Kinematics, sensors and control of the fully automated façade‐cleaning robot SIRIUSc for the Fraunhofer headquarters building, Munich

Elkmann, Norbert; Lucke, Mario; Krüger, Tino; Kunst, Dietmar; Stürze, Thomas; Hortig, Justus

doi:10.1108/01439910810868543

Cited by 13 publications

(7 citation statements)

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“…Therefore, many researchers have developed outer wall cleaning robots that replace the humans who perform this task manually [2,3,4,5,6,7]. Equipping these cleaning robots with systems that detect contaminants on outer walls will enable them to perform a greater variety of tasks.…”

Section: Introductionmentioning

confidence: 99%

Contaminated Facade Identification Using Convolutional Neural Network and Image Processing

et al. 2020

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In recent years, as number of new building getting larger, there has been an increased interest in the cleaning of exterior walls. Accordingly, there is a growing interest in automatic cleaning robots that move around the outer building façade. These robots are also required to apply different cleaning methods to remove various contaminants on the outer wall of the building. However, current surface contaminant detection systems can either detect only a single type of contaminant, or are not compact enough for installation on mobile platforms that move around the outer façade. As cleaning workers are able to distinguish various contaminants with the naked eye, we aim to solve this problem by developing a machinevision system using convolutional neural networks (CNNs) and image processing methods. As it is a compact system that uses only a camera to take pictures and a processor to process the images, it is suitable for applications involving mobile platforms. Object-type contaminants such as avian feces are handled by the YOLOv3 module using the object-detection algorithm. Area-type contaminants such as rusty stains are processed using the color-detection module using the HSV color space, median filter, and flood fill algorithm. Particle-type contaminants such as dust are handled by the grayscale module, converting images to grayscale images and then comparing the average brightness with a reference that is provided in advance. This proposed machine vision system will detect objects, areas, and particle-type contaminants with a single image and some reference images provided in advance.

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Section: Introductionmentioning

confidence: 99%

Contaminated Facade Identification Using Convolutional Neural Network and Image Processing

et al. 2020

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“…The SIRIUS robot is capable of performing an up-and-down motion on a vertical surface, using a crane installed on buildings for façade maintenance. The robot adheres to the wall by the suction system and moves with the help of linear actuators [10]. Even though the existing robot-aided solutions for façade cleaning each excel in their effectiveness and experimental results, the performance of the current robots are constrained to their target buildings.…”

Section: Introductionmentioning

confidence: 99%

Foot Location Algorithm considering Geometric Constraints of Façade Cleaning

Nansai

Itoh

2019

JASSE

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The purpose of this study is to propose a foot location algorithm for a glass façade cleaning robot. As an initial stage of this study, we set three assumptions regarding aimed module robot and window shape. These assumptions set the system some geometric constraint conditions: Right edge of the right cleaning unit moves along with the line of the right window frame. The cleaning area of right and left cleaning unit overlaps a little. The module robot walks with a step at a time. From the conditions, a foot location determination algorithm is constructed corresponding to four situations. The effectiveness of the algorithm is verified through a numerical simulation.

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“…The most common technology is to use passive suction cups [1]. Other methods use magnetic [2], van-der-Waals molecular adhesion [3] or kind of claws [4].…”

Section: Motivation and State Of The Artmentioning

confidence: 99%

Force and Traction Controlled Propulsion of the Omnidirectional Wheeled Climbing Robot Cromsci

et al. 2009

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Climbing on vertical concrete structures like bridge pylons or dams is still a great challenge for autonomous robots. This paper presents the force-and traction control system of the climbing robot Cromsci which uses a negative pressure system for adhesion and driven wheels for propulsion. Especially in vertical environments the propulsion system must be able to produce enough force for carrying and accelerating the robot contrarily to gravity. Slippery of the wheels must be minimized due to abrasion and uncontrollable movements of the robotic system. This can be done by measuring upcoming forces and taking them into account for a traction control system (TCS). Another problem may occur because of slightly different wheel orientations which will result in increasing shear forces. We will show a control system to minimize these errors and experimental results which demonstrate its functionality.

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Kinematics, sensors and control of the fully automated façade‐cleaning robot SIRIUSc for the Fraunhofer headquarters building, Munich

Cited by 13 publications

References 1 publication

Contaminated Facade Identification Using Convolutional Neural Network and Image Processing

Contaminated Facade Identification Using Convolutional Neural Network and Image Processing

Foot Location Algorithm considering Geometric Constraints of Façade Cleaning

Force and Traction Controlled Propulsion of the Omnidirectional Wheeled Climbing Robot Cromsci

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