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检索条件"主题词=Robotics and automation"

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Targetless Extrinsic Calibration for LiDAR and Camera Systems with Intensity Information

Targetless Extrinsic Calibration for LiDAR and Camera System...

引用

IEEE International Conference on Mechatronics, robotics and automation (ICMRA)

作者： Weiyang Jiang Yanduo Zhang Tao Lu Yunxiao Hu Jiaming Wang Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan China Guangyu Jingye(Wuhan) Intelligent Technology Co. Ltd Wuhan China Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

Accurate and efficient external calibration, available in real-time within a targetless environment, remains a significant challenge due to the inherent inhomogeneous reflectance, density distribution, and small FOV of Non-Repeat Scanning (NRS) LiDAR system data, these issues bring difficulties for subsequent applications in areas such as simultaneously localization and mapping (SLAM) and autonomous driving. To address these issues, this paper proposes a targetless method for the automatic calibration of LiDAR and camera systems. Initially, we analyzed the complexity and variability of LiDAR point cloud information and developed a feature extraction method that integrates geometric and intensity data. Subsequently, we optimized the intensity data within the point cloud to extract reliable intensity information. Finally, we matched and iteratively optimized the sensor system data to obtain the final extrinsic parameters. Through extensive experimental analysis, we verified the effectiveness and accuracy of our algorithm.

关键词： Point cloud compression Laser radar Accuracy Simultaneous localization and mapping Robot vision systems Feature extraction Cameras Sensor systems Calibration robotics and automation

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Output Feedback Control for High-Order Nonlinear Systems With Practical Predefined-Time Convergence

Output Feedback Control for High-Order Nonlinear Systems Wit...

引用

Advanced Information, Mechanical Engineering, robotics and automation (AIMERA), IEEE International Conference on

作者： Chen Wang Yani Wang An-Min Zou State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China College of Engineering Shantou University Shantou China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

The practical predefined-time (PPT) output feedback control issue of higher-order nonlinear systems is investigated in this paper. First, a state observer was designed to ensure the observer error to be PPT stable via the time-varying function method. Then, with the derived observer, a PPT output feedback controller is derived. Through the Lyapunov technique, it is demonstrated that the tracking error can reach a small region around zero in predefined time with unavailability of full-state measurements. Finally, the performance of the proposed controller is shown by a numerical example.

关键词： Protocols Observers Numerical simulation Time measurement Stability analysis Nonlinear systems robotics and automation Output feedback Time-varying systems Numerical stability

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17-Point Algorithm Revisited: Toward a More Accurate Way

17-Point Algorithm Revisited: Toward a More Accurate Way

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Chen Xie Rui Xing Ning Hao Fenghua He Harbin Institute of Technology Harbin China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

17-point algorithm is a popular method in relative pose estimation of multi-cameras. However, the role of overlap in 17-point algorithm remains unexplored. And the relaxed way in solving constrained normal equation leads to sub-optimal results. Both of them influence accuracy of the estimated pose. In this paper, we theoretically analyze the influence of overlap and the solvability of 17-point algorithm. In addition, we show that the abuse of overlap can harm accuracy in practice. In light of these findings, we propose an improved 17-point algorithm, which avoids using overlaps and derives a simple way to solve normal equation on manifold. Both simulations and real world data experiments demonstrate the proposed one outperforms the traditional 17-point algorithm in term of accuracy.

关键词： Manifolds Accuracy Pose estimation Cameras Mathematical models Data models robotics and automation

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GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy

GenDOM: Generalizable One-shot Deformable Object Manipulatio...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： So Kuroki Jiaxian Guo Tatsuya Matsushima Takuya Okubo Masato Kobayashi Yuya Ikeda Ryosuke Takanami Paul Yoo Yutaka Matsuo Yusuke Iwasawa The University of Tokyo Japan Osaka University Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Due to the inherent uncertainty in their deformability during motion, previous methods in deformable object manipulation, such as rope and cloth, often required hundreds of real-world demonstrations to train a manipulation policy for each object, which hinders their applications in our ever-changing world. To address this issue, we introduce GenDOM, a framework that allows the manipulation policy to handle different deformable objects with only a single real-world demonstration. To achieve this, we augment the policy by conditioning it on deformable object parameters and training it with a diverse range of simulated deformable objects so that the policy can adjust actions based on different object parameters. At the time of inference, given a new object, GenDOM can estimate the deformable object parameters with only a single real-world demonstration by minimizing the disparity between the grid density of point clouds of real-world demonstrations and simulations in a differentiable physics simulator. Empirical validations on both simulated and real-world object manipulation setups clearly show that our method can manipulate different objects with a single demonstration and significantly outperforms the baseline in both environments (a 62% improvement for in-domain ropes and a 15% improvement for out-of-distribution ropes in simulation, as well as a 26% improvement for ropes and a 50% improvement for cloths in the real world), demonstrating the effectiveness of our approach in one-shot deformable object manipulation. https://***/view/gendom/home.

