作者:
Du, ChaofengGuo, JianGuo, ShuxiangFu, QiangTianjin University of Technology
Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory BinshuiXidao Extension 391 Tianjin300384 China Shenzhen Institute of Advanced Biomedical Robot Co.
Ltd. No.12 Ganli Sixth Road Jihua Street Longgang District Shenzhen518100 China School of Life Science
Beijing Institute of Technology Key Laboratory of Convergence Medical Engineering System and Healthcare Technology The Ministry of Industry and Information Technology No.5 Zhongguancun South Street Beijing100081 China
The amphibious robot needs to accurately estimate the 6D pose of the target in tasks such as target tracking, docking with the recovery module, and target grasping. The current research on target 6D pose estimation is...
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A brain-computer interface (BCI) enables direct communication between the human brain and external devices. Electroencephalography (EEG) based BCIs are currently the most popular for able-bodied users. To increase use...
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具有低温升的高效散热方案是实现电泵浦激光的关键因素之一.高导热金刚石有望克服钙钛矿激光的散热限制.本文中,我们展示了一种可以将光泵浦过程中产生热量高效耗散的金刚石基底钙钛矿纳米片激光.此外,紧密光学束缚可以通过在纳米片和金刚石基底之间引入一层薄SiO_(2)间隔层而实现.该激光的品质因子高达~1962,激光阈值为52.19μJ cm^(-2).受益于金刚石基底,其泵浦能量密度相关温度灵敏度较低(~0.56±0.01 K cm~2μJ^(-1)).本工作有望促进电泵浦钙钛矿激光的发展.
In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To ...
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In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S $^{\text{4}}$ TP) framework. Specifically, S $^{\text{4}}$ TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV's planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S $^{\text{4}}$ TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs' trajectories as input. With the integration of SADRF, S $^{\text{4}}$ TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S $^{\text{4}}$ TP in terms of safety and rationality. S $^{\text{4}}$ TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%.
In this paper, we present a trajectory generation method of a quadrotor, based on the optimal smoothing B-spline, for tracking a moving target with consideration of relative tracking pattern or limited field of view o...
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In this paper, we present a trajectory generation method of a quadrotor, based on the optimal smoothing B-spline, for tracking a moving target with consideration of relative tracking pattern or limited field of view of the onboard sensor in cluttered environments. Compared to existing methods, safe flying zone,vehicle physical limits, and smoothness are fully considered to guarantee flight safety, kinodynamic feasibility, and tracking performance. To tackle the cluttered environments, a parallel particle swarm optimization algorithm is applied to find the feasible waypoints that the generated trajectory should be as close to as possible, with consideration of the target's future state as well as obstacles to trade off the tracking performance and flight safety. Then, a sequential motion planning method, considering the above constraints, is applied and embedded into a cost function for solving the problem of robust tracking trajectory generation for the quadrotor via a convex optimization approach. The feasibility and effectiveness of the proposed method are verified by numerical simulations.
Micro-assembly is an emerging method to fabricate microrobots with multiple modules or particles. However, there is always a lack of a flexible and efficient method to freely create the desired magnetic soft microrobo...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
Micro-assembly is an emerging method to fabricate microrobots with multiple modules or particles. However, there is always a lack of a flexible and efficient method to freely create the desired magnetic soft microrobots. In this paper, an automated assembly system based on a two-fingered microhand is presented for fabricating magnetic soft microrobots. Our proposed system can automatically pick and place components to assemble microrobots with a two-fingered micromanipulator, and orient these components through an external magnetic field. The automated assembly has the advantages of high accuracy, high speed, and high success rate. It can endow magnetic microrobots with flexible material selection, arbitrary geometry design, and programable magnetization profile. We can make full use of this system to fabricate multiple magnetic soft microrobots. The experiment results demonstrate that this system can efficiently fabricate microrobots with excellent mechanical properties, which have application potential in robotics, biomedical engineering, and environmental governance.
The increase in frequency and intensity of Extreme High-temperature Events(EHEs)over Central-Eastern China(CEC)in recent decades has severely impacted social development and *** observation and reanalysis datasets,thi...
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The increase in frequency and intensity of Extreme High-temperature Events(EHEs)over Central-Eastern China(CEC)in recent decades has severely impacted social development and *** observation and reanalysis datasets,this study explores the effect of the East Asian subtropical westerly jet stream(EAJ)on the CEC EHEs for the summers spanning 1979–*** its general relative location to the right side of the upper-level jet stream exit region,CEC would theoretically suffer more EHEs with a stronger and northwardly-shifted EAJ in summer due to the likelihood of abnormal subsidence induced by the ***,such an EAJ–EHE connection has been unstable over the past four decades but has displayed an evident interdecadal *** the late 1990s,the interannual variation of the EAJ was manifested mainly by its meridional displacement in the northeastern part of East Asia;thus,the atmospheric responses were essentially located to the east of CEC,exerting less of an influence on the CEC ***,since the late 1990s,the EAJ variation has featured an intensity change in its center over the northwest portion of the CEC,which has resulted in a westward shift in atmospheric responses to cover the CEC ***,the EAJ could potentially affect the summer CEC EHEs during 2000–*** findings offer support for an in-depth understanding of the formation mechanisms of extreme weather/climate events of this nature and thus provide a scientific reference for seasonal climate predictions.
The complexity and diversity of smart grid systems increase the likelihood of anomalies in communications between devices in the system, and how these anomalies are detected is critical to the security of the grid sys...
The complexity and diversity of smart grid systems increase the likelihood of anomalies in communications between devices in the system, and how these anomalies are detected is critical to the security of the grid system. However, the distribution of traffic types in the grid is often highly unbalanced, with the total amount of normal traffic data often far outweighing the anomalous data. This imbalance compromises the effectiveness of the detection model and poses a significant threat to the overall security of the grid. This paper improves on traditional conditional generation adversarial neural networks and designs a new approach to overcome this obstacle and enhance the security of smart grid systems. In the generator part, the spatial features of the data are captured by convolutional neural networks, and the temporal features of the data are captured by gated recurrent units. By integrating these two components into the generative network, we can generate synthetic data with both temporal and spatial features, thus enhancing the minority class samples. By incorporating the augmented minority data into the training process, the proposed approach enables the detection model to understand the distribution of features of the minority anomaly samples more accurately. This improved learning accuracy translates into improved model performance, resulting in higher detection rates for previously under-represented anomalies. In addition, our approach effectively addresses the inherent bias towards traffic types with higher flows typically observed in asymmetric datasets, thus providing a more equitable and comprehensive security solution for smart grid systems.
Because industrial robots have uneven positioning accuracy in the overall working space, they have greatly limited their applications in the fields of high-precision production and *** this status quo, this article fo...
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A phase unwrapping method based on constant false alarm rate (CF AR) detection is proposed for multi-baseline interferometric inverse synthetic aperture radar (InISAR). Aiming at the problem of ambiguity number estima...
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ISBN:
(纸本)9781665468893
A phase unwrapping method based on constant false alarm rate (CF AR) detection is proposed for multi-baseline interferometric inverse synthetic aperture radar (InISAR). Aiming at the problem of ambiguity number estimation error under the condition of phase noise, Chinese remainder theorem (CRT) and cluster analysis are utilized to acquire the probability distribution of phase noise. By introducing CFAR detection, the novel method reduced the error probability of ambiguity number estimation. Through simulation, it is verified that the novel method can effectively suppress phase noise and obtain accurate phase unwrapping results.
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