Design and modelling of a modular window cleaning robot

The design of a modular window façades cleaning robot is challenging given the conditions under which these robots are required to operate. In this work, we attempt to extend the locomotion capabilities of these robots beyond what is currently feasible. The modular design of three equal interconnect...

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Veröffentlicht in:Automation in construction 2019-07, Vol.103, p.268-278
Hauptverfasser: Vega-Heredia, M., Mohan, R. Elara, Wen, Tan Yeh, 'Aisyah, J. Siti, Vengadesh, A., Ghanta, S., Vinu, S.
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Sprache:eng
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Zusammenfassung:The design of a modular window façades cleaning robot is challenging given the conditions under which these robots are required to operate. In this work, we attempt to extend the locomotion capabilities of these robots beyond what is currently feasible. The modular design of three equal interconnected sections of our robot, called Mantis, allows increasing the range concerning the work of cleaning window façades. Mantis has the ability to make transition from one window panel to another by crossing over the metallic panel. We implemented the inductive sensors to detect the metallic frame for autonomous crossover. The mechanical design and system architecture are introduced in detail, followed by a detailed description of the locomotion control and the sensor system for the classification of the metallic frame. The experimental results are presented to validate Mantis' abilities. •The document show the design of a modular window facades cleaning robot called Mantis.•The modular design of three equal interconnected sections increases the range concerning the work of cleaning window facades.•Mantis has the ability to make the transition from one window to another, avoiding the window frame.•Mechanism, locomotion control, system architecture and, glass and metallic frame classification system are described.•The experiments conducted validated the proposed approach and the ability of the Mantis.
ISSN:0926-5805
1872-7891
DOI:10.1016/j.autcon.2019.01.025