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2023 China Automation Congress, CAC 2023

作者： Lan, Xinwei Wu, Chengdong Zhang, Xiangyue Northeastern University Faculty of Robot Science and Engineering Shenyang110169 China

ISBN: (纸本)9798350303759

Object localization is utilized as the first step in standard 6D object pose estimation methods to obtain the position information of the objects. However, these object localization methods cannot be directly applied to unseen objects, which is the focus of recent research on 6D object pose estimation. In this paper, an accurate and efficient localization method for unseen object is proposed, based on a template matching strategy. The Hybrid Channel-Spatial Attention Model (HCSAM) is designed to focus on the target object by enhancing the contextual differences between the target object and background. Additionally, The Multi-Scale Integration Transformer (MSIT) module is designed to eliminate noise interference and enhance semantic information in low-dimensional features by integrating multidimensional information. Our method outperforms existing approaches on the complicated occluded dataset LINEMOD, as well as on the challenging generalized pose estimation dataset GenMOP. © 2023 IEEE.

关键词： Template matching

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A Novel Generalized EEG Channel Selection Method Using Pearson Correlation Coefficient

A Novel Generalized EEG Channel Selection Method Using Pears...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Liu, Dongxu Ding, Qichuan Wen, Maiwei Tong, Chenyu Northeastern University Faculty of Robot Science and Engineering Shenyang110169 China

ISBN: (纸本)9798350325706

Electroencephalography (EEG), as a non-invasive and convenient method for implementing Brain-Computer Interface (BCI), has been widely used in clinical and research fields. EEG data often requires the acquisition of dozens or even hundreds of channels. Channel selection can reduce irrelevant and redundant channels, improve computational efficiency, and enhance the quality of EEG signals. This study introduces a filter method for channel selection based on Pearson correlation coefficient (PCC) with the candidate channel and employs topographic maps of EEG channel scores, derived from data collected across all subjects, to visualize the spatial distribution of channels selected by different methods. In addition, a generalized channel selection algorithm is proposed to determine consistent channels across all subjects in the experimental group. The effectiveness of the proposed method was evaluated on two steady-state visual evoked potential (SSVEP) datasets, and the results indicated that this method exhibits superior performance compared to both the all-channel method and other channel selection methods. And the application of the generalized channel algorithm has further improved the classification performance. This study uses selected generalized channels applied to new subjects with low BCI performance, yielding a significant improvement. The selected channels have a wide range of applicability, helping to simplify EEG acquisition and improve EEG data quality. © 2023 IEEE.

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Human Target Detection and Tracking Algorithm Based on Improved YOLOv7 13

Human Target Detection and Tracking Algorithm Based on Impro...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Min, Yuyao Shui, Jiayi Liu, Shiyu Wang, Junyi Guo, Zexin Northeastern University Department of Faculty of Robot Science and Engineering Shenyang China

ISBN: (纸本)9798350315196

Recently, human-oriented target tracking has broad application prospects in various practical fields, such as medicine and security. YOLOv7 is a powerful detection algorithm in the current main-stream, and it still has room for development in terms of detection accuracy. To handle the issue of mobile robots struggling to maintain recognition precision and stability in complicated scenes, we propose an improved target detection network for YOLOv7. Firstly, based on YOLOv7-tiny network structure, the CBAM attention mechanism is introduced into the bottom of the backbone network, and the SPPCSP module is adopted to reduce the computation of the feature map, which could enhance the network's representational capability. Then, the KCF algorithm is incorporated into the model, which could track the human target after determining its position with respect to the robot with the RGB-D camera. In order to ensure the flexibility of the robot, the Jetson Nano is utilized as the processor. Finally, we conduct human target tracking experiments for the mobile robot in a realistic scenario. The experimental results show that the mAP reaches 91.17%, which is 1.88% higher than only using YOLOv7 after introducing the CBAM module. © 2023 IEEE.

