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4th International Conference on robotics and Computer Vision, ICRCV 2022

作者： Zubair, Arif Fu, Yili Sohail, Amjad Khadija, Anwar Harbin Institute of Technology State Key Laboratory of Robotics and Systems Mechanical Engineering Department Harbin City China Space and Upper Atmosphere Research Commission Divisional Head TandD Hr Directorate General Karachi Pakistan Ned University of Engineering and Technology Industrial and Manufacturing Department Karachi Pakistan

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

A novel normalized-centered image moment invariant-based visual servoing scheme is presented. A fresh combination of the moments invariants is proposed for planar symmetrical objects that are parallel to the image plane at the desired position. The output of the face detection module in the image plane, that is a rectangle around the face is used as a virtual target, treated as a planar symmetrical object which always remains parallel to the image plane. The proposed method completely decouples the DoF and is 2D translation, rotation, and scaling invariant. It was experimentally validated on a real robotic arm using a haar cascade-based face detection system to accomplish visual servoing tasks for both 2D printed faces and actual 3D faces. Unlike traditional deep learning based visual servoing schemes, the proposed markerless visual servoing technique does not require a 3D model of the face or a reference image/pose at the desired position and it can also work with unfamiliar faces without prior training. © 2022 IEEE.

关键词： Face recognition

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CAD-Informed Uncertainty-Aware Sequence and Motion Planning for Robotic Assembly

CAD-Informed Uncertainty-Aware Sequence and Motion Planning ...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Takuya Kiyokawa Ismael Rodriguez Korbinian Nottensteiner Peter Lehner Thomas Eiband Maximo A. Roa Kensuke Harada Department of Systems Innovation Graduate School of Engineering Science Osaka University Osaka Japan Institute of Robotics and Mechatronics German Aerospace Center (DLR) Oberpfaffenhofen Weßling Germany Industrial Cyber-Physical Systems Research Center The National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

This study addresses a multi-objective optimization problem in the planning of an uncertainty-aware sequence and motion for mechanical products with intricate structures and numerous contact areas. To generate an optimized sequence and motion that satisfies multiple conditions under mandatory requirements, we use a multi-objective optimization algorithm inspired by Non-Dominated Sorting Genetic Algorithm III, along with contact-rich robotic assembly-oriented constraints and objective functions. The proposed pipeline takes as input the CAD models of robot hardware, workspace, and assembled parts, conducts 3D geometrical and physical simulations of assembly motions, and then optimizes the assembly plan, including parts order, object placement pose, state transition, grasp, and trajectory for the real robot to execute. To obtain the uncertainty-aware sequence and motion, we incorporated a Contingent Contact-Exploring Rapidly-exploring Random Trees (ConCERRT)-based state transition planner and an objective function to evaluate the uncertainty in the multi-objective optimization algorithm. Our experiments on assembly planning for a chainsaw product demonstrated that the proposed method can generate constraint-satisfied assembly plans with a success rate of 99.2% while lowering the uncertainty in the simulations.

关键词： Robotic assembly Solid modeling Uncertainty Linear programming Planning Trajectory Mechanical products Robots Optimization Assembly

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Bioinspired Sensing of Undulatory Flow Fields Generated by Leg Kicks in Swimming

arXiv

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arXiv 2025年

作者： Wang, Jun Shen, Tongsheng Zhao, Dexin Zhang, Feitian Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China The National Innovation Institute of Defense Technology Beijing China National Innovation Institute of Defense Technology Beijing100071 China

