, multi-agent planning, interactive planning, heuristics optimization, tool integration, and brain-inspired planning. For each category, we articulate the issues considered and existing gaps. A critical insight resulting from our review is that the true potential of LLMs unfolds when they are integrated with traditional symbolic planners, pointing towards a promising neuro-symbolic approach. This approach effectively combines the generative aspects of LLMs with the precision of classical planning methods. By synthesizing insights from existing literature, we underline the potential of this integration to address complex planning challenges. Our goal is to encourage the ICAPS community to recognize the complementary strengths of LLMs and symbolic planners, advocating for a direction in automated planning that leverages these synergistic capabilities to develop more advanced and intelligent planning systems. We aim to keep the categorization of papers updated on https://ai4society.github.io/LLM-Planning-Viz/, a collaborative resource that allows researchers to contribute and add new literature to the categorization. 

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4. Human-in-the-loop: Role in Cyber Physical Agricultural Systems

   https://doi.org/10.15837/ijccc.2021.2.4166  

   Sreeram, Maitreya; Nof, Shimon Y. (April 2021, INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL)

   null (Ed.)

   With increasing automation, the ‘human’ element in industrial systems is gradually being reduced, often for the sake of standardization. Complete automation, however, might not be optimal in complex, uncertain environments due to the dynamic and unstructured nature of interactions. Leveraging human perception and cognition can prove fruitful in making automated systems robust and sustainable. “Human-in-the-loop” (HITL) systems are systems which incorporate meaningful human interactions into the workflow. Agricultural Robotic Systems (ARS), developed for the timely detection and prevention of diseases in agricultural crops, are an example of cyber-physical systems where HITL augmentation can provide improved detection capabilities and system performance. Humans can apply their domain knowledge and diagnostic skills to fill in the knowledge gaps present in agricultural robotics and make them more resilient to variability. Owing to the multi-agent nature of ARS, HUB-CI, a collaborative platform for the optimization of interactions between agents is emulated to direct workflow logic. The challenge remains in designing and integrating human roles and tasks in the automated loop. This article explains the development of a HITL simulation for ARS, by first realistically modeling human agents, and exploring two different modes by which they can be integrated into the loop: Sequential, and Shared Integration. System performance metrics such as costs, number of tasks, and classification accuracy are measured and compared for different collaboration protocols. The results show the statistically significant advantages of HUB-CI protocols over the traditional protocols for each integration, while also discussing the competitive factors of both integration modes. Strengthening human modeling and expanding the range of human activities within the loop can help improve the practicality and accuracy of the simulation in replicating a HITL-ARS. 

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5. Generative AI in Lean Construction: A Scoping Review

   GanjiRad, M; Ilbeigi, M (June 2025, IGLC)

   Seppänen, O; Koskela, L; Murata, K (Ed.)

   Generative Artificial Intelligence (AI), specifically Large Language Models (LLMs), has the potential to reshape construction management practices. These novel solutions can transform lean construction by enabling real-time data analysis, streamlined communication, and automated decision-making across project teams. They can facilitate enhanced collaboration by generating insights from vast construction data sets, improving workflow efficiency, and reducing waste. Additionally, LLMs can support predictive modeling, proactive risk management, and knowledge sharing, aligning with lean principles of maximizing value and minimizing inefficiencies. Given the recent advancements in generative AI, it is critical to systematically shape future research directions by building on the existing body of knowledge and addressing key knowledge gaps. The first step toward identifying knowledge gaps and uncovering critical areas that remain underexplored is to systematically analyze the existing body of knowledge. Therefore, this study conducts a scoping review to synthesize the extent, range, and nature of existing studies that have proposed novel solutions using generative AI and LLMs for various aspects of construction management. The outcomes of this systematic scoping review will help identify potential research directions for future studies in this domain. 

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