关键词： Target tracking

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robotic Grasp Detection Based on the Improved Light-weight ShuffleNet Model

Robotic Grasp Detection Based on the Improved Light-weight S...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Jiang, Yang Zhang, Xuejiao Zhao, Bin Northeastern University Faculty of Robot Science and Engineering Shenyang China Northeastern University College of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350331790

To predict grasping configurations from RGB and aligned grasping object depth pictures, we propose improved ShuffleNet, an convolutional neural network that is accurate, real-time, and light-weight in this research. The new network adds attention mechanisms to the ShuffleNet. In the paper, it references Adan as the optimization function when training the network. Furthermore, after training and testing our network, it only has a 4% loss in picture identification in the five-parameter Cornell Grasping Dataset and the Jacquard Grasping Dataset, and the GPU inference speed is 7.1 frames per second (fps), which can match the demands of real-time performance. Additionally, after tweaking a few parameters, our model is significantly smaller than the original ShuffleNet (465.92MB), allowing for memory adaption to constrained situations. © 2023 IEEE.

关键词： Convolutional neural networks

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Research on Collision Detection of Collaborative robot using improved Momentum-based Observer

Research on Collision Detection of Collaborative Robot using...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Jiang, Yang Wu, Yao Wu, Xingmao Zhao, Bin Northeastern University Faculty of Robot Science and Engineering Shenyang China Northeastern University College of Information Science and Engineering Shenyang China

ISBN: (纸本)9798350331790

With the increasing application of collaborative robots, it is essential to ensure staff safety during human-machine collaboration, so the robot collision detection problem must be addressed. Traditional collision detection requires external sensors, which increases the cost and the structure's complexity. This paper constructed a generalized momentum-based observer to detect external collisions and introduced a compensation link and variable damping design to improve collision detection speed and accuracy. At the same time, after witnessing an external collision, a collision response strategy based on time-scaling was adopted to protect staff safety. Through algorithm simulation, it was verified that the improved momentum-based observer had significantly improved the real-time performance and detection accuracy. At last, the proposed collision detection algorithm was carried out on a collaborative robot with 6 degrees of freedom. The results show that the proposed collision detection algorithm can accurately detect external collisions without external sensors and effectively protect staff safety. © 2023 IEEE.

关键词： Momentum

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Improving Teleoperation of High Degree-of-Freedom robots with a Dual-Mode System and Variable Admittance Controller Using IMU-Based Tracking 13

Improving Teleoperation of High Degree-of-Freedom Robots wit...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Lian, Siyi Wen, Shiguang Liu, Jing Wu, Qi Han, Lu Wang, Fei Northeastern University Faculty of Robot Science and Engineering Shenyang110169 China Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China LiaoNing Disabled Persons' Service Center China

ISBN: (纸本)9798350315196

Admittance control allows for teleoperation with compliant performance by using external force signals driven by the operator. However, low-cost teleoperation solutions for high degree-of-freedom (DoF) robots using constant admittance controllers are mainly affected by the accuracy of sensors and jitter from operators, which can be avoided with cost-prohibitive solutions. We firstly propose a dual-mode teleoperation system with variable admittance controller and analysis the stability. To accurately track the operator's intention, an open-loop IMU-based method is designed. Our low-cost system enables operators to effectively perform complex tasks in the workspace by smoothly switching between two modes: an operating workspace to ensure teleoperation accuracy, and a moving workspace to improve efficiency of movement. We validate the proposed system through experiments using the UR5 robot in a controlled environment. Results show that our approach outperforms the constant admittance controller in terms of accuracy and efficiency. © 2023 IEEE.

关键词： Remote control

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Enhancing Incremental Learning with One-Class Envelopes for Online Unlabeled Mixed Samples 16

Enhancing Incremental Learning with One-Class Envelopes for ...

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16th International Conference on Digital Image Processing, ICDIP 2024

作者： Hu, Chengyong Ding, Qichuan Faculty of Robot Science and Engineering Northeastern University Shenyang110169 China Foshan Graduate School of Innovation Northeastern University Foshan528311 China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

Incremental learning is crucial for handling classification tasks involving continuously increasing new categories. Traditional models often grapple with challenges such as data forgetting and high storage requirements. An approach grounded in one-class envelope techniques effectively mitigates these concerns. However, this method introduces two new challenges: swiftly determining the class label of a new sample, and effectively distinguishing mixed samples online before training new sub-classifiers. To address these issues, this paper introduces an efficient incremental learning strategy that leverages one-class envelopes, where a dedicated one-class classifier is built for each category. A novel hybrid-distance-based search algorithm for the target category is introduced to enhance prediction efficiency during the testing phase. Moreover, for online mixed samples from multiple unknown categories, the K-means algorithm is employed to cluster all outlier samples. Silhouette coefficients are then calculated to identify well-defined clusters for subsequent classifier training. Experimental results show that the proposed approach reduce prediction time by 90% on Cifar100-30 dataset, and the incremental process for unlabeled mixed samples on the Cifar10 dataset achieves up to 55.3% performance improvement. © 2024 SPIE.

