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Design and preliminary testing of a piezoelectric-based sole energy harvester

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Design and preliminary testing of a piezoelectric-based sole...

IEEE International Conference on robotics and Biomimetics

作者： Yidi Wang Xuan Zhao Qiuyu Shi Haisu Liao Xinyu Wu Fei Gao Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong China

ISBN: (纸本)9781665481106

Harvesting energy from the human body to replenish existing energy sources is receiving substantial attention. Recently scientists have been focusing on harvesting biomechanical energy from human feet during walking. This paper develops a piezoelectric-based sole energy harvester that is designed to be worn on the bottom of shoes to harvest energy, thus powering wearable electronic devices. Specifically, a cam with piezoelectric(PZT) stacks is mounted on the heel to scavenge energy during the heel strike. The cam can change the input's direction from the vertical direction to the horizontal direction. In addition, the output power of this part can be optimized or improved by generating a specified cam profile. A bimorph piezoelectric plate is installed on the forefoot to convert the toes' bending deformation into electricity. Herein, piezoelectric Macro Fiber Composites (MFC) are employed to capture the large deformation. Preliminary testing on a healthy subject is conducted to validate this design. The average power output of the harvester is 8.83 m W.

关键词： Legged locomotion Biomechanics Wearable computers Bending Optical fiber devices Piezoelectric devices Optical fiber testing

Adaptive RISE Control for Dual-Arm Unmanned Aerial Manipulator systems with Deep Neural Networks

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arXiv 2025年

作者： Wang, Yang Yu, Hai Wu, Shizhen Yang, Zhichao Han, Jianda Fang, Yongchun Liang, Xiao Institute of Robotics and Automatic Information System College of Artificial Intelligence Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin300350 China Engineering Research Center of Trusted Behavior Intelligence Ministry of Education Nankai University Tianjin300350 China

The unmanned aerial manipulator system, consisting of a multirotor UAV (unmanned aerial vehicle) and a manipulator, has attracted considerable interest from researchers. Nevertheless, the operation of a dual-arm manipulator poses a dynamic challenge, as the CoM (center of mass) of the system changes with manipulator movement, potentially impacting the multirotor UAV. Additionally, unmodeled effects, parameter uncertainties, and external disturbances can significantly degrade control performance, leading to unforeseen dangers. To tackle these issues, this paper proposes a nonlinear adaptive RISE (robust integral of the sign of the error) controller based on DNN (deep neural network). The first step involves establishing the kinematic and dynamic model of the dual-arm aerial manipulator. Subsequently, the adaptive RISE controller is proposed with a DNN feedforward term to effectively address both internal and external challenges. By employing Lyapunov techniques, the asymptotic convergence of the tracking error signals are guaranteed rigorously. Notably, this paper marks a pioneering effort by presenting the first DNN-based adaptive RISE controller design accompanied by a comprehensive stability analysis. To validate the practicality and robustness of the proposed control approach, several groups of actual hardware experiments are conducted. The results confirm the efficacy of the developed methodology in handling real-world scenarios, thereby offering valuable insights into the performance of the dual-arm aerial manipulator system. Copyright © 2025, The Authors. All rights reserved.

关键词： Lyapunov functions

Initial energy deposition and initiation mechanism of nanosecond laser damage caused by KDP surface micro-defects

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arXiv 2021年

作者： Yang, Hao Chen, Mingjun Cheng, Jian Liu, Zhichao Zhao, Linjie Liu, Qi Tan, Chao Wang, Jian State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Research Center of Laser Fusion China Academy of Engineering Physics Mianyang621900 China

To enable an exploration of the initiation mechanism of nanosecond laser damage on a potassiumdihydrogen phosphate (KDP) surface, a defect-assisted energy deposition model is developed that involves light intensity enhancement and a sub-band gap energy level structure. The simulations provide an explanation on why the laser-induced damage threshold (LIDT) of the KDP crystal is two orders of magnitude lower than the theoretical value. The model is verified by use of the transient images that appear during the laser damage. In addition, the dimensions of the "dangerous" surface defects that are the most sensitive to the laser damage are proposed. This work enables clarification on the initial energy deposition (IED) and initiation mechanism of the nanosecond laser damage caused by the KDP surface defectson micro-nano scale. It is helpful in understanding the laser-matter interactions and to improve the processing technique for high quality optical components. Copyright © 2021, The Authors. All rights reserved.