关键词： Deformable models Training Point cloud compression Uncertainty Task analysis robotics and automation Physics

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TPGP: Temporal-Parametric Optimization with Deep Grasp Prior for Dexterous Motion Planning

TPGP: Temporal-Parametric Optimization with Deep Grasp Prior...

引用

IEEE International Conference on robotics and automation (ICRA)

作者： Haoming Li Qi Ye Yuchi Huo Qingtao Liu Shijian Jiang Tao Zhou Xiang Li Yang Zhou Jiming Chen College of Control Science and Engineering Zhejiang University Hangzhou China The College of Control Science and Engineering the State Key Laboratory of Industrial Control Technology Zhejiang University The Key Key Lab of CS&AUS of Zhejiang Province State Key Lab of CAD&CG Zhejiang University Zhejiang Lab OPPO US Research Center

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Grasping motion planning aims to find a feasible grasping trajectory in the configuration space given an input target grasp. While optimizing grasp motion with two or three-fingered grippers has been well studied, the study on natural grasp motion planning with a dexterous hand remains a very challenging problem due to the high dimensional working space. In this work, we propose a novel temporal-parametric grasp prior (TPGP) optimization method to simplify the difficulty of grasping trajectory optimization for the dexterous hand while maintaining smooth and natural properties of the grasping motion. Specifically, we formulate the discrete trajectory parameters into a temporal-based parameterization, where the prior constraint provided by a hand poser network, is introduced to ensure that hand pose is natural and reasonable throughout the trajectory. Finally, we present a joint target optimization strategy to enhance the target pose for more feasible trajectories. Extensive validations on two public datasets show that our method outperforms state-of-the-art methods regarding grasp motion on various metrics.

关键词： Measurement Employment Optimization methods Grasping Planning robotics and automation Grippers

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Android Malware Detection Model Incorporating Function Call Graph and Permission

Android Malware Detection Model Incorporating Function Call ...

引用

IEEE International Conference on Mechatronics, robotics and automation (ICMRA)

作者： Zekun Xia Shaofei Wu School of Computer Science and Engineering Wuhan Institute of Technology Hubei Province Key Laboratory of Intelligent Robots Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

The proliferation of malicious software in the smartphone market, particularly targeting the Android operating system, has sparked a need for accurate detection methodologies. Leveraging machine learning and deep learning techniques, prior research has focused on feature extraction from APK files to identify such threats. However, the reliance solely on single-dimensional feature vectors, such as permissions extracted from manifest files (***), often falls short in accurately predicting malware because code obfuscation makes accurate prediction difficult.”. In response, this paper presents a hybrid detection approach. Initially, permission data sourced from the APK manifest file is fed into a multilayer perceptron (MLP) model, yielding prediction result A with an accuracy of 91.81%. Subsequently, function call graphs (FCGs) extracted from APKs are processed by a graph convolutional network (GCN) model, producing prediction result B with an accuracy of 93.50%. These two prediction sets are then amalgamated, considering both model accuracies and individual APK prediction probabilities. Introducing a novel feature u i , computed as the product of a i (model accuracy) and p i (APK prediction probability), facilitates the integration process. Utilizing these enhanced features, a logistic regression meta-model is trained, achieving a prediction accuracy of 96.50%.

关键词： Accuracy Mechatronics Operating systems Computational modeling Predictive models Multilayer perceptrons Feature extraction Malware Vectors robotics and automation

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21st Century Skills and the Digital Economy Readiness Among TVET-Focused University Students in Malaysian Technical University

21st Century Skills and the Digital Economy Readiness Among ...

引用

International Conference on Engineering Education (ICEED)

作者： Mohd Shafie Rosli Nor Shela Saleh Azri Syazwan Atan Azlah Md. Ali Suaibah Abu Bakar Siti Sarawati Johar School of Education Universiti Teknologi Malaysia Johor Bahru Malaysia Department of Social Sciences Center for General Studies and Co-Curricular Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia Politeknik Tuanku Syed Sirajuddin Arau Malaysia School of Human Resource Development & Psychology Universiti Teknologi Malaysia Johor Bahru Malaysia

ISBN: (数字)9798350367416

ISBN: (纸本)9798350367423

The digital economy, a term coined for the future knowledge and creativity-based economy shaped by the Fourth Industrial Revolution's expansion of Artificial Intelligence, robotics, and automation, had a massive impact on higher education. Universities implemented educational technology and innovative pedagogical approaches to foster 21st century skills, which were expected to be the success formula for university graduates in the digital economy. The impact of Covid-19 accelerated the improvement in university courses. Work from home, virtual collaboration, and remote learning have accelerated the demand for 21st century skills in the labour market. TVET is an ideal solution to the digital economy's challenges of unemployment and precarity. However, there is still a research gap regarding digital economy readiness, the status of 21st century skills, and the relationship between skills and digital economy readiness among TVET -focused university students. In this correlational study, 219 students from a Malaysia University Technical Network (MTUN), a TVET-focused university in Malaysia, participated. As an instrument, a 24-items questionnaire was used. A descriptive analysis reveals a high level of perceived problem-solving and creativity abilities. Leadership and innovative skills were also rated highly, though not as highly as perceived problem-solving and creativity abilities. The perceived readiness for the digital economy is also high. The items were found to be extremely valid using Confirmatory Factor Analysis. The fitness of the Measurement Model and the Structural Model is exceptional. The Structural Equation Model demonstrates the strong links between innovation and problem-solving abilities in the context of digital economy readiness.

