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检索条件"机构=Faculty of Robot Science and Engineering"

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Research on deep reinforcement learning-based complex crowd navigation algorithm using risk perception and path selection 7

Research on deep reinforcement learning-based complex crowd ...

引用

7th International Conference on Mechanical, Electric, and Industrial engineering, MEIE 2024

作者： Zhao, Tianxiang Jiang, Yang Chang, Jiajun Zhao, Bin Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China College of Information Science and Engineering Northeastern University Liaoning 110819 China SIASUN Robot & Automation CO. Liaoning 110618 China College of Information Science and Engineering Shenyang University of Technology Liaoning 110870 China

Aiming at the Frozen robot Problem and the poor effect of dynamic obstacle avoidance in traditional navigation methods when encountering dynamic obstacles, a deep reinforcement learning navigation method based on risk perception and path selection is proposed. The core of this method lies in CP (Collision Probability) module and IOU (Intersection over Union) module. The CP module calculates the CP between the robot and the nearby dynamic obstacles in real time, so that the robot can avoid the dangerous obstacles first. At the same time, the intersection-merging ratio module calculates the "pass ability"of the area near the robot in real time, and guides the robot to choose a safer area to pass through. Compared with the DRL method without these two modules, the proposed method achieves the highest navigation success rate in all simulation test environments, and the maximum improvement rate is 11%. At the same time, it still has advantages in navigation time and other indicators. © Published under licence by IOP Publishing Ltd.

关键词： Deep reinforcement learning

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Brain-LEGO Interactive Control System Based on SSVEP-BCI 13

Brain-LEGO Interactive Control System Based on SSVEP-BCI

引用

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

作者： Ru, Fangzhou Wen, Shiguang Bi, Jingying Wen, Yongzhao Liu, Jing Wu, Qi Han, Lu Wang, Fei Northeastern University Faculty of Robot Science and Engineering Shenyang110169 China College of Information Science and Engineering Northeastern University Shenyang110169 China LiaoNing Disabled Persons' Service Center Shenyang China Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China

ISBN: (纸本)9798350315196

To improve the therapeutic effect for children and adolescents with learning disabilities, this paper developed a brain-Legointeractive(BLI) control system. Based on LEGO therapy, the system uses the analysis of EEG signals to control the movement of LEGO robots, enhancing patient engagement and personalized treatment effects. The experiment collects EEG data from four subjects and uses Canonical Correlation Analysis(CCA) to filter the signals spatially. Fast Fourier Transform(FFT) is then used to extract frequency domain features, which are decoded using Fisher Linear Discriminant Analysis (FLDA) to achieve motion control of the LEGO robot. Based on this foundation, this paper conducts an offline analysis to study the influence of the number and location of electrodes and the length of stimulus time on the performance of BLI, with the goal of exploring the low cost and portability of the system. Additionally, an online experiment is conducted to assess its performance in a practical setting. The experimental results showed that using three channels Oz, 01, and O2, and controlling the stimulus duration between 1.5s-2.5s, a good performance was achieved. The accuracy can both reach over 90%, and the information transfer rate can reach over 23 bits/min. The online evaluation results showed an average accuracy of 88.61% and an information transfer rate of 23.29 bits/min, indicating the feasibility of the brain-LEGO interactive control system. © 2023 IEEE.

关键词： Frequency domain analysis

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Parameters Evaluation and Optimization of Battery-Powered Wireless Smoke Alarms with Cluster-Based Sensor Networks for Real-World Application Requirements 14

Parameters Evaluation and Optimization of Battery-Powered Wi...

引用

14th IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, CYBER 2024

作者： Wang, Li Zhang, Xi Mei, Zhibin Yu, Xiaosheng Shenyang Fire Science and Technology Research Institute of MEM Liaoning Shenyang China Northeastern University Faculty of Robot Science and Engineering Liaoning Shenyang China

ISBN: (纸本)9798331506056

Fire is a leading cause of unnatural deaths in daily life. Battery-powered wireless smoke alarm systems are crucial in preventing fire deaths in communities. However, large-scale applications face issues such as rapid battery consumption, uneven energy consumption, concentrated risk of malfunctions, and vulnerability of communication to environmental impacts. To address this issue, we introduce a cluster-based network structure that operates on NB-IoT and AdHoc dual-mode converged communication. We propose a method for evaluating the system's performance and optimizing its parameters before physical fabrication. Laboratory experiments and 2-year-long real-world field tests validate the effectiveness of our method in solving the real-world wireless smoke alarm system application problem. © 2024 IEEE.

关键词： Smoke detectors

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Multi-agent long-distance end-to-end indoor navigation: Using imitation learning pre-training and global map 7

Multi-agent long-distance end-to-end indoor navigation: Usin...

