Social Density Monitoring Toward Selective Cleaning by Human Support Robot With 3D Based Perception System
Autor: | Vinu Sivanantham, Braulio Félix Gómez, Rajesh Elara Mohan, Balakrishnan Ramalingam, Anh Vu Le, Tran Hoang Quang Minh |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Scheme (programming language)
General Computer Science Coronavirus disease 2019 (COVID-19) Computer science media_common.quotation_subject Real-time computing social distance human support robot 02 engineering and technology 01 natural sciences Perception 0202 electrical engineering electronic engineering information engineering General Materials Science human space computer.programming_language media_common 010401 analytical chemistry General Engineering COVID-19 Perception system Grid 0104 chemical sciences Task (computing) Vision sensor Robot 020201 artificial intelligence & image processing cleaning robotics lcsh:Electrical engineering. Electronics. Nuclear engineering computer lcsh:TK1-9971 |
Zdroj: | IEEE Access, Vol 9, Pp 41407-41416 (2021) |
ISSN: | 2169-3536 |
Popis: | Monitoring the safe social distancing then conducting efficient sterilization in potentially crowded public places are necessary but challenging especially during the COVID-19 pandemic. This work presents the 3D human space-based surveillance system enabling selective cleaning framework. To this end, the proposed AI-assisted perception techniques is deployed on Toyota Human Support Robot (HSR) equipped with autonomous navigation, Lidar, and RGBD vision sensor. The human density mapping represented as heatmap was constructed to identify areas with the level being likely the risks for interactions. The surveillance framework adopts the 3D human joints tracking technique and the accumulated asymmetrical Gaussian distribution scheme modeling the human location, size, and direction to quantify human density. The HSR generates the human density map as a grid-based heatmap to perform the safe human distance monitoring task while navigating autonomously inside the pre-built map. Then, the cleaning robot uses the levels of the generated heatmap to sterilize by the selective scheme. The experiment was tested in public places, including food court and wet market. The proposed framework performance analyzed with standard performance metrics in various map sizes spares about 19 % of the disinfection time and 15 % of the disinfection liquid usage, respectively. |
Databáze: | OpenAIRE |
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