关键词： Laser damage

Joint Sparse Representations and Coupled Dictionary Learning in Multi-Source Heterogeneous Image Pseudo-color Fusion

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arXiv 2023年

作者： Bai, Long Yao, Shilong Gao, Kun Huang, Yanjun Tang, Ruijie Yan, Hong Meng, Max Q.-H. Ren, Hongliang The Department of Electronic Engineering The Chinese University of Hong Kong 999077 Hong Kong The Department of Electrical Engineering City University of Hong Kong 999077 Hong Kong Shenzhen Key Laboratory of Robotics Perception and Intelligence Shenzhen China The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China The Key Laboratory of Photoelectronic Imaging Technology and System Ministry of Education of China Beijing Institute of Technology Beijing100081 China The Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong 999077 Hong Kong Singapore117575 Singapore Research Institute Suzhou215123 China

Considering that Coupled Dictionary Learning (CDL) method can obtain a reasonable linear mathematical relationship between resource images, we propose a novel CDL-based Synthetic Aperture Radar (SAR) and multispectral pseudo-color fusion method. Firstly, the traditional Brovey transform is employed as a pre-processing method on the paired SAR and multispectral images. Then, CDL is used to capture the correlation between the pre-processed image pairs based on the dictionaries generated from the source images via enforced joint sparse coding. Afterward, the joint sparse representation in the pair of dictionaries is utilized to construct an image mask via calculating the reconstruction errors, and therefore generate the final fusion image. The experimental verification results of the SAR images from the Sentinel-1 satellite and the multispectral images from the Landsat-8 satellite show that the proposed method can achieve superior visual effects, and excellent quantitative performance in terms of spectral distortion, correlation coefficient, MSE, NIQE, BRISQUE, and PIQE. Copyright © 2023, The Authors. All rights reserved.

关键词： Synthetic aperture radar

Featherweight and high-precision closed-loop detection method

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Featherweight and high-precision closed-loop detection metho...

International Conference on Advanced Mechatronic systems (ICAMechS)

作者： Tao Jiang Xianggui Tang Ping Li Linshuai Zhang Shuoxin Gu Lin Xu Qian Wang Enzhi Zhu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Unmanned System Intelligent Perception Control Technology Engineering Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Chengdu School of Automation Chengdu University of Information Technology Sichuan Province Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Automation Chengdu University of Information Technology International Joint Research Center of Robotics and Intelligence System of Sichuan Province Chengdu School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu

One of the key technologies for autonomous driving is localization. Precise localization is inseparable from high-precision maps. Simultaneous Localization And Mapping (SLAM) is technologies for building maps and locating in unknown environments. One of the mainstream directions of current autonomous driving research is how to carry out efficient closed-loop processing over long periods of time, eliminating accumulated error in the odometer. Improving the accuracy of the map and ensuring the accuracy of localization is crucial for ensuring the safety of autonomous driving. Therefore, this paper proposes a featherweight and high-precision method for multi-sensor fusion of laser and GPS for loop detection. The closed-loop results are optimized together with IMU to improve the mapping accuracy.

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Look-to-Touch: A Vision-Enhanced Proximity and Tactile Sensor for Distance and Geometry Perception in Robotic Manipulation

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arXiv 2025年

作者： Dong, Yueshi Ren, Jieji Liu, Zhenle Peng, Zhanxuan Yuan, Zihao Zhang, Ningbin Gu, Guoying State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai China Robotics Institute School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China

Camera-based tactile sensors provide robots with a high-performance tactile sensing approach for environment perception and dexterous manipulation. However, achieving comprehensive environmental perception still requires cooperation with additional sensors, which makes the system bulky and limits its adaptability to unstructured environments. In this work, we present a vision-enhanced camera-based dual-modality sensor, which realizes full-scale distance sensing from 50 cm to -3 mm while simultaneously keeping ultra-high-resolution texture sensing and reconstruction capabilities. Unlike conventional designs with fixed opaque gel layers, our sensor features a partially transparent sliding window, enabling mechanical switching between tactile and visual modes. For each sensing mode, a dynamic distance sensing model and a contact geometry reconstruction model are proposed. Through integration with soft robotic fingers, we systematically evaluate the performance of each mode, as well as in their synergistic operation. Experimental results show robust distance tracking across various speeds, nanometer-scale roughness detection, and sub-millimeter 3D texture reconstruction. The combination of both modalities improves the robot’s efficiency in executing grasping tasks. Furthermore, the embedded mechanical transmission in sensor allows for fine-grained intra-hand adjustments and precise manipulation, unlocking new capabilities for soft robotic hands. Copyright © 2025, The Authors. All rights reserved.

