In this research, the combination of hyperspectral technology and chemometrics was applied for non-destructive testing of the internal quality of Hami melon. Sugar content and firmness serve as crucial indicators in a...
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A novel detection method is to use unmanned aerial vehicles for the detection of high-altitude passenger *** paper posed a challenging problem for detection system modeling and *** article proposed a method of detecti...
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Segmenting teeth in 2D oral CT images is crucial for diagnosing dental conditions. However, many current models for 2D tooth segmentation depend solely on end-to-end segmentation loss during training, which can fail t...
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Grading of onion is important for the purpose of quality as well as market value, and it has, in the past, used traditional methods. Onion grading has thus been automated, which has been a focus of numerous researcher...
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Technological advancements in the automotive industry are currently focused on autonomous driving systems or driver assistance systems. Depth estimation is also an important feature of the autonomous driving system as...
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To achieve real-time dynamic simulation analysis and optimization design, a dynamic digital twin of a nonholonomic mobile manipulator (one UR5e mounted on an industrial mobile robot MIR 200) has been developed in this...
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ISBN:
(纸本)9781728196817
To achieve real-time dynamic simulation analysis and optimization design, a dynamic digital twin of a nonholonomic mobile manipulator (one UR5e mounted on an industrial mobile robot MIR 200) has been developed in this paper. First, the digital twin integrated with dynamics of a mobile manipulator is established. The framework of the dynamic digital twin is presented in detail. Then, the dynamic model of the system has been established with the consideration of the physical interaction between the robot and humans/environments using Lagrange formulation. Finally, the experimental testing has been conducted to validate the dynamic model and evaluate the performances (such as real-time property, accuracy, etc.) of the dynamic digital twin that is integrated with the physical human/environment-robot interaction.
Modular and truss robots offer the potential of high reconfigurability and great functional flexibility, but common implementations relying on rigid components often lead to highly complex actuation and control requir...
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ISBN:
(纸本)9781728196817
Modular and truss robots offer the potential of high reconfigurability and great functional flexibility, but common implementations relying on rigid components often lead to highly complex actuation and control requirements. This paper introduces a new type of modular, compliant robot: TrussBot. TrussBot is composed of 3D-printed tetrahedral modules connected at the corners with compliant joints. We propose a truss geometry, analyze its deformation modes, and provide a simulation framework for predicting its behavior under applied loads and actuation. The TrussBot is geometrically constrained, thus requiring compliant joints to move. The TrussBot can be actuated through a network of tendons which pinch vertices together and apply a twisting motion due to the structure's connectivity. The truss was demonstrated in a physical prototype and compared to simulation results.
Remote controlling robots without any automated help is difficult due to various limitations. Autocomplete mitigates this difficulty by automatically detecting and completing the intended motions on robots from the in...
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ISBN:
(纸本)9781728196817
Remote controlling robots without any automated help is difficult due to various limitations. Autocomplete mitigates this difficulty by automatically detecting and completing the intended motions on robots from the input of the user. Such an approach can improve the system performance and reduce the load on the operator. Usually, recognizing intended motions is achieved using pre-trained Deep Learning (DL) models. In this paper, we introduce personalization to the autocomplete teleoperation framework when new operators take over by customizing the autocomplete DL model using incremental learning. Also, we tackle the problem of concept drift that arises in real-life applications;the data distribution of already learned classes may change in unforeseen ways as new observations of these classes come sequentially over time. We create and update an exemplar set using new observations of the classes online so that the model can be trained to adapt to the new observations. Several scenarios have been evaluated to balance the speed of learning with the accuracy of the model, and results demonstrate the effectiveness of the proposed models and their advantage in adapting to the specific operator versus our previous framework: personalization using transfer learning with full feedback.
Electric vehicle (EV) charging in complex environments presents significant challenges, precise and adaptable interaction between robotic systems and vehicle charging plugs. This paper introduces a novel approach util...
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Autonomous systems are heavily used in many safety-critical systems, such as industrial automation, autonomous cars, Industrial Internet of Things (I-IoT), etc. Verification of the functional and temporal correctness ...
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ISBN:
(纸本)9798350378030;9798350378023
Autonomous systems are heavily used in many safety-critical systems, such as industrial automation, autonomous cars, Industrial Internet of Things (I-IoT), etc. Verification of the functional and temporal correctness of such systems is necessary before deployment to ensure their safety. However, due to the presence of physical systems in the continuous-time domain and computational models in the discrete-time domain, end-to-end verification of these systems is highly challenging. Existing formal methods focus on verifying physical models assuming static or simplified computation models. In contrast, existing real-time systems focus on satisfying strict timing bounds but do not care how those bounds are obtained and how they relate to physical safety. Our approach bridges these two domains, and constitutes an end-to-end verification framework for arbitrary physical models and computational models incorporated within a cyber-physical automated system. By allowing the interaction between the computational and physical models, our verification framework enables a fine-grained scheme that verifies against the local environment instead of verifying against global worst-case assumptions. Moreover, to support locally varying worst-case scenarios, a mixed-criticality system is proposed where the system supports several critical models and switches among the modes based on environmental uncertainty. Finally, a proof-of-concept evaluation of the proposed framework is reported.
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