Assistive devices, such as exoskeletons and prostheses, have revolutionized the field of rehabilitation and mobility assistance. Efficiently detecting transitions between different activities, such as walking, stair a...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Assistive devices, such as exoskeletons and prostheses, have revolutionized the field of rehabilitation and mobility assistance. Efficiently detecting transitions between different activities, such as walking, stair ascending and descending, and sitting, is crucial for ensuring adaptive control and enhancing user experience. We present an approach for real-time transition detection, aimed at optimizing the processing-time performance. By establishing activity-specific threshold values through trained machine learning models, we effectively distinguish motion patterns and we identify transition moments between locomotion modes. This threshold-based method improves real-time embedded processing time performance by up to 11 times compared to machine learning approaches. The efficacy of the developed finite-state machine is validated using data collected from three different measurement systems. Moreover, experiments with healthy participants were conducted on an active pelvis orthosis to validate the robustness and reliability of our approach. The proposed algorithm achieved high accuracy in detecting transitions between activities. These promising results show the robustness and reliability of the method, reinforcing its potential for integration into practical applications.
This paper presents a study on using knowledge graph with ChatGP for robotics applications, called KGGPT. Traditional planning methods for robot tasks based on structured data and sequential actions, such as rosplan, ...
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Human-robot collaboration is a critical area of research, aiming to maximize synergy between humans and robots. However, a fundamental question persists: Should robots be seen as mere tools or genuine partners? Existi...
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ISBN:
(数字)9798331529505
ISBN:
(纸本)9798331529512
Human-robot collaboration is a critical area of research, aiming to maximize synergy between humans and robots. However, a fundamental question persists: Should robots be seen as mere tools or genuine partners? Existing experimental environments have limitations in supporting virtual human-robot collaboration studies. In this paper, we propose a novel robot training framework designed for mental-aware human-robot collaboration. Within this framework, we introduce a Trust-Aware Mental Model incorporating a Human Trust component to represent human trust in robots. We create a virtual simulation environment to evaluate this model and conduct human-robot collaboration experiments. Quantitative and qualitative analyses demonstrate that integrating the trust-aware mental model enhances collaboration effectiveness. This finding underscores the necessity of viewing robots as true human partners and provides valuable insights for future research in human-robot collaboration.
Spatio-temporal information is key to resolve occlusion and depth ambiguity in 3D human pose estimation. Previous methods have focused on either temporal contexts or local-to-global architectures that embed fixed-leng...
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ISBN:
(纸本)9781728190778
Spatio-temporal information is key to resolve occlusion and depth ambiguity in 3D human pose estimation. Previous methods have focused on either temporal contexts or local-to-global architectures that embed fixed-length spatiotemporal information. To date, there have not been effective proposals to simultaneously and flexibly capture varying spatiotemporal sequences and effectively achieves real-time 3D human pose estimation. In this work, we improve the learning of kinematic constraints in the human skeleton: posture, local kinematic connections, and symmetry by modeling local and global spatial information via attention mechanisms. To adapt to single- and multi-frame estimation. the dilated temporal model is employed to process varying skeleton sequences. Also, importantly, we carefully design the interleaving of spatial semantics with temporal dependencies to achieve a synergistic effect. To this end, we propose a simple yet effective graph attention spatio-temporal convolutional network (GAST-Net) that comprises of interleaved temporal convolutional and graph attention blocks. Experiments on two challenging benchmark datasets (Human3.6M and HumanEva-I) and YouTuhe videos demonstrate that our approach effectively mitigates depth ambiguity and self-occlusion, generalizes to half upper body estimation, and achieves competitive performance on 2D-to-3D video pose estimation.
Intrinsically elastic robots surpass their rigid counterparts in a range of different characteristics. By temporarily storing potential energy and subsequently converting it to kinetic energy, elastic robots are capab...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Intrinsically elastic robots surpass their rigid counterparts in a range of different characteristics. By temporarily storing potential energy and subsequently converting it to kinetic energy, elastic robots are capable of highly dynamic motions even with limited motor power. However, the time-dependency of this energy storage and release mechanism remains one of the major challenges in controlling elastic robots. A possible remedy is the introduction of locking elements (i.e. clutches and brakes) in the drive train. This gives rise to a new class of robots, so-called clutched-elastic robots (CER), with which it is possible to precisely control the energy-transfer timing. A prevalent challenge in the realm of CERs is the automatic discovery of clutch sequences. Due to complexity, many methods still rely on pre-defined modes. In this paper, we introduce a novel contact-implicit scheme designed to optimize both control input and clutch sequence simultaneously. A penalty in the objective function ensures the prevention of unnecessary clutch transitions. We empirically demonstrate the effectiveness of our proposed method on a double pendulum equipped with two of our newly proposed clutch-based Bi-Stiffness Actuators (BSA).
The prediction of diesel engine power is a vital prerequisite for diesel engine quality promotion. A key issue of diesel engine power prediction is the selection of representative features for forecasting. However, cu...
