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第35届中国控制与决策会议

作者： Ji Peng Gao Wei Li Ming Li Xiaoqiang Faculty of Robot Science and Engineering Northeastern University College of Information Science and Engineering Northeastern University

It is a research hotspot in the field of robotics to improve the running efficiency of robots based on the powerful storage and computing resources of the cloud ***,the traditional cloud computing technology is too centralized to adapt to the military,rescue,and other complex environments,so the self-organizing cloud robot technology that can achieve resource sharing and collaborative computing has received more and more *** self-organizing cloud robot has the characteristics of high stability,strong robustness,fast running speed,and strong computing *** meet the requirements of self-organizing cloud robots for cooperative work and shared computing,a resource optimization scheduling algorithm for a self-organizing robot platform is proposed in this *** the algorithm,a self-organizing network model is built based on the Raft algorithm to realize the interaction and sharing of computing resources between *** this basis,an optimization model for computing resource scheduling under time constraints that are based on the DQN algorithm is *** construction of the reward function and the design of the neural network model are used to achieve effective resource allocation and scheduling of self-organizing cloud robot systems for different task *** evaluate the performance of the proposed algorithm the simulation experiments are *** results show the proposed algorithm is more effective in resource scheduling compared with other current algorithms.

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AI-Based Maize and Weeds Detection on the Edge with CornWeed Dataset 18

AI-Based Maize and Weeds Detection on the Edge with CornWeed...

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18th Conference on Computer science and Intelligence Systems, FedCSIS 2023

作者： Iqbal, Naeem Manss, Christoph Scholz, Christian Konig, Daniel Igelbrink, Matthias Ruckelshausen, Arno Dfki Plan-based Robot Control Osnabrueck Germany Dfki Marine Perception Oldenburg Germany University of Applied Sciences Osnabrueck Faculty of Engineering and Computer Science Osnabrueck Germany

ISBN: (纸本)9788396744784

Artificial intelligence (AI) is used more heavily in agricultural applications. Yet, the lack of wireless-fidelity (Wi-Fi) connections on agricultural fields makes AI cloud services unavailable. Consequently, AI models have to be processed directly on the edge. In this paper, we evaluate state-of-the-art detection algorithms for their use in agriculture, in particular plant detection. Thus, this paper presents the CornWeed data set, which has been recorded on farm machines, showing labelled maize crops and weeds for plant detection. The paper provides accuracies for the state-of-the-art detection algorithms on the CornWeed data set, as well as frames per second (FPS) metrics for the considered networks on multiple edge devices. Moreover, for the FPS analysis, the detection algorithms are converted to open neural network exchange (ONNX) and TensoRT engine files as they could be used as future standards for model exchange. © 2023 Polish Information Processing Society.

关键词： Data acquisition

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Subconscious robotic Imitation Learning

arXiv

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arXiv 2024年

作者： Xie, Jun Wang, Zhicheng Tan, Jianwei Lin, Huanxu Ma, Xiaoguang Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China College of Information Science and Engineering Northeastern University Shenyang110819 China College of Information Science and Engineering Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China Foshan Graduate School Northeastern University Foshan528311 China

Although robotic imitation learning (RIL) is promising for embodied intelligent robots, existing RIL approaches rely on computationally intensive multi-model trajectory predictions, resulting in slow execution and limited real-time responsiveness. Instead, human beings subconscious can constantly process and store vast amounts of information from their experiences, perceptions, and learning, allowing them to fulfill complex actions such as riding a bike, without consciously thinking about each. Inspired by this phenomenon in action neurology, we introduced subconscious robotic imitation learning (SRIL), wherein cognitive offloading was combined with historical action chunkings to reduce delays caused by model inferences, thereby accelerating task execution. This process was further enhanced by subconscious downsampling and pattern augmented learning policy wherein intent-rich information was addressed with quantized sampling techniques to improve manipulation efficiency. Experimental results demonstrated that execution speeds of the SRIL were 100% to 200% faster over SOTA policies for comprehensive dual-arm tasks, with consistently higher success rates. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Human Target Detection and Tracking Algorithm Based on Improved YOLOv7

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

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

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

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.

