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2022 International Conference on Innovations and Development of Information Technologies and robotics, IDITR 2022

作者： Li, Liang Li, Yiping Zeng, Junbao Xu, Gaopeng Zhang, Yuexing Feng, Xisheng Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9781665409322

Passive sonar is widely used for underwater target tracking which has the advantages of long work distance and excellent concealment, so it has a widespread use in military and civil fields. In this paper, a target passive tracking method based on multiple autonomous underwater vehicles (AUV) is proposed. Passive sonar is utilized to detect and locate the target, and AUV's autonomy and good maneuverability are used to track the target. Firstly, a target tracking method with a single AUV is introduced in detail. Then, a target tracking method with multiple AUVs is proposed including the communication mode between AUVs, a cooperatively target localization method, and the motion control strategy of target tracking. Finally, the two methods are validated and compared by lake experiment. Experimental results show that using multiple AUVs to locate and track the target can track the target movement trajectory better. The method proposed has the advantages of high accuracy, good real-time performance, and strong robustness. © 2022 IEEE.

关键词： Target tracking

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Evaluation of a Bionic Cable-driven Ankle Exoskeleton system for Human Walking Assistance

Evaluation of a Bionic Cable-driven Ankle Exoskeleton System...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Weihao Yin Wei Wang Jianquan Ding Jingtai Liu Juanjuan Zhang Tianjin Key Laboratory of Intelligent Robotics Institute of Robotics and Automatic Information System Nankai University Tianjin China

ISBN: (纸本)9781665465373

In order to further explore the performance of assistive exoskeleton, we tested and evaluated a bionic cable-driven ankle exoskeleton system for human walking assistance in this paper. A bionic ankle exoskeleton was used as an end-effector to provide many different patterns of assistance. Assistive forces were transmitted by a flexible Bowden cable which attached fewer moments of inertia to the wearer and minimized the impact of wearable devices on the wearer’s natural movements. We preset the assistive torque trajectory according to biological ankle moment during walking and used a torque control method of proportional control with damping injection compensated by adaptive iterative learning to achieve high-performance torque control. We evaluated the kinematics and biological efforts of one subject with predefined assistance patterns. Assistance during walking on a treadmill reduced the metabolic cost by 10.34% compared with normal shoes. The root mean square (RMS) of the soleus muscle activity decreased by 52.2%, and ankle joint moment reduced by 40.4% with the same pattern. The experimental results show that the device is capable of improving walking economy.

关键词： Legged locomotion Torque Costs Wearable computers Exoskeletons Torque control Muscles

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Recursive parallelotope set-membership estimation algorithm in nonlinear system 28

Recursive parallelotope set-membership estimation algorithm ...

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28th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2022

作者： Qu, Danyang Zhao, Yiwen Zhao, Xingang Song, Guoli State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9798350334333

In this paper, a novel Recursive Parallelotope Set-Membership Estimation Algorithm for discrete nonlinear system is proposed, which can carry out highly accurate state estimation. The wrapping effect is significant in the prediction process, which lowers the accuracy of the state estimation, and the iteration of existing methods cannot converge in the case of a single observation failure. This is the driving force behind the development of the Recursive Parallelotope Set-Membership Estimation Algorithm. To account for this issue, multiple measurements are introduced in this paper to update the guaranteed state in each iteration, which can eliminate more redundant information and avoid estimation errors caused by the loss of measurements. Then, a covalent separation method is proposed to reduce the wrapping effect, and build the parallelotope set's tight enclosure in nonlinear system. Furthermore, the state set is also updated using a novel parallelotope intersection approach. The suggested method's use and efficiency in terms of maximum and average estimation accuracy are attested by simulation outputs. © 2022 IEEE.