关键词： Technological innovation Leadership Instruments Mathematical models Fourth Industrial Revolution Problem-solving Unemployment robotics and automation Engineering education Creativity

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Lightweight UAV Image Drowning Detection Method Based on Improved YOLOv7*

Lightweight UAV Image Drowning Detection Method Based on Imp...

引用

WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Yuhao Cui Mingqiu Li Xupeng Huang Yang Yang Changchun University of Science and Technology Changchun China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

Using Unmanned Aerial Vehicle (UAV) for searching for drowning individuals can enhance the efficiency of search and rescue operations. To tackle the challenges posed by the YOLOv7 model, including its numerous parameters and high computational costs, as well as the difficulties in UAV deployment, we propose a lightweight method for UAV image drowning detection, utilizing an enhanced version of YOLOv7. We replace the backbone in YOLOv7 with MobileNetV4 and reconstruct the SPPCSPC structure to decrease the model's parameter count and computational complexity. Additionally, we enhance detection accuracy by improving convolutional layers and modifying pooling structures. Additionally, we introduce a CA attention mechanism to mitigate the problem of limited feature information caused by the small area of human bodies exposed on water surfaces. Finally, we use Wise-IoU as the model loss function to mitigate the adverse effects of low-quality samples. Experimental results show that our improved algorithm only sacrifices 1.08% mAP, the number of parameters was reduced by 46.7% compared to YOLOv7, increases detection speed by 97.8%, and better balances detection speed with accuracy.

关键词： Accuracy Quantization (signal) Computational modeling Object detection Autonomous aerial vehicles Personnel robotics and automation

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A Lightweight CNN for RGB-D Grasp Classification in Computer Vision-Based Prosthetic Hands

A Lightweight CNN for RGB-D Grasp Classification in Computer...

引用

International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Umar Farooq Wajdan Ali Khan Ayesha Zeb Izna Awais Mohsin Tiwana Muhammad Qasim Tariq Department of Mechatronics Engineering CEME National University of Science and Technology (NUST) Islamabad Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Prosthetic hand development has progressed significantly from basic mechanical designs to advanced systems that incorporate computer vision for enhanced functionality. However, the challenge now is creating a lightweight, accurate and computationally efficient model suitable for integrating embedded microcontrollers in prosthetic devices. This paper presents a novel framework based on a Convolutional Neural Network (CNN) model with just 160,000 trainable parameters and a size of under 1MB, making it viable for deployment in microcontrollers like Arduino, ESP, and Raspberry Pi. The framework has a comprehensive input of 32x32x3 which incorporates grey-scaled RGB, depth, and masked data. The presented model is tested on a newly structured RGB-D dataset containing 51 objects across 7 grasp types, achieving 99% accuracy with minimal computational resources.

关键词： Computer vision Accuracy Microcontrollers Computational modeling Robot vision systems Feature extraction Cameras Convolutional neural networks robotics and automation Prosthetic hand

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A Low Complexity Parameter Extraction Digital Predistortion Technique for Frequency Hopping Transmitter

A Low Complexity Parameter Extraction Digital Predistortion ...

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Advanced Information, Mechanical Engineering, robotics and automation (AIMERA), IEEE International Conference on

作者： Yaqi Wu Xu Lu Xinyue Zhu Zunyan Liu Zhang Chen NanJing University of Information Science and Technology Nanjing China The Sixty-Thrid Reserech Institute National University of Defense Technology Nanjing China

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

In wireless communication systems, frequency Hooping (FH) is one of the anti-jamming communication systems. However, the nonlinear distortion of power amplifier (PA) in FH communication systems, it will lead to serious signal distortion. This paper proposed a low complexity digital pre-distortion parameter extraction mothed for FH transmitter. The signal's distortion characteristics are integrated into an operator matrix. Consequently, a single set of coefficients is applied to linearize the different carrier frequencies. Finally, the proposed algorithm is experimentally verified. The experimental parameters are set to 20 MHz QAM for the baseband signal and then up-converted to different carriers from 5.6 GHz to 5.7 GHz, with the step of 50MHz.

关键词： Wireless communication Baseband Transmitters Quadrature amplitude modulation Nonlinear distortion Predistortion Complexity theory robotics and automation Mechanical engineering Parameter extraction

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