Service robots for facade cleaning

Böhme, T.; Schmucker, U.; Elkmann, Norbert; Sack, M.

doi:10.1109/iecon.1998.724271

Cited by 14 publications

(6 citation statements)

References 2 publications

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“…Afterward, we draw a very rough picture of the evolutionary experiments that we have run, and we discuss the interactions between layers 2 and 3 for propagating successfully bred behaviors that require synchronization between the individuals in an OCbotics swarm. Consider the facades of large office buildings as examples of vertical surfaces: they are subject to cleaning (Bohme et al, 1998;Elkmann et al, 2002), trimming greenery (Pérez et al, 2011), and other maintenance tasks. As in the previous example, these tasks might benefit more from collaborative efforts than only in terms of efficiency.…”

Section: Collaborative Spatial Workmentioning

confidence: 99%

An Organic Computing Approach to Self-Organizing Robot Ensembles

2016

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Similar to the Autonomous Computing initiative, which has mainly been advancing techniques for self-optimization focusing on computing systems and infrastructures, Organic Computing (OC) has been driving the development of system design concepts and algorithms for self-adaptive systems at large. Examples of application domains include, for instance, traffic management and control, cloud services, communication protocols, and robotic systems. Such an OC system typically consists of a potentially large set of autonomous and self-managed entities, where each entity acts with a local decision horizon. By means of cooperation of the individual entities, the behavior of the entire ensemble system is derived. In this article, we present our work on how autonomous, adaptive robot ensembles can benefit from OC technology. Our elaborations are aligned with the different layers of an observer/controller framework, which provides the foundation for the individuals' adaptivity at system design-level. Relying on an extended Learning Classifier System (XCS) in combination with adequate simulation techniques, this basic system design empowers robot individuals to improve their individual and collaborative performances, e.g., by means of adapting to changing goals and conditions. Not only for the sake of generalizability but also because of its enormous transformative potential, we stage our research in the domain of robot ensembles that are typically comprised of several quad-rotors and that organize themselves to fulfill spatial tasks such as maintenance of building facades or the collaborative search for mobile targets. Our elaborations detail the architectural concept, provide examples of individual self-optimization as well as of the optimization of collaborative efforts, and we show how the user can control the ensembles at multiple levels of abstraction. We conclude with a summary of our approach and an outlook on possible future steps.

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Section: Collaborative Spatial Workmentioning

confidence: 99%

An Organic Computing Approach to Self-Organizing Robot Ensembles

2016

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“…Autonomous cleaning robots are particularly useful in hazardous environments, where the area is inaccessible to humans. Cleaning robots may be classified into several categories based on their special purposes such as autonomous vacuum cleaners [30]- [35], robotic floor scrubbers [36], autonomous pool cleaners [37], liquid storage tank cleaning robots [38], robotic road sweepers [39], [40], bathroom cleaning robots [41], facade cleaning robots [42], waste cleanup robots [43], and window cleaners [44], [45]. Recently, a type of domestic service robots were designed to execute tasks for materials delivery and security surveillance, in addition to floor cleaning in the domestic home environments [46]- [48].…”

Section: A Cleaning Robotsmentioning

confidence: 99%

A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments

Luo

Yang

2008

IEEE Trans. Neural Netw.

192

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Complete coverage navigation (CCN) requires a special type of robot path planning, where the robots should pass every part of the workspace. CCN is an essential issue for cleaning robots and many other robotic applications. When robots work in unknown environments, map building is required for the robots to effectively cover the complete workspace. Real-time concurrent map building and complete coverage robot navigation are desirable for efficient performance in many applications. In this paper, a novel neural-dynamics-based approach is proposed for real-time map building and CCN of autoxnomous mobile robots in a completely unknown environment. The proposed model is compared with a triangular-cell-map-based complete coverage path planning method (Oh et al., 2004) that combines distance transform path planning, wall-following algorithm, and template-based technique. The proposed method does not need any templates, even in unknown environments. A local map composed of square or rectangular cells is created through the neural dynamics during the CCN with limited sensory information. From the measured sensory information, a map of the robot's immediate limited surroundings is dynamically built for the robot navigation. In addition, square and rectangular cell map representations are proposed for real-time map building and CCN. Comparison studies of the proposed approach with the triangular-cell-map-based complete coverage path planning approach show that the proposed method is capable of planning more reasonable and shorter collision-free complete coverage paths in unknown environments.

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“…A new impedance control system has been proposed, and the effectiveness of the force control system to press brushes to the wall with a constant force has been shown through an experiment [20]. SIRIUS is a wall cleaning robot developed by Fraunhofer IFF, which is an industrial company in Germany [21][22][23][24]. SIRIUS realizes up-and-down motion by utilizing a crane installed on buildings for façade maintenance.…”

Section: Introductionmentioning

confidence: 99%

Design and Experiment of a Novel Façade Cleaning Robot with a Biped Mechanism

et al. 2018

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Façade cleaning in high-rise buildings has always been considered a hazardous task when carried out by labor forces. Even though numerous studies have focused on the development of glass façade cleaning systems, the available technologies in this domain are limited and their performances are broadly affected by the frames that connect the glass panels. These frames generally act as a barrier for the glass façade cleaning robots to cross over from one glass panel to another, which leads to a performance degradation in terms of area coverage. We present a new class of façade cleaning robot with a biped mechanism that is able overcome these obstacles to maximize its area coverage. The developed robot uses active suction cups to adhere to glass walls and adopts mechanical linkage to navigate the glass surface to perform cleaning. This research addresses the design challenges in realizing the developed robot. Its control system consists of inverse kinematics, a fifth polynomial interpolation, and sequential control. Experiments were conducted in a real scenario, and the results indicate that the developed robot achieves significantly higher coverage performance by overcoming both negative and positive obstacles in a glass panel.

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Service robots for facade cleaning

Cited by 14 publications

References 2 publications

An Organic Computing Approach to Self-Organizing Robot Ensembles

An Organic Computing Approach to Self-Organizing Robot Ensembles

A Bioinspired Neural Network for Real-Time Concurrent Map Building and Complete Coverage Robot Navigation in Unknown Environments

Design and Experiment of a Novel Façade Cleaning Robot with a Biped Mechanism

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