Industrial robots have been programmed to optimize mass production by executing predefined trajectories with their high speed, precision, and repeatability over long production cycles. However, the rise of Industry 4....
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Industrial robots have been programmed to optimize mass production by executing predefined trajectories with their high speed, precision, and repeatability over long production cycles. However, the rise of Industry 4.0 and the increasing demand for diverse product varieties in small quantities have sparked the need for new programming methodologies that enable rapid reprogramming and short production cycles. Recently, augmented reality (AR)-based robot programming has gained attention for overlaying virtual images into real working environments, making programming intuitive and fast. However, the discomfort from prolonged use of AR-handheld devices, as well as the accuracy and latency issues associated with vision-based hand tracking, limit their usability, and impede effective human-robot cooperation. To overcome these limitations, this paper introduces an augmented reality (AR)-based wearable robot programming system using a haptic glove, allowing users to intuitively program the robot in a handheld-free and wearable manner. The use of a haptic glove enables accurate measurements of finger joint angles and handheld-free haptic feedback, eliminating discomfort and occlusion problem. The system leverages measured hand posture and finger joint angles, combined with voice commands, to enable the simultaneous input of various types of commands for intuitive programming. A practical human-robot collaboration scenario demonstration with a 6-degrees of freedom (DOF) hydraulic manipulator to disassemble a ladder for repair shows the programming flexibility of our system. The proposed system addresses the evolving trends of the manufacturing industry by enhancing programming flexibility and intuitiveness, as well as promoting seamless cooperation between humans and robots.
programming skills and mathematical thinking are among the important skills in the field of information technology. It is common knowledge that programming often involves topics such as loops, variables, functions and...
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programming skills and mathematical thinking are among the important skills in the field of information technology. It is common knowledge that programming often involves topics such as loops, variables, functions and mathematical expressions. Students who are beginners in programming or who learn text-based programming are prone to assuming a negative attitude towards their programming education. The aim of this study is to examine the effect of using educational robots on students' mathematical achievement, mathematics anxiety and computer programming self-efficacy perception. 117 secondary school 6th grade students participated in the study, which was conducted in a quasi-experimental design. To compare the variables between the two groups in this study, an independent t-test was used. To analyze the corrected variance differences between the groups, a linear covariance (ANCOVA) analysis was done. At the end of the study, significant differences between the groups in terms of mathematics anxiety and programming self-efficacy perception were noted. In terms of mathematical achievement, however, although it increased both in the experimental group and the control group, no significant differences were found. When the mathematics anxiety pre-test score was controlled, ANCOVA analysis revealed that there was a significant difference in the mathematics anxiety Posttest. This study demonstrated that robots can be an effective tool for positive change in mathematics anxiety and self-efficacy perceptions in computer programming. However, although educational robots provided a positive change in math achievement, they did not provide a significant difference between the groups. At this point, long-term studies examining mathematics achievement with educational robots are needed.
The market of collaborative robots is thriving due to their increasing affordability. The ability to program a collaborative robot without requiring a highly skilled specialist would increase their spread even more. V...
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The market of collaborative robots is thriving due to their increasing affordability. The ability to program a collaborative robot without requiring a highly skilled specialist would increase their spread even more. Visual programming is a prevalent contemporary approach for end-users on desktops or handheld devices, allowing them to define the program logic quickly and easily. However, separating the interface from the robot's task space makes defining spatial features difficult. At the same time, augmented reality can provide spatially situated interaction, which would solve the issue and allow end-users to intuitively program, adapt, and comprehend robotic programs that are inherently highly spatially linked to the real environment. Therefore, we have proposed Spatially Anchored Actions to address the problem of comprehension, programming, and adaptation of robotic programs by end-users, which is a form of visual programming in augmented reality. It uses semantic annotation of the environment and robot hand teaching to define spatially important points precisely. Individual program steps are created by attaching parametrizable, high -level actions to the points. Program flow is then defined by visually connecting individual actions. The interface is specifically designed for tablets, which provide a more immersive experience than phones and are more affordable and wellknown by users than head-mounted displays. The realized prototype of a handheld AR user interface was compared against a commercially available desktop-based visual programming solution in a user study with 12 participants. According to the results, the novel interface significantly improves comprehension of pick and place-like programs, improves spatial information settings, and is more preferred by users than the existing tool.
BackgroundIn the digital age, fostering young children's computational thinking (CT) and executive functions (EFs) through programming has emerged as a significant research issue. While unplugged programming activ...
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BackgroundIn the digital age, fostering young children's computational thinking (CT) and executive functions (EFs) through programming has emerged as a significant research issue. While unplugged programming activities are commonly adopted in preschools, robot programming activities have recently gained attention for the potential to enhance both CT and EFs. Preschoolers are at a pivotal stage for developing CT and EFs. However, there is a dearth of empirical evidence comparing robot programming and unplugged programming activities on preschoolers' CT and EFs development. Therefore, the current research designed a randomized controlled trial to compare the impact of robot programming and unplugged programming activities on 198 5- to 6 year-old preschoolers' CT and EFs (including inhibition, working memory, and cognitive flexibility). Children were randomly allocated to either the robot programming group, the unplugged programming group, or the business-as-usual control *** a 12-week intervention, results revealed that: (1) the robot programming and unplugged programming groups both outperformed the conventional kindergarten group in CT, with the robot programming group having superior effects in CT over time;(2) the robot programming group outperformed the unplugged programming and conventional kindergarten group on inhibition, working memory, and cognitive flexibility of EFs over time;and (3) most preschoolers in the robot programming group had positive perceptions of programmable *** present research demonstrated that robot programming had a more significant and sustained impact on preschoolers' CT and EFs than unplugged programming and conventional kindergarten activities. Accordingly, these findings offered valuable implications for introducing effective programming activities to develop preschoolers' CT and EFs.
