Recognizing object surface materials to adapt robotic disinfection in infrastructure facilities

Abstract

Existing disinfection robots are not intelligent enough to adapt their actions to object surface materials for precise and effective disinfection. To address this problem, a new framework is developed to enable the robot to recognize various object surface materials and to adapt its disinfection methods to be compatible with recognized object surface materials. Specifically, a new deep learning network is proposed that integrates multi‐level and multi‐scale features to classify the materials on contaminated surfaces requiring disinfection. The infection risk of contaminated surfaces is computed to choose the appropriate disinfection modes and parameters. The developed material recognition method demonstrates state‐of‐the‐art performance, achieving an accuracy of 92.24% and 91.84% on the Materials in Context Database validation and test datasets, respectively. The proposed method was also tested and evaluated in the context of healthcare facilities, where the material classification achieved an accuracy of 89.09%, and the adaptive robotic disinfection was successfully implemented.

Deep deterministic policy gradient with constraints for gait optimisation of biped robots

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A human-simulated fuzzy membrane approach for the joint controller of walking biped robots

“…Biped robots have attracted much attention due to their good mobility and agility, and they have a wide range of application prospects in civil rescue, military surveillance, etc. As the basis of biped robot movement, walking control has always been the focus of research [1,2,3,4]. Generally, a biped robot walks by driving its joints to track the target trajectories [5,6,7].…”

Iterative application of generative adversarial networks for improved buried pipe detection from images obtained by ground‐penetrating radar

“…Object detection is also used for various purposes in civil engineering. For example, there are studies on detecting vehicles, pedestrians, and cyclists for autonomous driving (Carranza-García et al, 2022); detecting workers and heavy equipment for work zone safety (Shen et al, 2021); detecting external building objects to improve the accuracy and completeness of bridge information modeling (BIM) data (Ying et al, 2022); detecting the floating part from infrared images (Chun & Hayashi, 2021); detecting damaged parts from a 3D bridge model (Yamane et al, 2022(Yamane et al, , 2023; detecting material types of object surfaces for effective disinfection by robots (Hu & Li, 2022); and detecting unexploded ordnance materials in shallow waters to remove potentially dangerous objects (Foresti & Scagnetto, 2022).…”