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arXiv 2023年

作者： Batinovic, Ana Goricanec, Jurica Markovic, Lovro Bogdan, Stjepan University of Zagreb Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems Unska 3 Zagreb10000 Croatia

In this paper we address the problem of path planning in an unknown environment with an aerial robot. The main goal is to safely follow the planned trajectory by avoiding obstacles. The proposed approach is suitable for aerial vehicles equipped with 3D sensors, such as LiDARs. It performs obstacle avoidance in real time and on an on-board computer. We present a novel algorithm based on the conventional Artificial Potential Field (APF) that corrects the planned trajectory to avoid obstacles. To this end, our modified algorithm uses a rotation-based component to avoid local minima. The smooth trajectory following, achieved with the MPC tracker, allows us to quickly change and re-plan the UAV trajectory. Comparative experiments in simulation have shown that our approach solves local minima problems in trajectory planning and generates more efficient paths to avoid potential collisions with static obstacles compared to the original APF method. © 2023, CC BY-SA.

关键词： Motion planning

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A 5G-Based Switching Method between a Local controller and an Edge controller 6

A 5G-Based Switching Method between a Local Controller and a...

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6th International Conference on intelligent robotics and control Engineering, IRCE 2023

作者： Wang, Xiulian Lin, Jinggao Jin, Xi Xia, Changqing Xu, Chi College of Automation and Electrical Engineering Shenyang Ligong University China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350326338

With the rapid development of edge computing, industrial automation has evolved a two-layer distributed computing architecture, including local controllers communicating through wired networks and edge controllers communicating wirelessly. The edge controllers in existing studies use WIFI, which has a limited working distance and requires independent network deployment, so they are unsuitable for future large-scale complex industrial production. At present, 5G technology is undergoing rapid development in the field of industrial automation, with wide coverage and excellent mobility, gradually becoming suitable for future large-scale and complex industrial production scenarios. Therefore, this paper proposed an SW5G method based on 5G, which combined the local controller with the edge controller. The controllers were switched according to the constantly changing 5G network transmission delay, thereby optimizing the overall system control performance. Finally, simulation experiments demonstrated good control performance of SW5G in various network scenarios. © 2023 IEEE.

关键词： controllers

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Research on Sensorless control of Pulse Generator based on Sliding Mode Observer

Research on Sensorless Control of Pulse Generator based on S...

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Artificial Intelligence and Automation control (AIAC), International Conference on

作者： Hui Zhang Zhiguo Wu Bopi Jin Shengxuan Yang Guoliang Yin College of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China Shenyang Institute of Automation & Institutes of Robotics and Intelligent Manufacturing Chinese Academy of Sciences College of Electrical and Control Engineering Shenyang Jianzhu University & State Key Laboratory of Robotics Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences & Institutes of Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

To address the problems of chattering and slow response in the sliding mode control system of surface-mounted permanent magnet synchronous motor (SPMSM), a novel sliding mode controller combined with an enhanced sliding mode observer is proposed. A new reaching law of sliding mode is designed, which uses the tanh function as the switching function of the control system to smooth the switching process. The terminal attractor is introduced to improve the convergence speed and control quality of the system. An improved sliding mode observer is developed, which replaces the sign function with a continuous function, compensates for the observation error, reduces the chattering effect, and improves the observation accuracy. A simulation model of double closed-loop speed control system based on the proposed sliding mode controller and observer is established, and a simulation experiment is conducted to compare it with the PI control system and the sliding mode control system based on exponential reaching law. The simulation results show that the proposed control strategy can effectively diminish the chattering effect, enhance the system convergence, and better track the given signal.

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Design and Validation of a Wireless Drone Docking Station

arXiv

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arXiv 2023年

作者： Stuhne, Dario Vasiljević, Goran Bogdan, Stjepan Kovačić, Zdenko LARICS Laboratory for Robotics and Intelligent Control Systems University of Zagreb Faculty of Electrical Engineering and Computing Unska 3 Zagreb10000 Croatia

Drones are increasingly operating autonomously, and the need for extending drone power autonomy is rapidly increasing. One of the most promising solutions to extend drone power autonomy is the use of docking stations to support both landing and recharging of the drone. To this end, we introduce a novel wireless drone docking station with three commercial wireless charging modules. We have developed two independent units, both in mechanical and electrical aspects: the energy transmitting unit and the energy receiving unit. We have also studied the efficiency of wireless power transfer and demonstrated the advantages of connecting three receiver modules connected in series and parallel. We have achieved maximum output power of 96.5 W with a power transfer efficiency of 56.6% for the series connection of coils. Finally, we implemented the system in practice on a drone and tested both energy transfer and landing. Copyright © 2023, The Authors. All rights reserved.

