This letter introduces robotic Augmented Reality for Machine programming by Demonstration (Rampa), the first ML-integrated, XR-driven end-to-end robotic system, allowing training and deployment of ML models such as Pr...
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This letter introduces robotic Augmented Reality for Machine programming by Demonstration (Rampa), the first ML-integrated, XR-driven end-to-end robotic system, allowing training and deployment of ML models such as ProMPs on the fly, and utilizing the capabilities of state-of-the-art and commercially available AR headsets, e.g., Meta Quest 3, to facilitate the application of programming by Demonstration (PbD) approaches on industrial robotic arms, e.g., Universal robots UR10. Our approach enables in-situ data recording, visualization, and fine-tuning of skill demonstrations directly within the user's physical environment. Rampa addresses critical challenges of PbD, such as safety concerns, programming barriers, and the inefficiency of collecting demonstrations on the actual hardware. The performance of our system is evaluated against the traditional method of kinesthetic control in teaching three different robotic manipulation tasks and analyzed with quantitative metrics, measuring task performance and completion time, trajectory smoothness, system usability, user experience, and task load using standardized surveys. Our findings indicate a substantial advancement in how robotic tasks are taught and refined, promising improvements in operational safety, efficiency, and user engagement in robotic programming.
Efficient and natural programming strategies play a crucial role in enabling human -guided robotic assembly to adapt quickly to dynamic tasks. The combination of Augmented Reality (AR) and Digital Twins (DT) has shown...
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Efficient and natural programming strategies play a crucial role in enabling human -guided robotic assembly to adapt quickly to dynamic tasks. The combination of Augmented Reality (AR) and Digital Twins (DT) has shown promising potential in enhancing the intuitiveness of human-robot interaction while leveraging digital representations of human intelligence to empower robots in manufacturing tasks. However, traditional programming methods lack intuitive interaction and rely heavily on simulation environments or pre-set CAD models, leading to high costs for both initial setup and sim-to-real deployment. On the other hand, existing AR -based robot control methods have primarily focused on the basic movements of robots, overlooking higherlevel skills necessary for complex tasks. To address these limitations, this study introduces a four -layer system architecture that integrates AR -assisted DT into skill -based robotic assembly scenarios. Additionally, a skillbased and low -code programming system for human -guided robotic assembly is designed and implemented, which incorporates natural human guidance and robot autonomous intelligence to generate adaptive and feasible action plans. The feasibility and efficiency of the proposed system are verified by two case studies and a quantitative experiment comparing to traditional programming methods. The results demonstrate the usability of our AR -assisted DT approach in improving programming efficiency, intuitiveness, and safety for human -guided robotic assembly while reducing cognitive load.
Bayesian inference provides a probabilistic reasoning process for drawing conclusions based on imprecise and uncertain data that has been successful in many applications within robotics and information processing, but...
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Bayesian inference provides a probabilistic reasoning process for drawing conclusions based on imprecise and uncertain data that has been successful in many applications within robotics and information processing, but is most often considered in terms of data analysis rather than synthesis of behaviours. This paper presents the use of Bayesian inference as a means by which to perform Boolean operations in a logic programme while incorporating and propagating uncertainty information through logic operations by inference. Boolean logic operations are implemented in a Bayesian network of Bernoulli random variables with tensor-based discrete distributions to enable probabilistic hybrid logic programming of a robot. This enables Bayesian inference operations to coexist with Boolean logic in a unified system while retaining the ability to capture uncertainty by means of discrete probability distributions. Using a discrete Bayesian network with both Boolean and Bayesian elements, the proposed methodology is applied to navigate a mobile robot using hybrid Bayesian and Boolean operations to illustrate how this new approach improves robotic performance by inclusion of uncertainty without increasing the number of logic elements required. As any logical system could be programmed in this manner to integrate uncertainty into decision-making, this methodology can benefit a wide range of applications that use discrete or probabilistic logic.
During the past decade, the domain of robotics has evolved immensely where they are being used in almost all aspects of daily life. They are being used in every sector like construction field, automotive industry, agr...
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During the past decade, the domain of robotics has evolved immensely where they are being used in almost all aspects of daily life. They are being used in every sector like construction field, automotive industry, agriculture and many military applications. Jobs like maintaining electricity poles, cleaning sewer pipes, spray painting may have an ill effect on human life and hence, robots are used to do the work for humans. Repetitive tasks such as pick and place on an assembly line, drilling holes in a metal job, welding and painting are now done with the help of robots. The said process of deploying robots in place of human workers eliminates the margin of error that human workers can make, and it is highly time and cost efficient. The objective of the current review is to study robotic system design used over the years and the design procedures carried out by different authors. It covers numerous areas such as use of robots in factories/warehouses, construction sites, medical applications like lower limb rehabilitation and the use of robotic arm in CT scan, robot programming and other several applications.
robot object manipulation in real-world environments is challenging because robot operation must be robust to a range of sensing, estimation, and actuation uncertainties to avoid potentially unsafe and costly mistakes...
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Many industrial tasks—such as sanding, installing fasteners, and wire harnessing—are difficult to automate due to task complexity and variability. We instead investigate deploying robots in an assistive role for the...
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Gradient-based neural dynamics (GND) models are a classical algorithm for solving optimization problems, but it has non-negligible flaws in solving dynamic problems. In this study, a novel GND model, namely the zeroin...
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This paper develops a predefined-time convergent and noise-tolerant fractional-order zeroing neural network (PTC-NT-FOZNN) model, innovatively engineered to tackle time-variant quadratic programming (TVQP) challenges....
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This paper presents an approach to realize a universal robot programming language. Currently, numerous programming languages for industrial robots exit, depending on the manufacturer of the robot. Attempts to create a...
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ISBN:
(纸本)9781728173818;9781728173801
This paper presents an approach to realize a universal robot programming language. Currently, numerous programming languages for industrial robots exit, depending on the manufacturer of the robot. Attempts to create a standardized language have failed in the past. The presence of several new providers of robots on the market increases the number of different programming languages and concepts. For this reason, it would be desirable to have a unified procedure as can be found in the field of programmable logic controllers. Here, a specification is valid, which defines certain types of programming languages. In this contribution, a lightweight robot was connected to a programmable logic controller, where the programming of the robot should be performed. For this purpose, the communication between these two partner devices was achieved using precast data structures. Concerning the programmable logic controller, function blocks were developed to operate the robot and its periphery. They include robot-based functionality, known from common robot programming languages. With regard to the robot, a client program, which interprets and executes the commands from the programmable logic controller, was implemented. Some small robot tasks were performed to show the simplicity of our approach to robot programming based on the languages of programmable logic controllers.
Recent achievements in the field of electronic skin (e-skin) have provided promising technology for service robots. However, the development of a bionic perception system that exhibits superior performance in terms of...
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Recent achievements in the field of electronic skin (e-skin) have provided promising technology for service robots. However, the development of a bionic perception system that exhibits superior performance in terms of safety and interaction quality remains a challenge. Here, we demonstrate a biomimetic soft e-skin that is composed of an array of capacitors and air pouches. It is a single platform that shows dual-mode sensing capabilities of tactile sensing and proximity perception. We optimized the shape and area of the electrode via simulation of the approach of a robot to an object. Moreover, the compliance and temperature of the e-skin can be actively adjusted by tuning the pressure and heat of the air inside the pouches. The e-skin provided dual-mode sensing feedback and soft touch for humanoid service robots, for example, when a robot hugged a man, which illustrated the potential of this e-skin for applications in human-robot interactions. Published under an exclusive license by AIP Publishing.
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