Façade Cleaning Robot for the Skyscraper

Böck, Thomas; Bulgalow, A.; Ashida, Shigeki

doi:10.22260/isarc2002/0036

Cited by 2 publications

(2 citation statements)

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“…Sigmoid function maps values to the range of 0 to 1. and the tanh function gives weightage to the values which are passed deciding their level of importance ranging from −1 to 1, expressed in Equation (2).…”

Section: Recognizing Human Activities Using Lstm With 3d Joint Locationsmentioning

confidence: 99%

“…Among these, robots stand out as one of the most promising technologies capable of revolutionizing the construction landscape. These mechanical marvels possess the remarkable ability to carry out repetitive or hazardous construction activities with unparalleled speed, strength, and safety, thereby significantly increasing overall productivity and reducing fatalities [ 1 , 2 , 3 ]. However, the unique cognitive skills possessed by human workers remain indispensable for certain intricate tasks that demand adaptability and ingenuity.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Camera-Based Human Activity Recognition for Human–Robot Collaboration in Construction

Jang,

Jeong,

Younesi Heravi

et al. 2023

Sensors

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As the use of construction robots continues to increase, ensuring safety and productivity while working alongside human workers becomes crucial. To prevent collisions, robots must recognize human behavior in close proximity. However, single, or RGB-depth cameras have limitations, such as detection failure, sensor malfunction, occlusions, unconstrained lighting, and motion blur. Therefore, this study proposes a multiple-camera approach for human activity recognition during human–robot collaborative activities in construction. The proposed approach employs a particle filter, to estimate the 3D human pose by fusing 2D joint locations extracted from multiple cameras and applies long short-term memory network (LSTM) to recognize ten activities associated with human and robot collaboration tasks in construction. The study compared the performance of human activity recognition models using one, two, three, and four cameras. Results showed that using multiple cameras enhances recognition performance, providing a more accurate and reliable means of identifying and differentiating between various activities. The results of this study are expected to contribute to the advancement of human activity recognition and utilization in human–robot collaboration in construction.

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Section: Recognizing Human Activities Using Lstm With 3d Joint Locationsmentioning

confidence: 99%