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A method of relay UAV deployment for emergency communication scenarios in disaster areas

A method of relay UAV deployment for emergency communication...

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2024 China Automation Congress, CAC 2024

作者： Tao, Cancan Liu, Bowen Han, Yi Chen, Qiangqiang Ye, Jun Zhong, Ronglin School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Parallel Robot and Mechatronic System Laboratory of Hebei Province Yanshan University Qinhuangdao066004 China Faculty of Science Kunming University of Science and Technology Kunming650500 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China School of Information Science and Technology University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9798350368604

This article proposes a relay deployment method that uses UAVs to provide communication services to ground users, especially for emergency communication scenarios in disaster ***, a two-layer network communication model is constructed for emergency communication scenarios in disaster areas, and the relay deployment problem is transformed into a multi-objective optimization problem. On this basis, a detailed analysis of the problem is conducted, and a UAV relay deployment method based on the K-means++ algorithm is proposed. The two indicators of the number of UAVs used and the network communication performance are considered in solving the problem. Simulation results show that the proposed method can achieve a balance between the number of relay UAVs used and the achieved network performance. © 2024 IEEE.

关键词： Multiobjective optimization

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Utilizing Large Language Models Enhanced by Chain-of-Thought for the Diagnosis of Typical Medical Cases 10th

Utilizing Large Language Models Enhanced by Chain-of-Though...

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10th China Health Information Processing Conference, CHIP 2024

作者： Liu, Jiqiang Liu, Chenyang Faculty of Robot Science and Engineering Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9789819642977

In the past two years, the large language model has set off a new wave of research in the field of natural language processing, showing the ability of general-purpose artificial intelligence, which has been widely concerned by the industry. With the rapid development of medical vertical large model, its potential in clinical application has been paid more and more attention. However, there are gaps in the diagnosis of typical medical records. In order to continuously improve the application effect of medical large model in actual clinical scenarios and accelerate the implementation of medical large model, this paper describes the specific situation of our participation in the 10th China Conference on Health Information Processing (CHIP 2024). We used the improved LoRA method for fine tuning, and then used the Chain-of-Thought method for post-processing in the reasoning stage to improve performance. The experimental results show that the F1 score of our method in the final second round of evaluation reaches 0.9356, ranking second, which effectively verifies the generalization and robustness of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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A Face Super-Resolution Reconstruction Algorithm Based on Residual Estimation

A Face Super-Resolution Reconstruction Algorithm Based on Re...

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IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Xiaosheng Yu Guanglai Liu Zi Teng Xiaoqiang Li Jubo Chen Faculty of Robot Science and Engineering Northeastern University Shenyang Liaoning China College of Information Science and Engineering Northeastern University Shenyang Liaoning China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

Face super resolution can greatly improve the performance of various facial analysis applications, such as face recognition and facial expression analysis. This paper introduces a facial super-resolution network model that utilizes residual estimation for enhancing image quality. The model engages a two-stage reconstruction process to generate super resolution face images, which effectively simplifies the learning complexities of the network. Moreover, the model incorporates prior facial information into the loss function to mitigate the influence of the background on the facial region within the image. This incorporation facilitates a more accurate reconstruction of super resolution facial images. The robustness and effectiveness of this facial super-resolution network based on residual estimation are evaluated through both quantitative and qualitative assessments, employing some classical facial image datasets.

关键词： Image quality Face recognition Image edge detection Superresolution Estimation Reconstruction algorithms Robustness Intelligent systems Image reconstruction Faces

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State-of-the-art Research in Perception and Control of Lower Limb Exoskeletons: A Systematic Review 3

State-of-the-art Research in Perception and Control of Lower...