关键词： Robotic arms

Domain generalization for document authentication against practical recapturing attacks

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arXiv 2021年

作者： Chen, Changsheng Zhang, Shuzheng Lan, Fengbo Huang, Jiwu The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Recapturing attack can be employed as a simple but effective anti-forensic tool for digital document images. Inspired by the document inspection process that compares a questioned document against a reference sample, we proposed a document recapture detection scheme by employing Siamese network to compare and extract distinct features in a recapture document image. The proposed algorithm takes advantages of both metric learning and image forensic techniques. Instead of adopting Euclidean distance-based loss function, we integrate the forensic similarity function with a triplet loss and a normalized softmax loss. After training with the proposed triplet selection strategy, the resulting feature embedding clusters the genuine samples near the reference while pushes the recaptured samples apart. In the experiment, we consider practical domain generalization problems, such as the variations in printing/imaging devices, substrates, recapturing channels, and document types. To evaluate the robustness of different approaches, we benchmark some popular off-the-shelf machine learning-based approaches, a state-of-the-art document image detection scheme, and the proposed schemes with different network backbones under various experimental protocols. Experimental results show that the proposed schemes with different network backbones have consistently outperformed the state-of-the-art approaches under different experimental settings. Specifically, under the most challenging scenario in our experiment, i.e., evaluation across different types of documents that produced by different devices, we have achieved less than 5.00% APCER (Attack Presentation Classification Error Rate) and 5.56% BPCER (Bona Fide Presentation Classification Error Rate) by the proposed network with ResNeXt101 backbone at 5% BPCER decision *** Codes 94A08 Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

Deep learning-based forgery attack on document images

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arXiv 2021年

作者： Zhao, Lin Chen, Changsheng Huang, Jiwu the Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

With the ongoing popularization of online services, the digital document images have been used in various applications. Meanwhile, there have emerged some deep learning-based text editing algorithms which alter the textual information of an image in an end-to-end fashion. In this work, we present a low-cost document forgery algorithm by the existing deep learning-based technologies to edit practical document images. To achieve this goal, the limitations of existing text editing algorithms towards complicated characters and complex background are addressed by a set of network design strategies. First, the unnecessary confusion in the supervision data is avoided by disentangling the textual and background information in the source images. Second, to capture the structure of some complicated components, the text skeleton is provided as auxiliary information and the continuity in texture is considered explicitly in the loss function. Third, the forgery traces induced by the text editing operation are mitigated by some post-processing operations which consider the distortions from the print-and-scan channel. Quantitative comparisons of the proposed method and the exiting approach have shown the advantages of our design by reducing the about 2/3 reconstruction error measured in MSE, improving reconstruction quality measured in PSNR and in SSIM by 4 dB and 0.21, respectively. Qualitative experiments have confirmed that the reconstruction results of the proposed method are visually better than the existing approach in both complicated characters and complex texture. More importantly, we have demonstrated the performance of the proposed document forgery algorithm under a practical scenario where an attacker is able to alter the textual information in an identity document using only one sample in the target domain. The forged-and-recaptured samples created by the proposed text editing attack and recapturing operation have successfully fooled some existing document authenticat

关键词： Textures

Fast automatic docking control for electric tractors

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Fundamental Research 2024年

作者： Li, Pengpeng Bai, Yu Li, Weihua Chen, Bowen You, Bindi Zhuang, Hongchao Wang, Jianfeng Gao, Haibo Deng, Zongquan School of Astronautics Harbin Institute of Technology Harbin 150001 China School of Automotive Engineering Harbin Institute of Technology (Weihai) Weihai 264201 China Yangtze River Delta HIT robot technology research institute Wuhu 241060 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin 150001 China Tianjin University of Technology and Education Tianjin 300222 China

In this study, we investigate the automatic docking of an electric tractor with a semi-trailer in narrow and long-distance scenarios. We propose an automatic docking method based on a multi-sensor fusion high-precision positioning algorithm and a multi-strategy docking control algorithm. To solve the heading-angle error of the involved camera due to mechanical errors, a compensation value in the form of angle deviation is introduced. To alleviate the increase in the camera detection error with longitudinal distance, a piecewise coordinate compensation function is proposed. Repositioning is performed using light detection and ranging (Lidar) to improve the pose-perception accuracy to the millimeter level. To avoid the interference of point clouds on the relocation accuracy of Lidar, a search-and-location method that combines both small and large neighborhoods is proposed. To achieve the desired docking success rate, a multi-strategy-adjustment control algorithm is used to enable comprehensive adjustment, including approximate control of the maximum angle based on lateral deviation and fine proportional-integral-differential (PID) control based on lateral deviation and longitudinal distance, security check cycle and premature warning mechanism, and risk avoidance control. Finally, the reliability and stability of the design scheme are verified by conducting multiple experiments involving actual vehicles. © 2024

关键词： Automatic docking Electric tractor Fusion positioning Piecewise proportional-integral-differential