The artificial lateral line (ALL) is a bioinspired flow sensing system for underwater robots, comprising of distributed flow sensors. The ALL has been successfully applied to detect the undulatory flow fields generated by body undulation and tail-flapping of bioinspired robotic fish. However, its feasibility and performance in sensing the undulatory flow fields produced by human leg kicks during swimming has not been systematically tested and studied. This paper presents a novel sensing framework to investigate the undulatory flow field generated by swimmer’s leg kicks, leveraging bioinspired ALL sensing. To evaluate the feasibility of using the ALL system for sensing the undulatory flow fields generated by swimmer leg kicks, this paper designs an experimental platform integrating an ALL system and a lab-fabricated human leg model. To enhance the accuracy of flow sensing, this paper proposes a feature extraction method that dynamically fuses time-domain and time-frequency characteristics. Specifically, time-domain features are extracted using one-dimensional convolutional neural networks and bidirectional long short-term memory networks (1DCNN-BiLSTM), while time-frequency features are extracted using short-term Fourier transform and two-dimensional convolutional neural networks (STFT-2DCNN). These features are then dynamically fused based on attention mechanisms to achieve accurate sensing of the undulatory flow field. Furthermore, extensive experiments are conducted to test various scenarios inspired by human swimming, such as leg kick pattern recognition and kicking leg localization, achieving satisfactory results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Aircraft wing vibration parameters measurement system using MEMS IMUs and closed-loop optimal correction

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Aerospace systems 2022年第3期5卷 473-480页

作者： Maamo, M.S. Afonin, A.A. Sulakov, A.S. Department of Control Systems Informatics and Power Engineering Moscow Aviation Institute (National Research University) Volokolamskoe shosse 4 Moscow 125993 Russian Federation Faculty of Robotics and Mechatronics Moscow University for Industry and Finance «Synergy» Street Meshchanskaya 9/14 Moscow 129090 Russian Federation

Nowadays, there is still a need for the development of a high-precision measurement system for aircraft structural elements vibrations, this is due to the fact that many aviation problems are associated with analyzing vibration characteristics of aircraft mechanical structures during its main modes of operation, including flight, particularly the airplane wing health monitoring. This paper presents preliminary results of research carried out toward building a promising system designed to measure vibration parameters of airplane wing. Comparing it with the existing analogues systems, the proposed system features the use of approaches that are traditional for solving orientation and navigation problems for vibration measurements. The article discusses the principles of building such system based on micromechanical inertial measurements units, displacement sensors, on-board aircraft navigation system and sensor data fusion technology. The paper provides a brief overview of the existing solutions in this field of research, and substantiates the relevance and feasibility of the proposed technical solution. The paper presents the basic hardware components of the proposed system, presents the fundamentals of its operation based on the data collected from many sources, including displacement sensors, inertial units and the optimal Kalman estimation and correction algorithm. Besides, the paper presents the mathematical errors models of the system main components. The main algorithms of the system are shown, including algorithms for inertial measurements, estimation and correction in a closed-loop scheme for including optimal Kalman filter, and indirect calculations of vibration parameters. Finally, the initial simulation results of system operation are shown, demonstrating its operability and expected accuracy characteristics, which confirms the system effectiveness and the prospects of the chosen direction of research. © 2022, Shanghai Jiao Tong University.

关键词： Airplane wing Closed-loop optimal correction MEMS IMU Navigation and orientation Optimal Kalman filter Sensor data fusion Vibration measurement

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Research on Multiple Automated Guided Vehicles Path Planning to Reduce Default Time 2

Research on Multiple Automated Guided Vehicles Path Planning...

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2nd IEEE International Conference on Data Science and Computer Application, ICDSCA 2022

作者： Liu, Yiyang Yan, Shuaihua Hou, Zheng Zhao, Yang Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China Kunshan Intelligent Equipment Research Institute Key Laboratory of Networked Control Systems Shenyang China Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China University of Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China College of Information Science and Engineering Northeastern University Shenyang China School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

