The Kibo robot programming Challenge (Kibo-RPC) is a unique educational program in which students solve various problems by programming free-flying robots (NASA's Astrobee and JAXA's Int-Ball) in the Internati...
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In order to increase the number of human resources with programming thinking, programming education has become indispensable from elementary school students in Japan as well. To utilize communication robots for progra...
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This paper proposes a framework for industrial and collaborative robot programming based on the integration of hand gestures and poses. The framework allows operators to control the robot via both End-Effector (EE) an...
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This paper proposes a framework for industrial and collaborative robot programming based on the integration of hand gestures and poses. The framework allows operators to control the robot via both End-Effector (EE) and joint movements and to transfer compound shapes accurately to the robot. Seventeen hand gestures, which cover the position and orientation controls of the robotic EE and other auxiliary operations, are designed according to cognitive psychology. Gestures are classified by a deep neural network, which is pre-trained for two-hand pose estimation and fine-tuned on a custom dataset, achieving a test accuracy of 99%. The index finger's pointing direction and the hand's orientation are extracted via 3D hand pose estimation to indicate the robotic EE's moving direction and orientation, respectively. The number of stretched fingers is detected via two-hand pose estimation to represent decimal digits for selecting robot joints and inputting numbers. Finally, we integrate these three manners seamlessly to form a programming framework. We conducted two interaction experiments. The reaction time of the proposed hand gestures in indicating randomly given instructions is significantly less than that of other gesture sets, such as American Sign Language (ASL). The accuracy of our method in compound shape reconstruction is much better than that of hand movement trajectory-based methods, and the operating time is comparable with that of teach pendants.
Human-machine interaction (HMI) technologies enable the automation of various manufacturing and assembly applications while maintaining high flexibility. In this context, human-robot collaboration (HRC) capable robots...
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Human-machine interaction (HMI) technologies enable the automation of various manufacturing and assembly applications while maintaining high flexibility. In this context, human-robot collaboration (HRC) capable robots should no longer function as autonomous systems, but much more as assistance systems or as colleagues for workers. In connection with shorter product life cycles, increasing variant diversity and individualization, the challenge arises to set up flexible robot systems, which can be reprogrammed and commissioned with little effort in a short period of time with preservation of the required accuracies [1]. Therefore, intelligent path planning is essential for development of flexible robot systems. In this paper the development of different approaches are presented that allow the worker on the shop floor to rapidly and easily program a robot to implement new motion tasks based on a camera and sensor system without programming knowledge. Thereby various points are selected manually on an image. Then point-controlled processes or even continuous paths can thus be specified and transferred to the robot. A more automated approach based on further image processing methods allows contour detection and finally the generation of process-specific paths. The approaches are not only limited to simple geometries and flat surfaces, but also handle free-form surfaces. To achieve a high flexibility, the setup envisages mounting a 3D camera on the end effector of the robot. The above mentioned approaches will be conceptualized on sealing and riveting applications, which are common in the aircraft production. These application examples have a high demand for flexible robot systems, since CAD data for robot path planning is often not available for aircraft assembly and therefore robot programming has to be done on site. Furthermore, aircraft production is characterized by a high level of manual activities and the dimensions in the aircraft industry require the permanent re
Many HRI researchers have engaged in participatory research to include users in robot design processes. However, to our knowledge, people with mild cognitive impairment (PwMCI) and early stage dementia have yet to be ...
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ISBN:
(数字)9798350378931
ISBN:
(纸本)9798350378948
Many HRI researchers have engaged in participatory research to include users in robot design processes. However, to our knowledge, people with mild cognitive impairment (PwMCI) and early stage dementia have yet to be included in developing and programmingrobots, and the HRI community lacks tools to facilitate their inclusion. We bridge this gap by introducing PODER (programming framework to Develop robot behaviors), which enables a lived technology experience for PwMCI via scaffolding, peer programming, and development tools to support them as key developers of social robots. We conducted a study where PwMCI and early stage dementia used PODER to program robot interactions, and found that participants were highly engaged and deeply enjoyed their experience, creating programs for robots that reflected their interests, experiences, and needs. Our results show the impact of including participants with MCI and early stage dementia in robot programming, including an increased understanding of technology, shifting their perceived role from technology users to programmers, and desire to be involved with the end-to-end process. By releasing PODER to the community, we hope this work can facilitate the intentional inclusion of people with cognitive impairments in further HRI research.