引用

7th International Conference on Mechanical, Electric, and Industrial engineering, MEIE 2024

作者： Jiang, Yang Yuan, Guoqin Xing, Haobo Zhao, Bin Faculty of Robot Science and Engineering Northeastern University Shenyang110169 China School of Information Science & Engineering Northeastern University Shenyang110819 China School of Information Science and Engineering Shenyang University of Technology Shenyang110870 China SIASUN Robot & Automation Co. Ltd. Shenyang110167 China

To address the navigation challenges of mobile robot swarm coordination in complex environments, this paper presents a distributed navigation approach for mobile robot swarm. The study initially formulates the problem of robot swarm navigation as a partially observable Markov decision process, employing a distributed proximal policy optimization algorithm to train decision models mapping observations to actions directly. Furthermore, it adopts imitation learning pre-training to maximize expert decisions in the initial training phase, thereby reducing exploration space. Lastly, an improved path point generation algorithm along with a spatiotemporal reachability calculation method is proposed to guide the Deep Reinforcement Learning policy in accomplishing long-distance indoor navigation. Comprehensive performance evaluations are conducted in simulated environments to compare the proposed method with existing approaches. Results demonstrate that the proposed approach effectively enhances both convergence speed and model performance, while significantly improving the navigation capabilities of robot swarms in complex environments. © Published under licence by IOP Publishing Ltd.

关键词： Mobile robots

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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...

引用

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...

引用

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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Non-Ideal sEMG Gesture Recognition Based on DANN Under Electrode Offset

Non-Ideal sEMG Gesture Recognition Based on DANN Under Elect...

引用

2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Lin, Qinggui An, Zida Fujian Zhangpu Vocational and Technical School Zhangpu Fujian China Northeastern University Faculty of Robot Science and Engineering LiaoNing ShenYang China

ISBN: (纸本)9781665481090

The traditional program control method limits the flexibility of human-computer interaction, especially in robot interaction systems that come into direct contact with the human body. sEMG signal is a relatively flexible control signal, however, sEMG signal sensors may experience positional changes due to human motion, which can affect signal acquisition. To address this issue, this paper proposes a DANN method based on adversarial thinking to solve the problem of non-ideal sEMG gesture recognition on SeNic-Main dataset. Through adversarial thinking, 8 gesture actions under electrode offset were transferred and classified, achieving a maximum improvement of 9%. The results indicate that DANN can effectively solve the problem of non ideal sEMG gesture recognition. © 2024 IEEE.

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Synchronization Control of Delayed Inertial Neural Networks with Semi-Markovian Jumping 42

Synchronization Control of Delayed Inertial Neural Networks ...

引用

42nd Chinese Control Conference, CCC 2023

作者： Cui, Junyi Cui, Huixia Zhang, Yingxin Wang, Junyi Lei, Min College of Information Science and Engineering Northeastern University Shenyang China Northeastern University Faculty of Robot Science and Engineering Shenyang China Foshan Graduate School of Innovation Faculty of Robot Science and Engineering Northeastern University Shenyang China NO.710 R&D Institute CSIC Yichang China

ISBN: (纸本)9789887581543

This article deals with the synchronization issue of the time-varying delayed inertial neural networks (INNs) with semi-Markovian jumping (SMJ). The synchronization criteria of the system can be obtained by using the Lyapunov-Krasovskii functional (LKF) and a general free-matrix-based integral inequality. In addition, the mode-dependent control gain matrices are obtained based on synchronization conditions. At last, an example is proposed to show the validity of the theoretical results. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Synchronization

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A Visual Servo Human-robot Interaction Method Based on Unidirectional Adaptive Temporal Interaction

A Visual Servo Human-Robot Interaction Method Based on Unidi...

引用

2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Zhang, Haiyan Zhang, Libo Chen, Xu Wang, Fei Northeastern University Faculty of Robot Science and Engineering Shenyang110004 China School of Astronautics Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9781665481090

Gesture recognition holds a pivotal role within the realm of human-robot interaction. Existing methods for action recognition often exhibit suboptimal real-time performance and face challenges in deployment onto edge devices for instantaneous online recognition. Moreover, human-robot interaction typically relies on wearable devices, with limited purely vision-centric interaction methodologies available. This study introduces the Unidirectional Adaptive Temporal Interaction Module (Unidirectional-ATIM), which integrates unidirectional feature fusion across temporal dimensions to capture historical frame-based temporal information. Drawing from this module, we developed a lightweight online action recognition algorithm, ATIM-Online, and established a visual servo-based framework for gesture human-robot interaction. Experimental out comes from deployments on quadruped robots and robotic arms demonstrate that our approach achieves high accuracy in gesture recognition and a high success rate in interaction. © 2024 IEEE.

关键词： Microrobots

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Sub-pixel Underwater Object Size Measurement Algorithm Based on Improved Otsu Binarization and Edge Curvature Filtering

Sub-pixel Underwater Object Size Measurement Algorithm Based...

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

作者： Chen, Chen Cao, Hangbin Liu, Jun Hu, Shaoli Ru, Jingyu Xu, Hongli Northeastern University Faculty of Robot Science and Engineering Shenyang China

ISBN: (纸本)9781665409322

In the field of target recognition, shape size is an important attribute of object. There have been many research bases on vision-based size measurement in air, but few studies have been done on underwater target size measurement. This paper mainly proposes solutions to the problems of external lighting conditions, the reflective properties of object materials, and the disturbance of robot motion. Firstly, based on the improvement of Otsu, an Otsu method suitable for bimodal or multimodal object images is proposed. Secondly, a linear interpolation denoising method based on edge contour curvature fluctuation is proposed, which combines polynomial interpolation sub-pixel technology to achieve contour edge smoothing. Finally, using polynomial interpolation sub-pixel technology, taking the image threshold segmentation effect and the measurement accuracy of the object size as the measurement standard, the underwater ball and pipeline are tested, and the measurement accuracy is improved by more than 2%, which verifies the robustness of the method. © 2022 IEEE.

关键词： Image enhancement

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