关键词： Drones

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Pedestrian Target Detection Model Based on Improved Yolov8 5

Pedestrian Target Detection Model Based on Improved Yolov8

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5th International Conference on Artificial Intelligence and Electromechanical Automation, AIEA 2024

作者： Huang, Jinglun Liang, Jianan Cao, Yongjun School of Electronics and Information Engineering Wuyi University Guangdong Jiangmen China Guangdong Key Laboratory of Modern Control Technology Institute of Intelligent Manufacturing Gdas Guangdong Guangzhou China South China Robotics Innovation Research Institute Guangdong Foshan China School of Mechanical & Automotive Engineering South China University of Technology Guangdong Guangzhou China

ISBN: (纸本)9798350366174

Dense pedestrian detection is a key technology in public intelligent surveillance. It identifies the location and number of pedestrians in videos through object detection methods, thereby achieving intelligent surveillance of crowds. However, in crowded scenes, due to occlusion and small pedestrian targets, it is easy to miss pedestrians. To solve this problem, this paper proposes an improved YOLOv8 detection algorithm, YOLOv-8FastTCD. Firstly, the FasterNet module is used to replace the backbone network. Secondly, a task collaborative dynamic detection head (TCDDH) is introduced after the neck network. The performance of this model on the CrowdHuman dense pedestrian detection dataset has been improved, with mAP50 and mAP50-95 reaching 78.4% and 51.5% respectively, which are 2.1% and 2.0% higher than the original YOLOv8n model. © 2024 IEEE.

关键词： FasterNet Pedestrian Detection TCDDH YOLOv8

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Virtual chain: A storage model supporting cross-blockchain transaction

Virtual chain: A storage model supporting cross-blockchain t...

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作者： Wang, Yan Yang, Bingqing Liu, Jingxin Zeng, Hui Xia, Changqing College of Information Liaoning University Shenyang China State key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

At present, blockchain is not only applied in the financial field, but also extended to the medical, insurance, internet of things, and many other fields. Its independence leads to more and more significant problems of network isolation. To address the problem of the cooperative operation between different blockchains, this article proposes virtual chain, a storage model supporting cross-blockchain transaction. It not only ensures that cross-chain transactions are tamper-proof and traceable but also realizes completely decentralized storage. First, to tackle the multidimensional heterogeneous problem existing in cross-chain trading, a data structure based on prefix tree is proposed to manage cross-chain trading data on top of source chain and target chain. Second, a signature verification mechanism based on elliptic curve is proposed to meet the operational atomicity requirements of both parties of cross-chain transaction. Third, the data fusion technology is used to expand the block capacity indirectly to achieve the high throughput of cross-chain transactions. Finally, by using the credibility-based access control and block chain technology to ensure the security of the data storage model, and conduct security analysis from three aspects of tamper-proof, data protection and anti-conspiracy node attack. The experiment shows that the proposed cross-chain transaction storage model can realize the efficient storage of the heterogeneous data, and satisfy the high requirement of the cross-chain transaction. Moreover, the virtual chain storage model establishes a safe and reliable working environment. © 2020 John Wiley & Sons Ltd.

关键词： Blockchain

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An Optimization Method for SLAM Feature Point Extraction in Fuzzy Scenes

An Optimization Method for SLAM Feature Point Extraction in ...

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Electrical Engineering and Automation (IFEEA), International Forum on

作者： Yunmeng Wang Long Cui Zaoming Liu Nailong Liu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Science and Technology on Space Intelligent Control Laboratory Beijing Institute of Control Engineering Beijing China

In order to solve the problem that image blur is easily caused by camera shaking of micro-robots during surgical navigation, which leads to the accumulation of SLAM errors, this paper studies the feature point extraction optimization method based on deep learning in fuzzy scenes, proposes an improved defuzzification network, and finally applies the network to the front end of SLAM, and conducts experimental verification on the open data set TUM. The results show that the proposed algorithm significantly improves the accuracy of front-end feature extraction and matching.