关键词： Contrastive Learning

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Sampled-data synchronization for heterogeneous delays inertial neural networks with generally uncertain semi-Markovian jumping and its application

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Neural Computing and Applications 2024年第33期36卷 20963-20982页

作者： Wang, Junyi He, Wenyuan Xu, Hongli Cai, Haibin Chen, Xiangyong Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China Foshan Graduate School of Innovation Northeastern University Foshan528311 China College of Information Science and Engineering Northeastern University Liaoning Shenyang110819 China Department of Computer Science Loughborough University LoughboroughLE11 3TU United Kingdom School of Automation and Electrical Engineering Linyi University Linyi276000 China

This article is concerned with sampled-data synchronization problem of heterogeneous delays inertial neural networks (INNs) with generally uncertain semi-Markovian (GUSM) jumping. Different from traditional Markovian inertial neural networks (MINNs), the INNs with GUSM are investigated in this paper by fully considering the sojourn time and the lacking transition rates, which is more general and applicable for practical system. The new extended two-sided looped-functional (ETSLF) approach is adopted in this paper, and some improved less conservative criteria are derived to achieve the synchronization of the drive and response INNs. The controller gain matrices are acquired based on synchronization criteria. Finally, the viability of the method is presented through three examples. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Markov processes

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A Reinforcement Learning-Based Approach to Improve the Trajectory Generalization Performance of DMP 42

A Reinforcement Learning-Based Approach to Improve the Traje...

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42nd Chinese Control Conference, CCC 2023

作者： Yu, Peng Fang, Lijin Qian, Yian College of Information Science and Engineering Northeastern University Shenyang110819 China Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China

ISBN: (纸本)9789887581543

Dynamic Motion Primitives (DMP) is a trajectory coding method based on imitation learning, which achieves a wide application owing to its robustness and generalization ability. However, the generalization of DMP in some special cases is not very satisfactory. For instance, when the target point changes, the similarity of the middle part of the trajectory is also greatly affected. Moreover, scaling the trajectory of the DMP is often followed by a reduction in endpoint accuracy. To solve the above problems, the following work is carried out in this paper: First, we reviewed the basic DMP framework and analyzed its worthy improvements. Second, the Pearson correlation coefficient is introduced to quantitatively assess the similarity of generalized trajectories. Next, a model-free, sampling-based reinforcement learning framework Path Integrals (PI2) is designed for the optimization of generalized trajectories, where the Gaussian weights of the DMP are iteratively updated probabilistically. Finally, the feasibility of the method in this paper is verified by simulation in the case of target variation and trajectory scaling. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Reinforcement learning

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ACF-Net: Attention Context Fusion Network for 6D Pose Estimation 13

ACF-Net: Attention Context Fusion Network for 6D Pose Estima...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2023

作者： Guo, Yi Chu, Hao Wu, Jinxiu Shen, Ze Wang, Fei College of Information Science and Engineering Northeastern University Shenyang110819 China Northeastern University Faculty of Robot Science and Engineering Shenyang110169 China

ISBN: (纸本)9798350315196

The advent of cost-effective RGB-D sensors has made it more feasible to achieve high-precision 6D pose prediction from RGB-D images. One of the primary research challenges entails extracting distinctive features from the color features presented in RGB images, as well as capturing geometric details from depth data, while effectively harnessing the synergistic potential of these two mutually complementary data sources. In this study, we propose a bidirectional attention context fusion framework for learning 6D pose parameters from RGB-D images. Specifically, we introduce a lightweight attention mechanism to improve the ability of appearance features learning from RGB images. Additionally, we design a features extraction framework of point clouds to fully explore the spatial information of the local distance structure within the neighborhood and more representative global features obtained by hybrid contextual local information. Furthermore, we combine the speed-up robust feature algorithm with the farthest point sampling algorithm, taking full advantage of our appearance and geometric information extraction to predict pose estimation. In the experimental results, we provide compelling evidence that our method surpasses the performance of state-of-the-art methods on two demanding benchmark datasets, LineMOD and YCB-Video. © 2023 IEEE.

关键词： Cost effectiveness

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