As the demand and market for building maintenance are increasing, automated building façade cleaning has become essential. Robots are replacing human workers because cleaning work on high-rise buildings using gondolas can be dangerous. Several façade cleaning robots have been developed for climbing, and practical knowledge to clean the façade is being adopted in their cleaning devices. In this study, a passive linkage suspension mechanism and tri-star wheels are applied to solve the problems of unclean zones due to failures during overcoming obstacles and the problems through the use of additional actuators. Various mechanism models have been introduced and their performances have been compared based on dynamic simulation considering obstacle encounters.
As the number of high-rise buildings is increasing, more methods of exterior-wall cleaning are being developed. There are a few models based on artificial intelligence that determine the type and level of contamination primarily by moving the cleaning area. In this study, we propose an system using YOLOv3 algorithm, color-detection, to install on façade cleaning robot and brightness-discrimination. There are three types of contaminant-detection parameters: size, color, and brightness, and these parameters are subjected to a robust optimization process to maintain a constant detection rate under different conditions. The three parameters are determined via Taguchi method with signal to noise ratio and noise factors. An environment for algorithm testing is established, and artificial contamination is implemented on the specimen. A field test with the detection algorithm shall be performed in the near future.
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.
This paper proposes a three-degree-of-freedom manifold composed of three linear actuators. The proposed mechanism consists of a workspace suitable for facade cleaning and can compensate for the horizontal position from disturbances in a gondola-based exterior-wall cleaning. We design a cleaning manipulator that can ensure a constant cleaning area by compensating for the disturbance in each direction. The position, velocity kinematic, and Jacobian-based singularity analysis are presented, and kinematic variables are defined to extend a singularity-free workspace. In addition, optimization is performed based on an index that demonstrates the mechanical properties of the manipulator. The result shows how the manipulator compensates for the disturbances as well as the features of the optimization model. This study can be applied to robot manipulators for facade cleaning in the future.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.