关键词： state estimation

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3D Source Tracking Using a Position-Unknown AOA Sensor with Measurement Drift and UAV Moving Direction Optimization

3D Source Tracking Using a Position-Unknown AOA Sensor with ...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Rongrong Xu Sheng Xu Kaimin Cao Xianliang Li Jialiang Wang Wujing Cao Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China Robotics Laboratory China Nanhu Academy of Electronics and Information Technology China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

This work solves the angle-of-arrival (AOA) source tracking problem in 3-dimensional (3D) space utilizing a position-unknown unmanned aerial vehicle (UAV). An AOA sensor is carried by a mobile UAV, and the conventional self-positioning module, such as the global positioning system (GPS), is not allowed due to environmental limitations. Thus, to guarantee the feasibility of this source tracking problem, at least two position-known landmarks are required. Firstly, the detailed problem formulation is provided with the consideration of measurement drift caused by sensor-vehicle installation errors. Secondly, all the unknown states are estimated using a proposed extended Kalman filter (EKF). Thirdly, a UAV moving direction optimization algorithm is developed to promote the estimation accuracies of the source, UAV and measurement drift. Finally, simulation examples demonstrate the characteristics of the proposed method.

关键词： Three-dimensional displays Accuracy Measurement uncertainty Estimation Autonomous aerial vehicles Robot sensing systems Velocity measurement

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Configuration Enumeration of Multi-Limb Reconfigurable Modular Robots 4

Configuration Enumeration of Multi-Limb Reconfigurable Modul...

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2022 4th International Symposium on robotics and Intelligent Manufacturing Technology, ISRIMT 2022

作者： Feng, Jingkai Liu, Jinguo State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China

By introducing modular and reconfigurable designs, the Multi-limb Reconfigurable Modular Robots (MLRMR) have many different configurations instead of a single fixed configuration, which greatly expands the boundaries of the operation. However, the number of non-isomorphic configurations increases exponentially with the number of modules, making it necessary to explore the enumeration method theoretically. Based on the group theory, the permutation groups of the Multi-limb (including three-limb, four-limb, and five-limb) Reconfigurable Modular Robots are established in this paper, and the theoretical formulas of configuration enumeration are derived by applying the Pólya enumeration theorem. This work can be used as the basis of configuration analysis. © Published under licence by IOP Publishing Ltd.

关键词： Modular robots

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RTHG: Towards Real- Time Head Detection And Gaze Estimation

RTHG: Towards Real- Time Head Detection And Gaze Estimation

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IEEE International Conference on robotics and Biomimetics

作者： Kunxu Zhao Zhengxi Hu Qianyi Zhang Jingtai Liu Institute of Robotics and Automatic Information System and Tianjin Key Laboratory of Intelligent Robotics Nankai University Tianjin China

ISBN: (纸本)9781665481106

As an important way of understanding human in-tentions, gaze estimation has always been a research hotspot in the field of human-robot interaction. Most studies now estimate gaze direction by analyzing head features and head detection is required before gaze estimation. For these two sequential tasks, the current research usually adopts two different networks, which increases the memory occupation of the graphics card and is not easy to deploy on the edge device. In this paper, we propose a unified network for simultaneous head detection and gaze estimation, unifying these two tasks into a multi-task learning model. In this network framework, head detection and gaze estimation share the same set of features, which enables them to promote each other to improve detection accuracy. We evaluated our model on gaze360 dataset and the gaze error dropped to 19.62 degrees while running at 23 fps.

关键词： Image edge detection Neural networks Estimation Human-robot interaction Feature extraction Multitasking Real-time systems

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Diverse Representations Embedding for Lifelong Person Re-Identification

arXiv

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arXiv 2024年

作者： Liu, Shiben Fan, Huijie Wang, Qiang Chen, Xiai Han, Zhi Tang, Yandong The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China The University of Chinese Academy of Sciences Beijing100049 China The Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China The Key Laboratory of Manufacturing Industrial Integrated Automation Shenyang University The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China