Augmented Reality (AR)-based programming approaches hold great promise for addressing the challenges of flexible automation by facilitating fast and intuitive programming processes. Pose estimation of novel objects en...
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ISBN:
(数字)9798350378931
ISBN:
(纸本)9798350378948
Augmented Reality (AR)-based programming approaches hold great promise for addressing the challenges of flexible automation by facilitating fast and intuitive programming processes. Pose estimation of novel objects enhances the program-ming experience by bridging the real and virtual environments. However, a prerequisite for pose estimation is to perform a 2D segmentation to determine the region of interest (ROI). In this work, we present an AR-based approach that enables point-and-click ROI detection through human interaction. Our proof of concept investigates how the achievable accuracy varies with the quality of the user input. The results show that the accuracy of the ROI estimation has a minimal impact on the overall accuracy. Existing limitations can be addressed by other approaches presented.
In the artificial intelligence age, cultivating young children's computational thinking (CT) has sparked tremendous attention. Programmable robotics is a developmental-appropriate and screen-free means that provid...
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Conventional robot programming methods are complex and time-consuming for users. In recent years, alternative approaches such as mixed reality have been explored to address these challenges and optimize robot programm...
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Perceived social agency-the perception of a robot as an autonomous and intelligent social other-is important for fostering meaningful and engaging human-robot interactions. While end-user programming (EUP) enables use...
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ISBN:
(数字)9798350378931
ISBN:
(纸本)9798350378948
Perceived social agency-the perception of a robot as an autonomous and intelligent social other-is important for fostering meaningful and engaging human-robot interactions. While end-user programming (EUP) enables users to customize robot behavior, enhancing usability and acceptance, it can also potentially undermine the robot's perceived social agency. This study explores the trade-offs between user control over robot behavior and preserving the robot's perceived social agency, and how these factors jointly impact user experience. We conducted a between-subjects study (N = 57) where participants customized the robot's behavior using either a High-Granularity Interface with detailed block-based programming, a Low-Granularity Interface with broader input-form customizations, or no EUP at all. Results show that while both EUP interfaces improved alignment with user preferences, the Low-Granularity Interface better preserved the robot's perceived social agency and led to a more engaging interaction. These findings highlight the need to balance user control with perceived social agency, suggesting that moderate customization without excessive granularity may enhance the overall satisfaction and acceptance of robot products.
Industrial robot programming necessitates specialized expertise and significant time commitment, particularly for small-batch productions. In response to the escalating demand for production agility, novel approaches ...
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ISBN:
(数字)9798350375022
ISBN:
(纸本)9798350375039
Industrial robot programming necessitates specialized expertise and significant time commitment, particularly for small-batch productions. In response to the escalating demand for production agility, novel approaches have emerged in intuitive robot programming. These inventive systems, rooted in diverse conceptual frameworks, are designed to expedite the deployment of robot systems. A prominent innovation in this domain is adopting no-code robot programming through finger-based gestures. A robot program can be generated by capturing and tracking non-expert users’ finger movements and gestures, converting 3D coordinates into an executable robot programming language. However, accurately determining finger positions for 3D coordinates and precise geometrical features presents an ongoing challenge. In pursuit of heightened trajectory precision and reducing more significant effort for the users, we propose a hybrid methodology that amalgamates finger-gesture programming with point cloud data. This synergistic integration demonstrates promising outcomes, substantiating its potential to facilitate the precise and adaptive generation of robot paths within robot applications.
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