skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: Evaluating the Perception of Human-Robot Collaboration among Construction Project Managers
In construction applications, a robot is commonly seen a semi-automated tool or a piece of equipment that assists with specialized work tasks. However, as robots become more technically capable and widely available, they may be seen more as a teammate or co-worker that collaborates with human crews. Using a survey questionnaire, 63 project managers from two national construction management firms in the US were shown videos of three different applications of robotic systems, each exhibiting different characteristics, and were asked to share their perceptions of the robot. Through a between and across group comparison of their responses, we found that a robot was more likely to be seen as a teammate when its movement was less unpredictable, it was seen as more productive than human workers, it was considered durable, it remained constantly active, it took its surroundings into account before moving, it worked well alongside human workers, it was not unreliable, and it made the task more predictable. These findings identify clear challenges for human-robot teaming and the design of robotic systems for construction applications.  more » « less
Award ID(s):
1928415
PAR ID:
10357464
Author(s) / Creator(s):
; ; ;
Editor(s):
Jazizadeh, Farrokh; Shealy, Tripp; and Garvin, Michael J.
Date Published:
Journal Name:
Construction Research Congress 2022: Computer Applications, Automation, and Data Analytics
Page Range / eLocation ID:
550 to 559
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, Journal of Computing in Civil Engineering)
    With the construction sector primed to incorporate such advanced technologies as artificial intelligence (AI), robots, and machines, these advanced tools will require a deep understanding of human–robot trust dynamics to support safety and productivity. Although other disciplines have broadly investigated human trust-building with robots, the discussion within the construction domain is still nascent, raising concerns because construction workers are increasingly expected to work alongside robots or cobots, and to communicate and interact with drones. Without a better understanding of how construction workers can appropriately develop and calibrate their trust in their robotic counterparts, the implementation of advanced technologies may raise safety and productivity issues within these already-hazardous jobsites. Consequently, this study conducted a systematic review of the human–robot trust literature to (1) understand human–robot trust-building in construction and other domains; and (2) establish a roadmap for investigating and fostering worker–robot trust in the construction industry. The proposed worker–robot trust-building roadmap includes three phases: static trust based on the factors related to workers, robots, and construction sites; dynamic trust understood via measuring, modeling, and interpreting real-time trust behaviors; and adaptive trust, wherein adaptive calibration strategies and adaptive training facilitate appropriate trust-building. This roadmap sheds light on a progressive procedure to uncover the appropriate trust-building between workers and robots in the construction industry. 
    more » « less
  2. ; ; (, Frontiers of Engineering Management)
    Robots present an innovative solution to the construction industry’s challenges, including safety concerns, skilled worker shortages, and productivity issues. Successfully collaborating with robots requires new competencies to ensure safety, smooth interaction, and accelerated adoption of robotic technologies. However, limited research exists on the specific competencies needed for human—robot collaboration in construction. Moreover, the perspectives of construction industry professionals on these competencies remain underexplored. This study examines the perceptions of construction industry professionals regarding the knowledge, skills, and abilities necessary for the effective implementation of human—robot collaboration in construction. A two-round Delphi survey was conducted with expert panel members from the construction industry to assess their views on the competencies for human—robot collaboration. The results reveal that the most critical competencies include knowledge areas such as human—robot interface, construction robot applications, human—robot collaboration safety and standards, task planning and robot control system; skills such as task planning, safety management, technical expertise, human—robot interface, and communication; and abilities such as safety awareness, continuous learning, problemsolving, critical thinking, and spatial awareness. This study contributes to knowledge by identifying the most significant competencies for human—robot collaboration in construction and highlighting their relative importance. These competencies could inform the design of educational and training programs and facilitate the integration of robotic technologies in construction. The findings also provide a foundation for future research to further explore and enhance these competencies, ultimately supporting safer, more efficient, and more productive construction practices. 