Programmable robotics is recently used in early childhood education (ECE) to introduce programming and computational thinking (CT) skills. However, there is a further need for research to contrast the efficacy of chil...
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Programmable robotics is recently used in early childhood education (ECE) to introduce programming and computational thinking (CT) skills. However, there is a further need for research to contrast the efficacy of children's participation in robot programming and traditionally beneficial ECE activities. The present study thus investigated the effects of a robot programming intervention versus a block play program on kindergarteners' CT, sequencing ability, and self-regulation. The experiment (robot programming) versus comparison (block play) condition was randomly assigned to four kindergarten classes, which included 101 kindergarteners (M = 64.78 months, SD = 7.64). Statistical analyses revealed that the robot programming group (N = 54) had experienced greater gains over time in sequencing ability relative to those in the block play group (N = 47;F = 5.09, p < 0.05). Children in the robot programming group with lower level of self-regulation at baseline showed larger improvements in sequencing ability over time relative to the block play group (F = 2.37, p = 0.01). Also, children in the robot programming group with older age showed larger improvements in CT over time relative to the block play group (F = 2.40, p < 0.01). The study demonstrates the positive benefits of robot programming to early childhood development in terms of CT and sequencing ability, compared to a traditional curriculum activity in ECE-block play.
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:
(纸本)9798350375039;9798350375022
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.
Despite the importance given to providing students with programming skills, there are significant problems in teaching programming, mainly at the entry level. Various programming education tools and methods have been ...
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Despite the importance given to providing students with programming skills, there are significant problems in teaching programming, mainly at the entry level. Various programming education tools and methods have been developed to solve these problems in introductory programming education. For this reason, block-based and robot programming tools are widely used at various education levels, especially at the novice level. Although the advantages of using block-based and robot programming over classical or text-based programming methods in introductory programming education have been demonstrated in various studies, the two methods have not been compared in experimental studies. The main purpose of this study was to compare the effects of using block-based programming and robot programming methods in introductory programming education on students' perceptions of programming self-efficacy. The results of the study indicated that the perceptions of programming self-efficacy of the students who used the robot programming method in programming education were significantly more positive than those of the students who used the block-based programming method. Moreover, the programming self-efficacy perceptions of the male students who used robot programming in introductory programming education were significantly more positive than those of the female students. After the robot programming activities, the students had more fun with programming and their self-confidence increased.
Industrial robots are widely used in industrial production as mechanical devices. It is essential to guarantee that their control software operates safely and properly, as any functional or security-related defects ma...
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ISBN:
(纸本)9798400704208
Industrial robots are widely used in industrial production as mechanical devices. It is essential to guarantee that their control software operates safely and properly, as any functional or security-related defects may lead to serious incidents. However, industrial robots are programmed mostly in proprietary languages varying from vendor to vendor, making it challenging to formally analyze their correctness in a unified way. One of the most representative robot programming languages is the RAPID language proposed by ABB robotics. In this paper, we present K-RAPID, a formal executable semantics of RAPID in the K-Framework (K). K-RAPID is developed according to the official ABB documentation and defined in a generic extensible manner. It can be used either for validating the correctness of compiler implementation or analyzing the control programs written in RAPID. We evaluate the correctness of K-RAPID by executing 563 test programs collected from multiple sources and comparing the results against the official robot simulation environment robotStudio. The results suggest that K-RAPID covers the core features of RAPID correctly. Moreover, we show how we could apply K-RAPID to verify RAPID programs using LTL model checking and to provide a formal specification of RAPID to uncover inappropriate behaviors in the programs.
Industrial robots are applied in a widening range of industries, but robot programming mostly remains a task limited to programming experts. We propose a natural language-based assistant for programming of advanced, i...
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Industrial robots are applied in a widening range of industries, but robot programming mostly remains a task limited to programming experts. We propose a natural language-based assistant for programming of advanced, industrial robotic applications and investigate strategies for domain-specific fine-tuning of foundation models with limited data and compute.
programming by Demonstration (PbD) is an intuitive technique for programmingrobot manipulation skills by demonstrating the desired behavior. However, most existing approaches either require extensive demonstrations o...
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ISBN:
(纸本)9798350377712;9798350377705
programming by Demonstration (PbD) is an intuitive technique for programmingrobot manipulation skills by demonstrating the desired behavior. However, most existing approaches either require extensive demonstrations or fail to generalize beyond their initial demonstration conditions. We introduce Diffusion-PbD, a novel approach to PbD that enables users to synthesize generalizable robot manipulation skills from a single demonstration by utilizing the representations captured by pre-trained visual foundation models. At demonstration time, hand and object detection priors are used to extract waypoints from the human demonstrations anchored to reference points in the scene. At execution time, features from pretrained diffusion models are leveraged to identify corresponding reference points in new observations. We validate this approach through a series of real-world robot experiments, showing that Diffusion-PbD is applicable to a wide range of manipulation tasks and has strong ability to generalize to unseen objects, camera viewpoints, and scenes. Code and supplementary videos can be found at https://diffusion- ***
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