This paper studies the path planning problem of multiple automated guided vehicles (multi-AGV) in a matrix manufacturing workshop. We proposed the multi-AGV path planning problem with default time, which is a variant of the classic path planning problem. In order to ensure the timely completion of each task, the default time of the task is explicitly considered. The goal is to obtain a conflict-free path with the minimum total default time, total time, and total path length. For these three goals, this problem is NP-hard. In order to solve this problem better, we established a multi-objective optimization model of total default time, total time and total path length of multi-AGV in flexible manufacturing environment. This paper proposes a multi-AGV path planning method that integrates game theory and A∗ algorithm. This method regards each AGV as a participant in the game model, through the default time obtained from each game dynamically adjusts the probability of each AGV planning the next path, and uses game theory to help the system dynamically select the most suitable AGV to plan the next path to improve the on-time delivery rate of tasks. Finally, the simulation results show that the multi-AGV path planning method integrating game theory and A∗ algorithm further reduces the total default time, total time and total path length of all AGVs. © 2022 IEEE.

关键词： Motion planning

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Real Time Humanoid Footstep Planning With Foot Angle Difference Consideration for Cost-To-Go Heuristic

Real Time Humanoid Footstep Planning With Foot Angle Differe...

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International Conference on Ubiquitous Robots (UR)

作者： Joon-Ha Kim Young-Ha Shin Hyobin Jeong Jun-Ho Oh Hae-Won Park Department of Mechanical Engineering Humanoid Research Center School of Mechanical Aerospace & Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Rainbow Robotics Company Daejeon Republic of Korea

This paper proposes a real-time footstep planning framework based on the A* footstep planning algorithm. The proposed footstep planner utilizes an energy consumption-based cost function to generate energy efficient navigation plan. To increase the computational efficiency, the cost-to-go heuristic function, which approximates the remaining cost to reach the goal, is modeled more precisely by considering the angle difference between the robot and the goal position. Furthermore, an efficient dual-level feasibility check is done to avoid occlusion with the environment while stepping over small objects if possible. Finally, the proposed footstep planning algorithm is integrated with the mapping algorithm and the walking controller to solve the humanoid navigation problem and validated in simulation and real-world experiments.

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Automated Electromagnetic Manipulation of Micron-Scale Rotors on Unstructured Surfaces in Low Reynolds Numbers

Automated Electromagnetic Manipulation of Micron-Scale Rotor...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Shihao Zhong Kailun Bai Yaozhen Hou Hen-Wei Huang Qing Shi Qiang Huang Huaping Wang Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Science and Technology on Space Physics Laboratory Beijing China Laboratory for Translational Engineering Harvard Medical School Cambridge MA USA Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Ministry of Education Beijing China

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

The utilization of magnetic microrotors in low Reynolds number liquid environments holds great promise for a wide range of biomedical applications, owing to their ability to maneuver with flexibility. However, during the movement of the microrotors on solid unstructured surfaces, the interaction with neighboring walls has the potential to generate unpredictable disruptions in its motion. This study proposes an automated electromagnetic manipulation method for guiding micron-scale surface rotors along planar paths at low Reynolds numbers. To this end, we designed a magnetic field drive system using multistage electromagnets, and a double-helix rotor with micrometer-scale dimensions. Additionally, we developed a kinematic model for the microrotor that closely resembles that of wheeled mobile robots and employed a sliding mode motion controller to ensure high-precision path following. Results obtained from simulation and experimentation confirm the efficacy of our proposed method, demonstrating precise following of planar paths.

关键词： Visualization Mechatronics Rotors Kinematics Solids Electromagnets Magnetic fields Mobile robots Micromagnetics Electromagnetics

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Optimizing Handwritten Character Recognition systems: Neural Networks vs. Statistical Methods

Optimizing Handwritten Character Recognition Systems: Neural...

