Autonomous Construction Sites
Understanding Sensory Information Requirements for Teleoperation in Construction
The construction industry has recently experienced an increased deployment of automation and robotics on- and off-site. Despite technological developments, however, the use of fully autonomous machines has been restricted to a limited number of applications, with remotely-operated and, more recently, teleoperated machines representing the majority of robots found in construction sites.
For teleoperated robots, specifically, although important results have been borrowed from more technologically-advanced industries such as aerospace and military, in most cases, the human-machine interfaces are rather primitive, with control systems relying solely on levers that are used to control the equipment's joints and end-effectors, and feedback systems based on 2D images on flat screens and audio. In the teleoperation context, given the problems that arise from decoupling the worker from the physical environment where the construction task takes place, it is key to understand what the sensory information requirements are for different classes of construction tasks at various levels of task complexity to design human-machine interfaces that reduce the operator's cognitive loads while increasing safety, health, and potentially productivity levels. In this project, we have selected demolition as our case study. We aim to understand the operator's sensory information requirements for a variety of demolition tasks at varying levels of complexity to develop effective human-machine interfaces for teleoperation workstations.
Moreover, our research goes beyond optimization for efficiency alone. We intend to use the insights gained during the design and testing of the interface to propose adaptation alternatives aimed at promoting inclusivity and diversity in the construction industry. These adaptations will be designed to attract a broader spectrum of historically marginalized workers in the construction industry, including females, young professionals, older workers nearing retirement, and individuals with disabilities.
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Status: On-going
Reskilling & Upskilling for Human-Robot Interactions on Construction Sites
The construction industry is increasingly adapting robots for automating various construction tasks. Unlike other industries, where multiple robots complete various tasks independently, worker–robot collaboration is necessary for construction activities due to the specific project and site requirements, such as the need for multiple parallel or sequential activities, exposure to outdoor conditions, and dangerous working conditions.
his project aims to contribute to the fundamental understanding of workforce training impacts on workers’ safety behavior, operational skills, trust-in-automation, robot operation self-efficacy, situational awareness, and mental workload. We focus on understanding the impact of VR-based training as a whole package and how its components moderate the development of human-related factors in Human-Robot Interaction (HRI). Through this effort, compared with real-life situations, we will systematically collect data on the affordances and hindrances of existing automation and common problems that arise at the individual- and construction site levels. In addition, the environment developed as part of this project will serve as a platform for the research community to explore important research questions related to human-machine collaboration and its impact on construction work processes.
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Status: Completed
Worker-Robot Collaboration on Construction Sites
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The construction industry is increasingly adapting robots for automating various construction tasks. Unlike other industries, where multiple robots complete various tasks independently, worker–robot collaboration is necessary in construction activities due to specific project and site requirements, such as the need for multiple parallel or sequential activities, exposure to outdoor conditions and dangerous working conditions .
In this project, we aim to contribute to the fundamental understanding of trust-in-automation, specifically understanding how construction workers develop trust-in-automation. We focus on understanding the role of individual, as well as task differences, and how they moderate the development of trust-in-automation. Through this effort, compared with real-life situations, we will systematically collect data on the affordances and hindrances of existing automation and common problems that arise at the individual- and construction site-level. In addition, the environment, developed as part of this project, will serve as a platform for the research community to explore important research questions as they relate to human-machine collaboration, as well as human-machine collaboration’s impact on construction work processes.
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Status: Completed
Understanding & Monitoring Fatigue in Construction
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Construction industry relies heavily on manual labor, and has one of the highest rate of fatal and nonfatal injuries. Construction work typically involves physically demanding tasks often performed in harsh environmental conditions, which can lead to fatigue. Fatigue can lead to reduction in productivity, poor judgement, poor quality of work and increased risk of accidents. Most common methods of assessing fatigue rely on subjective feedback and questionnaires, which can be cumbersome to deploy in construction sites.
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The goal of this study is to automate the assessment of fatigue by understanding the physiological changes that occur during physical work measured using different wearable sensors. Sensors used include temperature sensors, heart rate monitor, and electroencephalogram (EEG) to monitor thermoregulatory changes, variations in heart rate, and changes in electrical activity in the brain, respectively. The study also aims to study the links between the development of physical and mental fatigue during physical work typical to construction industry and how the increase in physical and/or mental fatigue relates to increased risk of accidents and reduction in productivity. If successful, the study will result in novel sensing methods and algorithms for monitoring fatigue based on different physiological changes. Preventive measure,s such as better work rest schedules, can be developed and implemented at construction sites based on real time monitoring of fatigue, which can lead to the reduction in accidents in the industry.
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Status: Completed