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ISBN:
(数字)9781665490429
ISBN:
(纸本)9781665490429
The prediction of diesel engine power is a vital prerequisite for diesel engine quality promotion. A key issue of diesel engine power prediction is the selection of representative features for forecasting. However, current feature selection methods mainly rely on correlation analysis which cannot distinguish between direct correlation and indirect correlation. This paper presents a causal feature selection method for diesel engine power forecasting. Causalities distinguish direct influences from indirect ones. Therefore, this paper proposes a diesel engine power prediction framework based on using Markov Blanket-based feature selection approach and Gradient Boosting Decision Tree (GBDT) forecasting model. The proposed framework first applies Markov Blanket to identify causalities between manufacturing variables and diesel engine power and generates a causal feature set. Then, the quantitative relationship between causal features and the diesel engine power is established through GBDT. Finally, the proposed framework is tested by the experiment on a real diesel engine dataset. And the results show that the proposed framework delivers a satisfactory performance advantage for the validation condition in actual applications, the root mean squared error and the coefficient of variation of the root mean squared error of the GBDT model under the validation condition are 2.94kW and 1.17%, respectively.
This study presents a recursive algorithm for solving the regularised least squares problem for online identification of rigid body dynamic model parameters with emphasis on the physical consistency of estimated inert...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
This study presents a recursive algorithm for solving the regularised least squares problem for online identification of rigid body dynamic model parameters with emphasis on the physical consistency of estimated inertial parameters. One of the geometric approaches is to use a regulariser that represents how close the pseudo-inertia matrix is to a given reference on the feasible manifold in the regression problem. The proposed extension enables memory-efficient online learning in addition to the benefits of geometry-aware convex regularisation using the log-determinant divergence of the pseudo-inertia matrix. Also, the recursive version endows the estimator with the capability to deal with time-variation of parameters by introducing an optional forgetting mechanism. The characteristics of the recursive regularised least squares algorithm is demonstrated using the MIT Cheetah 3 leg swinging experiment dataset and compared to the existing batch optimisation method.
As an important part in smart manufacturing under Industry 4.0 era, human-robot collaboration (HRC) features the interaction between human operators and machines, which makes the research of human fatigue come into si...
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ISBN:
(数字)9798331518493
ISBN:
(纸本)9798331518509
As an important part in smart manufacturing under Industry 4.0 era, human-robot collaboration (HRC) features the interaction between human operators and machines, which makes the research of human fatigue come into sight. However, most existing studies on human fatigue or efficiency detection are realized using detectors and models from bioelectronics, whose intrusive detection and decoding of electromyographic signal limits the generality and applicability of such methods. Therefore, this study proposes a human fatigue extraction method based on video data. A new dataset on human manipulation is established by collecting video data of assembly operations to simulate the working status of human operators under smart manufacturing environment. With human skeletal data extracted from the video using a machine learning-based pose extraction tool, MediaPipe, a spatiotemporal analysis for critical skeleton points is implemented for working status categorization and learning using a stochastic gradient descent (SGD) classifier. In this way, the duration taken to complete an assembly task can be extracted as the operation time using the trained SGD classifier, and thus the time-varying operation time series data are obtained to show the trend of human fatigue level. An accuracy of 98.3% is obtained for working status identification for the dataset. Several quantitative indicators like pearson correlation, R-squared value, root mean squared error (RMSE), and Fréchet Distance, are used to evaluate the accuracy of both the extracted operation time and its time-varying curve as compared to the ground truth, with satisfactory results showing the effectiveness of the proposed method.
In this paper, a novel single underwater image enhancement method is proposed to address color distortion, poor contrast and detail blur in underwater degraded images. First, the two attenuation channels of underwater...
In this paper, a novel single underwater image enhancement method is proposed to address color distortion, poor contrast and detail blur in underwater degraded images. First, the two attenuation channels of underwater degraded images are color compensated in the RGB color space. Then, the improved White Patch hypothesis is used to implement color correction. Moreover, contrast stretching and detail preservation of color-corrected images are carried out in the Lab color space. Finally, the two images and the weights defined are fused to obtain the final image at multiple scales. The experiment shows that the proposed method can effectively improve color distortion, enhance contrast and reduce blur in multiple underwater scenes, which is superior to existing methods.
Modeling scene geometry using implicit neural representation has revealed its advantages in accuracy, flexibility, and low memory usage. Previous approaches have demonstrated impressive results using color or depth im...
Modeling scene geometry using implicit neural representation has revealed its advantages in accuracy, flexibility, and low memory usage. Previous approaches have demonstrated impressive results using color or depth images but still have difficulty handling poor light conditions and large-scale scenes. methods taking global point cloud as input require accurate registration and ground truth coordinate labels, which limits their application scenarios. In this paper, we propose a new method that uses sparse LiDAR point clouds and rough odometry to reconstruct fine-grained implicit occupancy field efficiently within a few minutes. We introduce a new loss function that supervises directly in 3D space without 2D rendering, avoiding information loss. We also manage to refine poses of input frames in an end-to-end manner, creating consistent geometry without global point cloud registration. As far as we know, our method is the first to reconstruct implicit scene representation from LiDAR-only input. Experiments on synthetic and real-world datasets, including indoor and outdoor scenes, prove that our method is effective, efficient, and accurate, obtaining comparable results with existing methods using dense input.
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