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3rd International Conference on Service robotics, ICoSR 2024

作者： Luo, Xiaofeng Ma, Yang Cai, Zhibin Zhou, Tianqi Liu, Xiaoyu Long, Yi Weapon Equipment Research Institute China Ordnance Industries Group Beijing China School of Mechanical Engineering and Automation Northeastern University Shenyang China Northeastern University Faculty of Robot Science and Engineering Shenyang China Foshan Graduate School of Innovation Northeastern University Foshan China

ISBN: (纸本)9798350367119

The lower limb exoskeleton is an intelligent robot worn by the human body and moves in coordination with the human limbs. It can enhance the human body's weight-bearing capacity, maneuverability and operation capabilities. Perception and control, as the brain of the exoskeleton robot, directly determine its collaborative performance and auxiliary effect with the main body. However, the current lower limb exoskeleton has not been widely adopted. One of the reasons is that the 'brain' of the exoskeleton robot is not intelligent enough. Therefore, this article reviewed relevant literature on lower limb exoskeleton sensing and control technology in the past five years, and studied the impact of different sensing methods and control strategies on exoskeleton performance. In addition, this article discusses the current challenges of lower limb exoskeletons and provides some innovative insights into future research directions to promote the further development of lower limb exoskeleton technology. © 2024 IEEE.

关键词： Exoskeleton (robotics)

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RPANet: A Residual Pyramid Semantic Segmentation Network Based on Atrous Convolution

RPANet: A Residual Pyramid Semantic Segmentation Network Bas...

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Chinese Control and Decision Conference, CCDC

作者： Suyi Liu Chengdong Wu Fang Xu Jianning Chi Xiaosheng Yu Longxing Wei Faculty of Robot Science and Engineering Northeastern University Shenyang China Academia Sinica Shenyang Siasun Robot Automation Co. Ltd Shenyang China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Semantic segmentation is a challenging task to achieve accurate pixel classification. Recent studies witnessed that residual networks can better solve problems such as gradient vanishing and exploding in neural network backpropagation. However, when objects are obstructed or certain target sizes are small, it can lead to insufficient feature extraction ability of the residual network. In this paper, we propose a residual pyramid network based on atrous convolution (RPANet) to obtain better semantic segmentation results and improve segmentation network efficiency. Specifically, we first put forward a novel residual pyramid architecture, which integrates contextual information with the relationship features between pixels, and outputs multi-level and different scale features. In addition, we propose multi-scale atrous convolutions, which can not only capture long-range contexts information and enlarge the effective receptive field, but also the size of the feature map is not reduced by the residual connection. Extensive experiments demonstrate the effectiveness and superiority of our method on Cityscapes and CamVid datasets. We demonstrate that our method can improve the semantic segmentation performance reduce network complexity.

关键词： Visualization Accuracy Convolution Semantic segmentation Semantics Feature extraction Real-time systems

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Semi-Autonomous Navigation Control System of Intelligent Wheelchair Based on Asynchronous SSVEP-BCI 11

Semi-Autonomous Navigation Control System of Intelligent Whe...

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11th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2021

作者： Ping, Jingyu Wang, Fei Xu, Zongfeng Bi, Jinying Xiao, Ling Northeastern University Faculty of Robot Science and Engineering Shenyang110169 China Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China College of Information Science and Engineering Northeastern University Shenyang110169 China

ISBN: (纸本)9781665425278

Intelligent wheelchairs based on braincomputer interface (BCI) have been greatly developed, but there are still many drawbacks such as low recognition accuracy, poor control flexibility, and few control commands. To overcome these shortcomings, a semi-Autonomous navigation control system of intelligent wheelchair based on asynchronous steady-state visual evoked potential (SSVEP) is proposed in this paper. Firstly, a two-layer human-computer interaction interface is designed. The first layer is a mode selection interface, including destination mode and roaming mode, and the second layer is a destination and direction selection interface. Secondly, an idle state detection method based on spectral energy of Electroencephalogram (EEG) signals is proposed. Control commands will be recognized with Multivariate Synchronization Index (MSI) only when the control state is detected. Finally, an intelligent wheelchair platform with navigation is set up for online verification. The average recognition accuracy of five subjects is 91.58%, and all subjects arrived at the destinations. The final experimental results show that the system has high stability and flexibility and can meet people's autonomous and multi-command control requirements. © 2021 IEEE.

关键词： Navigation

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