Enhancing perceived safety in human–robot collaborative construction using immersive virtual environments

You, Sangseok; Kim, Jeonghwan; Lee, Sang Hyun; Kamat, Vineet R.; Robert, Lionel

doi:10.1016/j.autcon.2018.09.008

Cited by 131 publications

(38 citation statements)

References 68 publications

(88 reference statements)

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“…A large part of research in the field of industrial HRI also concentrates on the relationship between human and robot. These studies investigate primarily object-related factors, such as motion and appearance of the robot (e.g., References [ 11 , 46 , 74 , 105 ]), interaction mechanisms (e.g., References [ 51 , 59 ]), and the design of the workspace (e.g., Reference [ 103 ]). Their findings show that these factors are important, because they help people understand and predict the behavior of the robot, which is essential for trusting it and feeling safe when interacting with it.…”

Section: Acceptance Of Human-robot Interactionmentioning

confidence: 99%

Friend or Foe? Understanding Assembly Workers’ Acceptance of Human-robot Collaboration

Meißner

Trübswetter²,

Conti-Kufner

et al. 2020

J. Hum.-Robot Interact.

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Due to rising demands on productivity and flexibility, assembly processes are currently experiencing a substantial transformation. Workstations where humans and robots work closely together are becoming increasingly popular, as they provide major advantages compared to manual assembly and full automation. Yet, human-robot collaboration (HRC) can only be successful if the workforce is willing to accept it. How assembly workers perceive HRC still has to be properly investigated. An exploratory investigation using a Grounded Theory approach was conducted to identify factors that are likely to influence workers' acceptance of introducing HRC at work. Seventeen workers with various levels of HRC experience from four different manufacturing companies were interviewed. Findings reveal that some workers perceive HRC as a threat, while others regard it as an opportunity. This perception seems to depend both on their thoughts and feelings about the technology, i.e., collaborative robots, and the organizational change associated with the introduction of this technology. Several factors related to the robot (object-related factors), the individual background of the workers (subject-related factors), and the organizational environment (context-related factors) are found to influence workers' thoughts and feelings. Implications for researchers and manufacturing companies are outlined. CCS Concepts: • Human-centered computing → HCI theory, concepts and models ; Empirical studies in HCI ; User models; User studies; Field studies; Collaborative interaction; • Social and professional topics → Socio-technical systems; • Computer systems organization → Robotics ; • Social and professional topics → Computer supported cooperative work; Employment issues; • Applied computing → Psychology;

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Section: Acceptance Of Human-robot Interactionmentioning

confidence: 99%

Friend or Foe? Understanding Assembly Workers’ Acceptance of Human-robot Collaboration

Meißner

Trübswetter²,

Conti-Kufner

et al. 2020

J. Hum.-Robot Interact.

View full text Add to dashboard Cite

show abstract

“…Therefore, to reduce fall-related accidents, robotics could apply in most dangerous places so that we can reduce the fatalities in the construction industry around the world. However, this is a challenging task and requires huge effort to enhance human-robotics interaction [59]. In addition, there is huge research work is required to the adaptation of robotics in the construction industry in the near future.…”

Section: Advance Technologies For Safety Enhancementmentioning

confidence: 99%

An Overview of Construction Occupational Accidents in Hong Kong: A Recent Trend and Future Perspectives

Shafique

Rafiq

2019

Applied Sciences

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The construction industry is infamous for a high number of fatalities around the world. The Hong Kong construction industry is also one of the most hazardous industries for worker’s safety compared to all other industries. This paper presents a current trend of occupational injuries among Hong Kong construction workers. Yearly reports of the labour department of Hong Kong were evaluated in this study. A statistical analysis of incidence rates (IRs) and mortality rates (MRs) was performed to evaluate each accident type. Moreover, analysis of variance (ANOVA) was performed to find out the relationship between IRs and MRs of individual accident type. From the analysis, “Slip, trip or fall on the same level” caused the higher number of occupational injuries while “fall of the person from height” caused the higher number of occupational fatalities from 2011 to 2017. Finally, the future perspectives of effective safety management system incorporated with advance technologies in the construction industry are highlighted in this study.

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“…Automated activity identification of construction equipment enables real-time applications including, but not limited to, real-time productivity monitoring (Ahn et al, 2015;Louis and Dunston, 2016b;Kim H. et al, 2018), real-time safety analysis (Carbonari et al, 2011;Cheng and Teizer, 2013;Rashid et al, 2017;Rashid and Behzadan, 2018), real-time routing of resources (Louis and Dunston, 2016a), and realtime AR/VR visualization (Ku et al, 2011;Dong and Kamat, 2013;Park et al, 2014;You et al, 2018). Offline applications enabled by automated activity identification include preparing dynamic simulation input (Akhavian and Behzadan, 2013;Vahdatikhaki and Hammad, 2014), automated cycle time analysis (Vahdatikhaki and Hammad, 2014;Mathur et al, 2015;Kim H. et al, 2018), operation analysis (Vahdatikhaki and Hammad, 2014;, and fuel use analysis (Lewis et al, 2011) etc.…”

Section: Introductionmentioning

confidence: 99%

Automated Activity Identification for Construction Equipment Using Motion Data From Articulated Members

Rashid

Louis

2020

Front. Built Environ.

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Real-time monitoring and assessment of construction resources have always been a challenge due to the unique, dynamic, and complex nature of each construction site and operation. The ability to automatically classify activities performed by various equipment in real time can aid in making timely tactical operational decisions that can lead to increased fleet productivity, reduced time and cost of operations, and minimized idle times. Such endeavors have traditionally been performed manually through human observation, making it extremely labor and time-intensive. Meanwhile, the development of low-cost micro-electro-mechanical systems (MEMS) with rapidly evolving computing, networking, and storage capabilities, along with the advances in computational techniques such as machine learning present new opportunities in the real-time activity identification domain. Even though previous studies have shown promising results for equipment activity identification at limited levels of detail, they have a fundamental limitation in their reliance on the equipment vibration. Equipment vibration is highly dependent on factors that are extrinsic to the performance of an activity itself such as ground conditions, age and condition of equipment, and operator skill. This aspect of current methods necessitates the collection of training data from the specific equipment of interest and requires manual labeling of training data, thereby limiting its application across different types of equipment and operating conditions. This paper investigates the use of activity-specific equipment motions instead of vibration for activity identification. This approach is the first step toward the larger goal of generating training data automatically from virtual kinematic models of equipment in the future. This paper also provides an array of sensitivity analyses in order to determine the most appropriate parameters for implementing machine learning algorithms for equipment activity identification. A case study was performed using an excavator working on an earthmoving site that demonstrated a significant improvement in equipment activity identification results by utilizing inertial measurement unit (IMU) data of different articulated elements over previous efforts. The results of this paper indicate that more accurate results for activity identification can be obtained by using articulated equipment motion over vibration, which paves the way for the automatic generation of labeled training data in the future.

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Enhancing perceived safety in human–robot collaborative construction using immersive virtual environments

Cited by 131 publications

References 68 publications

Friend or Foe? Understanding Assembly Workers’ Acceptance of Human-robot Collaboration

Friend or Foe? Understanding Assembly Workers’ Acceptance of Human-robot Collaboration

An Overview of Construction Occupational Accidents in Hong Kong: A Recent Trend and Future Perspectives

Automated Activity Identification for Construction Equipment Using Motion Data From Articulated Members

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