    more » « less
  3. (, ProQuest)
    With the introduction of Industry 5.0, there is a growing focus on human-robot collaboration and the empowerment of human workers through the se of robotic technologies. Collaborative robots, or cobots, are well suited for filling the needs of industry. Cobots have a prioritization on safety and collaboration, giving them the unique ability to work in close proximity with people. This has the potential impact of increasing task productivity and efficiency while reducing ergonomic strain on human workers, as cobots can collaborate on tasks as teammates and support their human collaborators. However, effectively deploying and using cobots requires multidisciplinary knowledge spanning fields such as human factors and ergonomics, economics, and human-robot interaction. This knowledge barrier represents a growing challenge in industry, as workers lack the skills necessary to effectively leverage and realize the potential of cobots within their applications, resulting in cobots often being used non-collaboratively as a form of cheap automation. This presents several research opportunities for the creation of new cobot systems that support users in the creation of cobot interactions. The goal of this dissertation is to explore the use of abstraction and scaffolding supports within cobot systems to assist users in building human-robot collaborations. Specifically, this research (1) presents updates to the design of systems for planning and programming collaborative tasks, and (2) evaluates each system to understand how it can support user creation of cobot interactions. First, I present the CoFrame cobot programming system, a tool built on prior work, and illustrate how it supports user creation and understanding of cobot programs. Then, I present the evaluation of the system with domain experts, novices, and a real-world deployment to understand in which ways CoFrame does and does not successfully support users. I then describe the Allocobot system for allocating work and planning collaborative interactions, describing how it encodes multiple models of domain knowledge within its representation. Finally, I evaluate the Allocobot system in two real-world scenarios to understand how it produces and optimizes viable interaction plans. 
    more » « less
  4. ; ; ; ; ; (, 35th International Symposium on Automation and Robotics in Construction)
    The objective of this research is to evaluate vision-based pose estimation methods for on-site construction robots. The prospect of human-robot collaborative work on construction sites introduces new workplace hazards that must be mitigated to ensure safety. Human workers working on tasks alongside construction robots must perceive the interaction to be safe to ensure team identification and trust. Detecting the robot pose in real-time is thus a key requirement in order to inform the workers and to enable autonomous operation. Vision-based (marker-less, marker-based) and sensor-based (IMU, UWB) are two of the main methods for estimating robot pose. The marker-based and sensor-based methods require some additional preinstalled sensors or markers, whereas the marker-less method only requires an on-site camera system, which is common on modern construction sites. In this research, we develop a marker-less pose estimation system, which is based on a convolutional neural network (CNN) human pose estimation algorithm: stacked hourglass networks. The system is trained with image data collected from a factory setup environment and labels of excavator pose. We use a KUKA robot arm with a bucket mounted on the end-effector to represent a robotic excavator in our experiment. We evaluate the marker-less method and compare the result with the robot’s ground truth pose. The preliminary results show that the marker-less method is capable of estimating the pose of the excavator based on a state-of-the-art human pose estimation algorithm. 
    more » « less
  5. ; ; ; ; (, Construction Robotics)
    Abstract Robotic automation in construction has created the need for new competencies that will enable the workforce to engage with robots safely and effectively. However, differing perceptions between industry professionals and academia make aligning academic programs with industry needs challenging. This study evaluates these perceptions to guide the design of HRC training programs. A three-round Delphi study was conducted separately with panels of industry professionals and academic experts to assess their views on HRC competencies in construction. The findings revealed that both panels identified human–robot interfaces, HRC safety and standards, robot control systems, and construction robot applications as the top five HRC knowledge areas. Industry professionals also emphasized task planning knowledge, while academic experts focused on HRC ethics. Key HRC skills include effective communication, safety management, technical proficiency, and compliance with regulations and standards, with industry professionals prioritizing proficiency in task planning and academics emphasizing human–robot interface proficiency. Both expert panels prioritized teamwork, continuous learning, problem-solving, communication, and adaptability as top-rated HRC abilities. This study contributes to knowledge by defining key HRC competencies and identifying differences in priorities between industry and academia. These insights can guide the development of academic curricula that better align with industry needs, supporting the creation of training programs that equip the workforce with the competencies required for safe and effective robotic collaboration. The study also promotes collaboration between industry and academia, fostering innovation in HRC and robotics in construction. Future research directions are proposed to explore innovative training methods to equip the future workforce with HRC competencies. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email