383 research outputs found

    Bionic hand: A brief review

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    The hand is one of the most crucial organs in the human body. Hand loss causes the loss of functionality in daily and work life and psychological disorders for the patients. Hand transplantation is best option to gain most of the hand function. However, the applicability of this option is limited since the side effects and the need for tissue compatibility. Electromechanical hand prosthesis also called bionic hand is an alternative option to hand transplantation. This study presents a quick review of bionic hand technology

    Direct Torque Control of Permanent Magnet Synchronous Motors

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    Examination of Effects of Embedding Formative Assessment in Inquiry-Based Teaching on Conceptual Learning

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    Scaffolding in learning and teacher guidance during inquiry can be attained by formative assessment, which needs to be built into every stage of inquiry. Investigation of the effects of embedded formative assessment in inquiry-based learning on students’ conceptual understanding was the aim of this study. Mixed method experimental research design including quantitative and qualitative data collection methods was used for this study. The participants were 41 students, who were in tenth grade of a suburban public high school. The study reached the following conclusions. First, formative assessment combined with inquiry-based teaching serves as a catalyst for students’ conceptual learning and elevates effects of inquiry. Second, eliciting evidence of learning and feedback may be the primary stages of formative assessment in accelerating student learning and supporting student knowledge development. This study suggests that assessment should be done when teaching continuous and teachers need to adopt formative assessment while performing inquiry-based teaching

    Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces

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    Objects we interact with and manipulate often share similar parts, such as handles, that allow us to transfer our actions flexibly due to their shared functionality. This work addresses the problem of transferring a grasp experience or a demonstration to a novel object that shares shape similarities with objects the robot has previously encountered. Existing approaches for solving this problem are typically restricted to a specific object category or a parametric shape. Our approach, however, can transfer grasps associated with implicit models of local surfaces shared across object categories. Specifically, we employ a single expert grasp demonstration to learn an implicit local surface representation model from a small dataset of object meshes. At inference time, this model is used to transfer grasps to novel objects by identifying the most geometrically similar surfaces to the one on which the expert grasp is demonstrated. Our model is trained entirely in simulation and is evaluated on simulated and real-world objects that are not seen during training. Evaluations indicate that grasp transfer to unseen object categories using this approach can be successfully performed both in simulation and real-world experiments. The simulation results also show that the proposed approach leads to better spatial precision and grasp accuracy compared to a baseline approach.Comment: Accepted by IEEE RAL. 8 pages, 6 figures, 3 table

    Neural Field Movement Primitives for Joint Modelling of Scenes and Motions

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    This paper presents a novel Learning from Demonstration (LfD) method that uses neural fields to learn new skills efficiently and accurately. It achieves this by utilizing a shared embedding to learn both scene and motion representations in a generative way. Our method smoothly maps each expert demonstration to a scene-motion embedding and learns to model them without requiring hand-crafted task parameters or large datasets. It achieves data efficiency by enforcing scene and motion generation to be smooth with respect to changes in the embedding space. At inference time, our method can retrieve scene-motion embeddings using test time optimization, and generate precise motion trajectories for novel scenes. The proposed method is versatile and can employ images, 3D shapes, and any other scene representations that can be modeled using neural fields. Additionally, it can generate both end-effector positions and joint angle-based trajectories. Our method is evaluated on tasks that require accurate motion trajectory generation, where the underlying task parametrization is based on object positions and geometric scene changes. Experimental results demonstrate that the proposed method outperforms the baseline approaches and generalizes to novel scenes. Furthermore, in real-world experiments, we show that our method can successfully model multi-valued trajectories, it is robust to the distractor objects introduced at inference time, and it can generate 6D motions.Comment: Accepted to IROS 2023. 8 pages, 7 figures, 2 tables. Project Page: https://fzaero.github.io/NFMP

