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Identifying relevant stimuli that help generate solutions of desired novelty and quality is challenging in analogical design. To quell this challenge, the multifaceted effects of using stimuli which are located at various analogical distances to the design problem on the novelty and quality of concepts generated using the stimuli are studied in this research. Data from a design project involving 105 student designers, individually generating 226 concepts of spherical rolling robots, are collected. From these data, 138 concepts generated with patents as stimuli and the patents used are analyzed. Analogical distance of a patent is measured in terms of knowledge similarity between technology classes constituting the patent and design problem domain of spherical rolling robots. The key observations are (a) technology classes in closer than farther distances from the design problem are used more frequently to generate concepts, (b) as analogical distance increases the novelty of concepts increases, and (c) as analogical distance decreases the quality of concepts increases.

Abstract:In recent decades, skyscrapers, as represented by the Burj Khalifa in Dubai and Shanghai Tower in Shanghai, have been built due to the improvements of construction technologies. Even in such newfangled skyscrapers, the façades are generally cleaned by humans. Wall climbing robots, which are capable of climbing up vertical surfaces, ceilings and roofs, are expected to replace the manual workforce in façade cleaning works, which is both hazardous and laborious work. Such tasks require these robotic platforms to possess high levels of adaptability and flexibility. This paper presents a detailed review of wall climbing robots categorizing them into six distinct classes based on the adhesive mechanism that they use. This paper concludes by expanding beyond adhesive mechanisms by discussing a set of desirable design attributes of an ideal glass façade cleaning robot towards facilitating targeted future research with clear technical goals and well-defined design trade-off boundaries.

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The role of mobile robots for cleaning and sanitation purposes is increasing worldwide. Disinfection and hygiene are two integral parts of any safe indoor environment, and these factors become more critical in COVID-19-like pandemic situations. Door handles are highly sensitive contact points that are prone to be contamination. Automation of the door-handle cleaning task is not only important for ensuring safety, but also to improve efficiency. This work proposes an AI-enabled framework for automating cleaning tasks through a Human Support Robot (HSR). The overall cleaning process involves mobile base motion, door-handle detection, and control of the HSR manipulator for the completion of the cleaning tasks. The detection part exploits a deep-learning technique to classify the image space, and provides a set of coordinates for the robot. The cooperative control between the spraying and wiping is developed in the Robotic Operating System. The control module uses the information obtained from the detection module to generate a task/operational space for the robot, along with evaluating the desired position to actuate the manipulators. The complete strategy is validated through numerical simulations, and experiments on a Toyota HSR platform.

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