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A Gaze-Guided Human Locomotion Intention Recognition and Volitional control Method for Knee-Ankle Prostheses

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Chen, Xinxing Chen, Zhaokai Wang, Yuxuan Yin, Shucong Chen, Chuheng Huang, Jian Fu, Chenglong Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Hubei Key Laboratory of Brain-inspired Intelligent Systems School of Artificial Intelligence and Automation Wuhan430074 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen518055 China Shanghai Jiaotong University Shanghai China

This paper presents a novel gaze-guided volitional control method for knee-ankle prostheses, designed to enhance the precision and intuitiveness of prosthetic control in complex locomotion tasks. The method utilizes an eye-tracking system coupled with an RGBD camera to estimate 3D gaze points, which are then used to construct a gaze attention map. This map serves as the basis for predicting human locomotion intentions, enabling the prosthesis to accurately step on the intended foot placement. Experimental validation was conducted with both a healthy subject and an amputee subject, focusing on level-ground walking and stair ascent tasks. The results demonstrated that the prosthesis could achieve precise foot placement, with a mean error of 1.22 cm ± 3.14 cm for the healthy subject and 3.40 cm ± 0.66 cm for the amputee subject during level-ground walking. In stair ascent tasks, the system successfully adapted to various gait patterns, with the prosthetic joint angles closely matching natural human movements. The proposed method also effectively reduced the root mean square error (RMSE) of 3D gaze estimation to 2.70 cm, 2.63 cm, and 1.97 cm along the x, y, and z axes, respectively. These findings indicate that the gaze-guided volitional control method not only enhances the functionality of knee-ankle prostheses but also offers a more intuitive interface for users, aligning prosthetic actions with the user’s visual focus and intentions. © 2016 IEEE.

关键词： Knee prostheses

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Landing Area Prediction in Complex Terrains for Walking-assisted Lower-limb Exoskeleton Robot

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Procedia Computer Science 2023年 226卷 120-126页

作者： Yuexun Liu Du-Xin Liu Yue Ma Sheng Guo Xinyu Wu Robotics Research Center School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing 100044 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China

With the expansion of application scenarios, the walking-assisted lower-limb exoskeleton robot urgently needs to improve its walking ability in complex terrains. However, due to the lack of environmental understanding ability and decision-making ability, the exoskeleton can not meet the need of people with lower-limb disability in complex terrains. To improve the walking abillity, this paper proposes a method of predicting the landing area in complex terrains for walking-assisted lower-limb exoskeleton robot. Multiple sensors were used to obtain the terrain information, step length, and stride length of human walking, and a multi-type input convolutional network was built to establish the mapping relationship to learn the decision-making mechanism of human standing in a complex environment. The result shows that the established network effectively predicts the landing area. Under the condition of full training, the predicted position deviation of the landing area is controlled within 6%, and the prediction rationality of the landing area can reach 90%.

关键词： Landing area prediction Walking-assisted lower-limb exoskeleton robot Complex terrains

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Transfer Learning: An Approach on the Riemannian Manifold for Multiclass Motor Imagery EEG Brain-Computer Interface

Transfer Learning: An Approach on the Riemannian Manifold fo...

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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Xu Sun Yaqi Chu Yang Luo Xingang Zhao Department of Control Engineering Northeastern University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation (SIA) Chinese Academy of Sciences (CAS) Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences (CAS) Shenyang China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Motor imagery (MI) EEG-based brain-computer interface (BCI) facilitates direct communication between the brain’s intentions and a computer. In essence, it allows a person to control external equipment by decoding EEG signals during the mental imagery of limb movements. However, due to the intricate individual variances in EEG signals, creating a general decoding model with parameters applicable to all subjects is exceptionally challenging. Consequently, a prolonged calibration process is necessary to gather labeled subject-specific data for each individual, making it less user-friendly. A potential solution to this problem is transfer learning, a methodology that transfers knowledge from related domains to a target domain. To tackle the problem, this paper proposes a novel transfer learning approach on the Riemannian manifold framework in the context of multiclass MI EEG-based BCI classification. Particularly, a user-specific frequency band selection (FBS) method with MI EEG class distinctiveness is utilized to improve the accuracy and efficiency of Riemannian space calculations, which is measured using the inter-class distance and intra-class variance on the manifold. Then, the Riemannian space Alignment (RA) strategy is used to calibrate the MI EEG variances of different subjects. The comparative experiments between the proposed approach and baseline/conventional methods are conducted on a public dataset with four-class motor imagery EEG, including two-class transfer learning scenario and four-class transfer learning scenario. proposed transfer learning approach is outperformed. Compared with the baseline/conventional methods using a fixed wide frequency band, the preliminary results suggests that the proposed approach can significantly improve the transfer learning performance for the MI EEG signals from different subjects in Riemannian space.

关键词： Manifolds Mechatronics Machine vision Transfer learning Motors Particle measurements Electroencephalography Brain-computer interfaces Frequency measurement Decoding

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