    Wireless Power Transfer by Using Magnetically Coupled Resonators

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    In this chapter, a wireless power transmission system based on magnetic resonance coupling circuit was carried out. Mathematical expressions of optimal coupling coefficients were examined with the coupling model. Equivalent circuit parameters were calculated with Maxwell 3D software, and then, the equivalent circuit was solved using MATLAB technical computing software. The transfer efficiency of the system was derived using the electrical parameters of the equivalent circuit. System efficiency was analyzed depending on the different air gap values for various characteristic impedances using PSIM circuit simulation software. Since magnetic resonance coupling involves creating a resonance and transferring the power without the radiation of electromagnetic waves, resonance frequency is a key parameter in system design. The aim of this research was to define the efficiency according to variations of coefficients in wireless power transfer (WPT) system. In order to do that, the calculation procedure of mutual inductance between two self-resonators is performed by Maxwell software. Equivalent circuit is solved in circuit simulator PSIM platform. The calculations show that using the parameters that are obtained by magnetic analysis can be used for the equivalent circuit which has the capability to provide the efficiency using electrical quantities. The chapter discusses the application of this approach to a coil excited by a sinusoidal voltage source and a receiver coil, which receives energy voltage and current. Both could be obtained to calculate the instantaneous power and efficiency. To do so, the waveforms for voltage and current were obtained and computed with the PSIM circuit simulator. As the air gap between the coils increased, the coupling between the coils was weakened. The impedance of the circuit varied as the air gap changed, affecting the power transfer efficiency. In order to determine the differences between the software programs, efficiency values were calculated using three kinds of software. And it is concluded that equivalent circuit analysis by means of numerical computing is proper to obtain the voltage and current waveforms. Correspondingly, transmission efficiency can be calculated using the electrical relations

    Bayesian Optimization-based Nonlinear Adaptive PID Controller Design for Robust Mobile Manipulation

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    In this paper, we propose to use a nonlinear adaptive PID controller to regulate the joint variables of a mobile manipulator. The motion of the mobile base forces undue disturbances on the joint controllers of the manipulator. In designing a conventional PID controller, one should make a trade-off between the performance and agility of the closed-loop system and its stability margins. The proposed nonlinear adaptive PID controller provides a mechanism to relax the need for such a compromise by adapting the gains according to the magnitude of the error without expert tuning. Therefore, we can achieve agile performance for the system while seeing damped overshoot in the output and track the reference as close as possible, even in the presence of external disturbances and uncertainties in the modeling of the system. We have employed a Bayesian optimization approach to choose the parameters of a nonlinear adaptive PID controller to achieve the best performance in tracking the reference input and rejecting disturbances. The results demonstrate that a well-designed nonlinear adaptive PID controller can effectively regulate a mobile manipulator's joint variables while carrying an unspecified heavy load and an abrupt base movement occurs

    Grasp Transfer Based on Self-Aligning Implicit Representations of Local Surfaces

    Get PDF
    Objects we interact with and manipulate often share similar parts, such as handles, that allow us to transfer our actions flexibly due to their shared functionality. This work addresses the problem of transferring a grasp experience or a demonstration to a novel object that shares shape similarities with objects the robot has previously encountered. Existing approaches for solving this problem are typically restricted to a specific object category or a parametric shape. Our approach, however, can transfer grasps associated with implicit models of local surfaces shared across object categories. Specifically, we employ a single expert grasp demonstration to learn an implicit local surface representation model from a small dataset of object meshes. At inference time, this model is used to transfer grasps to novel objects by identifying the most geometrically similar surfaces to the one on which the expert grasp is demonstrated. Our model is trained entirely in simulation and is evaluated on simulated and real-world objects that are not seen during training. Evaluations indicate that grasp transfer to unseen object categories using this approach can be successfully performed both in simulation and real-world experiments. The simulation results also show that the proposed approach leads to better spatial precision and grasp accuracy compared to a baseline approach

    Magnetic equivalent circuit model of surface type fractional-slot permanent magnet synchronous generator

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    Design of permanent magnet synchronous machines becomes more of an issue for all systems lately. There are many parameters that have influence for machine design. Each parameter should have optimized and their effects on the system should be determined. Any desired pre-design have been done for machine design except a few paper and it only showed by of Finite element analysis (FEA). In this article, analytical method is used in permanent magnet synchronous machine design and the effects of geometric on the performance of machine are presented. Magnetic equivalent circuit (MEC) model is used as numeric method. It is observed that the proposed MEC model is pertinent to Speed PC-BDC model and FEA. Besides proposed MEC model provides to calculate performances of the machines which have the desirable slot/pole combinations correctly. Proposed model is applied on the recently increased fractional slot direct drive synchronous generators
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