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Intelligent systems and Advanced Applications (ICISAA), International Conference on

作者： Vikash K. Agrawal Srinivasa Rao Bogireddy Lalit N. Patil Mahesh Sonekar Yashraj M. Patil Vikas Singh Panwar Department of Automation and Robotics Engineering Dr. D. Y. Patil Institute of Technology Pune India Horizon Systems Inc Phoenix AZ USA Department of Mechanical Engineering Dr. D. Y. Patil Institute of Technology Pune India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

This investigation focuses on the identification of handwritten characters using neural network technology combined with statistical methods. Handwriting recognition has gained popularity due to its wide range of applications in document processing, signature authentication, and digitized communication. The use of neural networks, particularly deep learning models, has greatly enhanced the accuracy and efficiency of character recognition systems. This study provides a comprehensive analysis aimed at evaluating the effectiveness of neural networks in recognizing handwritten characters. Various neural network architectures, including convolution and recurrent neural networks, are explored; each designed to address specific challenges in character recognition. Additionally, statistical methods are employed for feature extraction, further improving recognition accuracy. Extensive testing and benchmarking are conducted on diverse handwritten datasets to assess the performance of the proposed approaches. The results demonstrate the significant benefits of combining artificial neural networks with statistical techniques to achieve high levels of accuracy in character recognition, paving the way for practical applications. This study makes substantial contributions to the field of handwritten character recognition by highlighting the synergistic advantages of neural networks and statistical methods, which will aid in the development of more accurate and robust authentication systems for future applications.

关键词： Training Handwriting recognition Accuracy Statistical analysis Neural networks Authentication Artificial neural networks Writing Feature extraction Character recognition

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Trustworthy Bayesian Perceptrons

Trustworthy Bayesian Perceptrons

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International Conference on Information Fusion

作者： Markus Walker Hayk Amirkhanian Marco F. Huber Uwe D. Hanebeck Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany Department Cyber Cognitive Intelligence (CCI) Fraunhofer Institute for Manufacturing Engineering and Automation IPA Stuttgart Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Germany

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

Bayesian Neural Networks (BNNs) offer a sophisticated framework for extending classical neural network point estimates to encompass predictive distributions. Despite the high potential of BNNs, established BNN training methods such as Variational Inference (VI) and Markov Chain Monte Carlo (MCMC) grapple with issues such as scalability and hyperparameter dependence. In addressing these issues, our research focuses on the fundamental elements of BNNs, in particular perceptrons and their predictive capabilities. We introduce a new perspective on the closed-form solution for backward-pass computation for the Bayesian perceptron and prove that the state-of-the-art solution is equivalent to statistical linearization. To assess the efficacy of Bayesian perceptrons and provide insights into their performance in distinct input space regions, a novel methodology utilizing k-d trees as a space partitioning method is introduced to evaluate prediction quality within specific input space regions.

关键词： Training Systematics Monte Carlo methods Scalability Neural networks Estimation Predictive models Nonhomogeneous media Bayes methods Testing

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Care3D: An Active 3D Object Detection Dataset of Real Robotic-Care Environments

Care3D: An Active 3D Object Detection Dataset of Real Roboti...

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IEEE International Symposium on Multimedia

作者： Michael G. Adam Sebastian Eger Martin Piccolrovazzi Maged Iskandar Joern Vogel Alexander Dietrich Seongjien Bien Jon Skerlj Abdeldjallil Naceri Eckehard Steinbach Alin Albu-Schaeffer Sami Haddadin Wolfram Burgard Department of Computer Engineering School of Computation Information and Technology Chair of Media Technology Technical University of Munich Munich Germany Institute of Robotics and Mechatronics German Aerospace Center (DLR) Wessling Germany Department of Computer Engineering School of Computation Information and Technology Technical University of Munich Chair of Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence (MIRMI) Munich Germany Engineering Department University of Technology Nuremberg Nuremberg Germany

As labor shortage increases in the health sector, the demand for assistive robotics grows. However, the needed test data to develop those robots is scarce, especially for the application of active 3D object detection, where no real data exists at all. This short paper counters this by introducing such an annotated dataset of real environments. The captured environments represent areas which are already in use in the field of robotic health care research. We further provide ground truth data within one room, for assessing SLAM algorithms running directly on a health care robot.

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