Lifelong Person Re-Identification (LReID) aims to continuously learn from successive data streams, matching individuals across multiple cameras. The key challenge for LReID is how to effectively preserve old knowledge while incrementally learning new information, which is caused by task-level domain gaps and limited old task datasets. Existing methods based on CNN backbone are insufficient to explore the representation of each instance from different perspectives, limiting model performance on limited old task datasets and new task datasets. Unlike these methods, we propose a Diverse Representations Embedding (DRE) framework that first explores a pure transformer for LReID. The proposed DRE preserves old knowledge while adapting to new information based on instance-level and task-level layout. Concretely, an Adaptive Constraint Module (ACM) is proposed to implement integration and push away operations between multiple overlapping representations generated by transformer-based backbone, obtaining rich and discriminative representations for each instance to improve adaptive ability of LReID. Based on the processed diverse representations, we propose Knowledge Update (KU) and Knowledge Preservation (KP) strategies at the task-level layout by introducing the adjustment model and the learner model. KU strategy enhances the adaptive learning ability of learner models for new information under the adjustment model prior, and KP strategy preserves old knowledge operated by representation-level alignment and logit-level supervision in limited old task datasets while guaranteeing the adaptive learning information capacity of the LReID model. Extensive experiments were conducted on eleven Re-ID datasets, including five seen datasets for training in order-1 and order-2 orders and six unseen datasets for inference. Compared to state-of-the-art methods, our method achieves significantly improved performance in holistic, large-scale, and occluded datasets. Our code will be availab

关键词： Embeddings

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PCEB-YOLOV8: An Improved PCB Defect Detection Method Based on YOLOV8

PCEB-YOLOV8: An Improved PCB Defect Detection Method Based o...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Meihui Li Chunhe Song Xia Yu Mengmeng Chu Rui Li Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Shenyang University of Technology Shenyang China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Printed circuit board defect recognition is of great significance to ensuring the quality of electronic products. This paper proposes an improved PCB defect detection method based on YOLOV8. Firstly, the data augmentation method PredMix (prediction mix) is adopted to simulate scenarios such as overlapping and occlusion in images, where the displays are incomplete or blurred, thereby enhancing the robustness of the network model. Secondly, BiFPN is used to replace PAN-FPN in the fusion layer to strengthen the feature fusion capability. Finally, the CESE_C2f module is employed in the Backbone part, leveraging a lightweight attention mechanism to improve the detection effect while reducing the number of parameters. Experimental results show that compared with the original YOLOV8, the MAP value of this improved algorithm reaches $\mathbf{92.6}\%$, with a $\mathbf{2.3\%}$ increase in accuracy.

关键词： Accuracy Costs Printed circuits Diversity reception Production Feature extraction Data augmentation Data models Real-time systems Defect detection

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Autonomous Communication Relay Position Planning Based on Predictive Model

Autonomous Communication Relay Position Planning Based on Pr...

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2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Jiang, Han Chang, Yanchun Sun, Xinxuan Liu, Xu Yang, Liying He, Yuqing Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Unit 31432 of the Pla Shenyang China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

When operating in complex mountainous areas, the mission UAV must fly at low altitudes to execute a certain mission. The mission UAVloses wireless communication with the ground station due to the occlusion of the mountains along the way. To this end, this paper proposes a multi-UAV autonomous communication relay position planning method based on the predictive model. The relay UAV is therefore introduced that must always maintain communication transmission between the mission UAV and the ground station. The method includes the establishment of a visibility model, the lightweight characterization of terrain, and the description of the predictive model optimization problem. Simulation results show that the method can autonomously plan the relay UAV position that satisfies the relay function according to the trajectory of the mission UAV in complex mountainous areas. © 2022 IEEE.

关键词： Visibility

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Research on Object Detection Methods in Low-Light Conditions 16th

Research on Object Detection Methods in Low-Light Conditio...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： Wang, Feifan Chen, Xi’ai Wang, Xudong Ren, Weihong Tang, Yandong School of Automation and Electrical Engineering Shenyang Ligong University Shenyang110159 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shenzhen518055 China

ISBN: (纸本)9789819964918

Low-light images are images taken in poorly illuminated environments. Such images suffer from colour distortion, loss of detail and blurriness, which seriously affects the detection accuracy of object detection tasks. In order to improve the accuracy of object detection in low-light images, we propose a low-light image object detection algorithm based on image enhancement. The algorithm is jointly trained on the input side of the YOLOv5 network in combination with an unsupervised low-light enhancement model. The training phase optimises the overall network with the loss of object detection so that the image enhancement results are more favourable for improving the object detection accuracy. In the feature extraction phase, we design a feature enhancement model based on an attention mechanism. Our algorithm is tested on the publicly available ExDark dataset and achieves a mean average precision (mAP) of 79.15%, which is a 4.25% improvement over the baseline. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Object detection

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