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Autonomous navigation in construction environments is particularly challenging due to dynamic obstacles and uncertain surroundings. While recent advances in Building Information Modeling (BIM)-based planning have leveraged spatial and semantic information to improve navigation, most prior work assumes precise localization of the BIM model to enable global path planning. In contrast, this paper introduces an online replanning framework that registers obstacles on discovery within BIM and replans according to the updated semantic map. Our method integrates object-aware path planning by utilizing large language models (LLMs) to extract semantic danger sentiments from BIM-annotated objects and their spatial information about the mission environment. Additionally, we demonstrate practical feasibility by integrating a path tracking control, ensuring generated paths are not only safer but also realistically executable by mobile robots. Experimental results demonstrate an improved obstacle avoidance by 2.8× compared to traditional A* algorithms in dynamically updated environments. 

Quadrupeds are becoming increasingly popular in construction engineering research and practice for their affordability and accessibility. These robots navigate uneven terrain commonly found in construction sites, making them suitable vehicles for sensors and monitoring tasks. However, the lack of streamlined and fully developed client-side software packages inhibits rapid deployment of application-specific models to the field. Furthermore, substantial prerequisite knowledge of computer science and programming significantly impedes the ability of non-experts to adapt the robots to specific applications. In this work, we present a comprehensive framework to address these gaps in accessibility, enabling users to customize these robots to their needs. This framework provides a template that facilitates seamless communication between the robotic vehicle, edge devices, sensors, pathfinding algorithms, and a Unity simulation for mission planning and execution. As an example of this framework’s flexibility, we have conducted a case study using this template to demonstrate an application of the framework in the construction domain that performs worker activity recognition and features a novel self-labeling mechanism for construction activity video data. The findings highlight the potential of accessible software tools in expanding the utility of robotic platforms across various engineering domains. 

Robots have the potential to enhance safety on construction job sites by assuming hazardous tasks. While existing safety research on physical human-robot interaction (pHRI) primarily addresses collision risks, ensuring inherently safe collaborative workflows is equally important. For example, ergonomic optimization in co-manipulation is an important safety consideration in pHRI. While frameworks such as Rapid Entire Body Assessment (REBA) have been an industry standard for these interventions, their lack of a rigorous mathematical structure poses challenges for using them with optimization algorithms. Previous works have tackled this gap by developing approximations or statistical approaches that are error-prone or data-dependent. This paper presents a framework using Reinforcement Learning for precise ergonomic optimization that generalizes to different types of tasks. To ensure practicality and safe experimentations, the training leverages Inverse Kinematics in virtual reality to simulate human movement mechanics. Results of a comparison between the developed framework and ergonomically naive approaches are presented. 

This study aimed to investigate the key technical and psychological factors that impact the architecture, engineering, and construction (AEC) professionals’ trust in collaborative robots (cobots) powered by artificial intelligence (AI). This study seeks to address the critical knowledge gaps surrounding the establishment and reinforcement of trust among AEC professionals in their collaboration with AI-powered cobots. In the context of the construction industry, where the complexities of tasks often necessitate human–robot teamwork, understanding the technical and psychological factors influencing trust is paramount. Such trust dynamics play a pivotal role in determining the effectiveness of human–robot collaboration on construction sites. This research employed a nationwide survey of 600 AEC industry practitioners to shed light on these influential factors, providing valuable insights to calibrate trust levels and facilitate the seamless integration of AI-powered cobots into the AEC industry. Additionally, it aimed to gather insights into opportunities for promoting the adoption, cultivation, and training of a skilled workforce to effectively leverage this technology. A structural equation modeling (SEM) analysis revealed that safety and reliability are significant factors for the adoption of AI-powered cobots in construction. Fear of being replaced resulting from the use of cobots can have a substantial effect on the mental health of the affected workers. A lower error rate in jobs involving cobots, safety measurements, and security of data collected by cobots from jobsites significantly impact reliability, and the transparency of cobots’ inner workings can benefit accuracy, robustness, security, privacy, and communication and result in higher levels of automation, all of which demonstrated as contributors to trust. The study’s findings provide critical insights into the perceptions and experiences of AEC professionals toward adoption of cobots in construction and help project teams determine the adoption approach that aligns with the company’s goals workers’ welfare. 

Human activity recognition (HAR) using machine learning has shown tremendous promise in detecting construction workers’ activities. HAR has many applications in human-robot interaction research to enable robots’ understanding of human counterparts’ activities. However, many existing HAR approaches lack robustness, generalizability, and adaptability. This paper proposes a transfer learning methodology for activity recognition of construction workers that requires orders of magnitude less data and compute time for comparable or better classification accuracy. The developed algorithm transfers features from a model pre-trained by the original authors and fine-tunes them for the downstream task of activity recognition in construction. The model was pre-trained on Kinetics-400, a large-scale video-based human activity recognition dataset with 400 distinct classes. The model was fine-tuned and tested using videos captured from manual material handling (MMH) activities found on YouTube. Results indicate that the fine-tuned model can recognize distinct MMH tasks in a robust and adaptive manner which is crucial for the widespread deployment of collaborative robots in construction. 

Construction technology researchers and forward-thinking companies are experimenting with collaborative robots (aka cobots), powered by artificial intelligence (AI), to explore various automation scenarios as part of the digital transformation of the industry. Intelligent cobots are expected to be the dominant type of robots in the future of work in construction. However, the black-box nature of AI-powered cobots and unknown technical and psychological aspects of introducing them to job sites are precursors to trust challenges. By analyzing the results of semi-structured interviews with construction practitioners using grounded theory, this paper investigates the characteristics of trustworthy AI-powered cobots in construction. The study found that while the key trust factors identified in a systematic literature review -conducted previously by the authors- resonated with the field experts and end users, other factors such as financial considerations and the uncertainty associated with change were also significant barriers against trusting AI-powered cobots in construction.