The increasing interest in cryogenic circuits is driven by their transformative potential across high-performance computing, medical devices, space exploration, and quantum technologies. Operating transistors at cryog...
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This study aimed to develop and evaluate a costeffective Inertial Measurement Unit (IMU) system for gait analysis, comparing its performance with the Vicon system and the VideoPose3D algorithm. The system comprises fi...
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While many advancements have been made in the development of template models for describing upright-trunk locomotion, the majority of the effort has been focused on the stance phase. In this paper, we develop a new co...
Bowden cables serve as essential components in various mechanical systems, facilitating power transmission from remote actuators to specific destinations. The pretension of Bowden cables profoundly influences system p...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Bowden cables serve as essential components in various mechanical systems, facilitating power transmission from remote actuators to specific destinations. The pretension of Bowden cables profoundly influences system performance, notably in terms of friction. This study investigates the effects of cable pretension and shape on friction and torque efficiency. A custom self-designed testbed, comprising integrated actuator units, pulleys, and a novel pretension mechanism connected by Bowden cables, is utilized to conduct experimental tests under varying parameters. This work adopts an integrated approach of experimentation, modeling, and validation, offering preliminary insights into the torque transmission characteristics of tendon driven actuator systems. Additionally, the precise model exhibits excellent conformity across a broad range of shapes and provides initial insights into hysteresis modeling attributable to cable material properties.
The relentless drive for advanced technologies, fueled by the demands of AI and safety-critical applications, has intensified the focus on transistor aginga pivotal concern that undermines both transistor reliability ...
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Artificial intelligence has proven its benefits in many domains. Yet, traditional deep learning models are still too energy and compute-intensive for resource-constrained edge environments. Spiking neural networks (SN...
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Integrating humanoid service mobile robots into human environments presents numerous challenges, primarily concerning the safety of interactions between robots and humans. To address these safety concerns, we propose ...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Integrating humanoid service mobile robots into human environments presents numerous challenges, primarily concerning the safety of interactions between robots and humans. To address these safety concerns, we propose a novel approach that leverages the capabilities of digital twin technology by tailoring it to incorporate comprehensive and robust safety concepts. This paper introduces a "safe-by-design" digital twin that operates alongside the real twin robot in the loop, engaging real-time safety framework during physical interactions with the surrounding environment, including *** validate the effectiveness of our proposed safe-by-design digital twin framework, we conducted experiments using a humanoid service mobile robot alongside simulated human counterparts. Our results demonstrate the capability of the integrated impact safety module within the proposed digital twin approach to limit the velocities of both the robot’s base and arms, adhering to injury biomechanics-based safety thresholds. These findings emphasize the promise of our proposed approach for ensuring the physical safety of humanoid service mobile robots operating in dynamic human environments. It enables the digital twin to preemptively identify potential safety hazards and formulate safe intervention actions to ensure the robot’s compliance with safety regulations, paving the way for safer and more widespread adoption of robotic systems in various service domains.
We present FuncGrasp, a framework that can infer dense yet reliable grasp configurations for unseen objects using one annotated object and single-view RGB-D observation via categorical priors. Unlike previous works th...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
We present FuncGrasp, a framework that can infer dense yet reliable grasp configurations for unseen objects using one annotated object and single-view RGB-D observation via categorical priors. Unlike previous works that only transfer a set of grasp poses, FuncGrasp aims to transfer infinite configurations parameterized by an object-centric continuous grasp function across varying instances. To ease the transfer process, we propose Neural Surface Grasping Fields (NSGF), an effective neural representation defined on the surface to densely encode grasp configurations. Further, we exploit function-to-function transfer using sphere primitives to establish semantically meaningful categorical correspondences, which are learned in an unsupervised fashion without any expert knowledge. We showcase the effectiveness through extensive experiments in both simulators and the real world. Remarkably, our framework significantly outperforms several strong baseline methods in terms of density and reliability for generated grasps.
An in-vivo rabbit model was developed to enable experiments for deciding the safety criteria for skin injuries in human-robot contact. The skin of the human finger was considered as a part vulnerable to harm during hu...
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Learning models or control policies from data has become a powerful tool to improve the performance of uncertain systems. While a strong focus has been placed on increasing the amount and quality of data to improve pe...
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ISBN:
(数字)9798350382655
ISBN:
(纸本)9798350382662
Learning models or control policies from data has become a powerful tool to improve the performance of uncertain systems. While a strong focus has been placed on increasing the amount and quality of data to improve performance, data can never fully eliminate uncertainty, making feedback necessary to ensure stability and performance. We show that the control frequency at which the input is recalculated is a crucial design parameter, yet it has hardly been considered before. We address this gap by combining probabilistic model learning and sampled-data control. We use Gaussian processes (GPs) to learn a continuous-time model and compute a corresponding discrete-time controller. The result is an uncertain sampled-data control system, for which we derive robust stability conditions. We formulate semidefinite programs to compute the minimum control frequency required for stability and to optimize performance. As a result, our approach enables us to study the effect of both control frequency and data on stability and closed-loop performance. We show in numerical simulations of a quadrotor that performance can be improved by increasing either the amount of data or the control frequency, and that we can trade off one for the other. For example, by increasing the control frequency by 33%, we can reduce the number of data points by half while still achieving similar performance.
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