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International Conference on Autonomous Unmanned Systems, ICAUS 2021

作者： Ru, Jingyu Hao, Dongqiang Yu, Shuangjiang Liu, Hao Xu, Hongli Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China School of Information Science and Engineering Northeastern University Shenyang110819 China

ISBN: (纸本)9789811694912

Cooperative detection of fixed detection sequence positions in complex underwater areas through multi-agent combination is an indispensable means to complete special underwater detection tasks. Studying the scheduling and matching underwater robot resources to meet the requirements of environmental detection tasks under the conditions of meeting task and detection requirements is a research hotspot in the field of multi-agent task allocation. In traditional task allocation process, the detection dimension is single, and the fixed detection sequence is disre garded. This work proposes a novel multidimensional resource scheduling allocation algorithm (CR-C-NSPSO) for heterogeneous agents based on improved NSPSO. The algorithm is composed of a solution set conflict resolution algorithm and a collision optimization strategy module. It can establish a mathematical expression model that meets the needs of a fixed sequence of tasks and builds a solution space for each task while resolving the needs of task conflicts in the same time and space. On the basis of meeting the task priority, the impact of resource scheduling and allocation conflicts caused by optimized search is weakened. Finally, simulations verify the feasibility and effectiveness of the algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multi agent systems

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A Face Super-Resolution Reconstruction Algorithm Based on Residual Estimation 14

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

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

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

ISBN: (纸本)9798331506056

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. © 2024 IEEE.

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Research on Reflector Navigation Algorithm and Optimization Smoothing Critical Path Trajectory

Research on Reflector Navigation Algorithm and Optimization ...

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2022 Chinese Automation Congress, CAC 2022

作者： Bin, Zhao Cheng-Dong, Wu Yang, Jiang Ming-Hao, Yu Ruo-Huai, Sun Northeastern University School of Information Science & Engineering Shenyang110819 China Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China

ISBN: (纸本)9781665465335

For mobile robots working in dense environments, navigation and path planning are crucial to understanding and reproducing human wayfinding thinking patterns. Firstly, this paper analyzed a hybrid SLAM navigation algorithm based on the reflector and laser constructed by the grid map method. Secondly, in the case of Kalman filter localization loss, this paper uses the generalized least squares inverse method to solve the nonlinear equations of the reflector to calibrate the localization information. Thirdly, the report used the RDP(Ramer-Douglas-Peucker algorithm) algorithm to reduce the critical path points of the trajectory, which solves the problem of many path points in the traditional JPS algorithm. At the same time, used the optimal boundary NURBS curve algorithm to improve the robot trajectory quality. Finally, in large-scale simulation and physical environment, the navigation algorithm based on the reflector and the optimized smooth critical path trajectory algorithm was verified, proving the algorithm's effectiveness and accuracy in the complex domain. © 2022 IEEE.

关键词： Motion planning

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Dynamic 3D Path Planning for UAV Based on Enhanced Grey Wolf Optimization Algorithm 3rd

Dynamic 3D Path Planning for UAV Based on Enhanced Grey Wolf...

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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Xu, Hongli Liu, Yubo Ru, Jingyu Faculty of Robot Science and Engineering Northeastern University Shenyang110169 China College of Information Science and Engineering Northeastern University Shenyang110819 China

ISBN: (纸本)9789819710904

The enhanced grey wolf optimization algorithm is proposed for the path planning problem of UAV in a multi-obstacle and dynamic obstacle environment. Firstly, the population is initialized by combining Tent chaos mapping, secondly, a differential evolution strategy is adopted for the leader grey wolf individuals, and finally, the enhanced grey wolf optimization algorithm and the artificial potential field method are fused to achieve the goal of avoiding dynamic obstacles. The results are tested under the dynamic obstacle model, and it is proved that the enhanced grey wolf optimization algorithm has excellent global search capability and better robustness under the complex environment. The method introduces innovative ideas to the domain of dynamic obstacle avoidance in path planning. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Unmanned aerial vehicles (UAV)

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Edge-guided nonlinear dynamic convolution network for lightweight semantic segmentation 2

Edge-guided nonlinear dynamic convolution network for lightw...

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2nd International Conference on Physics, Photonics, and Optical engineering, ICPPOE 2023

作者： Zhang, Chunyu Xu, Fang Wu, Chengdong Zhang, Chenghao Faculty of Robot Science and Engineering Northeastern University Shenyang China Shenyang Siasun Robot and Automation Company Ltd. Shenyang110168 China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9781510674684

As the demand for autonomous driving and robot vision arises, semantic segmentation has developed rapidly. Semantic segmentation provides the means for machines to understand the environment. However, current methods face a balance issue between segmentation quality and computational resources. Our proposed edge-guided non-linear dynamic convolutional network (ENNet) achieves real-time and accurate semantic segmentation. The core of our network is a novel non-linear, dynamic combination (Nd-conv) that allows for a non-linear, dynamic combination (Nd-conv) of multiple convolution weights, improving the convolution's encoding ability while maintaining a low computational burden. The Non-linear Dynamic Convolutional Modules (NDCM) is introduced to enhance the segmentation accuracy of the network while maintaining efficiency. The Multi-stage Feature Fusion Module (MFFM) is also introduced to fuse low-level details and high-level semantic information to improve the segmentation accuracy of the network. Experiments conducted on a 1080Ti GPU show that our model achieves 74.6 mIOU on the Cityscapes dataset while being able to perform inference at 202 FPS. Our network design has achieved an optimal balance between speed and accuracy. © COPYRIGHT 2024 SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Convolution

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

Synchronization Control of Delayed Inertial Neural Networks ...

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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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Design and Control of a Quasi-direct Drive Actuated Knee Exoskeleton

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Journal of Bionic engineering 2022年第3期19卷 678-687页

作者： Yi Long Yajun Peng Foshan Graduate School of Northeastern University Foshan528000China Faculty of Robot Science and Engineering Northeastern UniversityShenyang110000China Zhongshan Walkbest Intelligent Medical Device Co.Ltd Zhongshan528400China

This paper describes the design and control of a portable and lightweight knee exoskeleton for people with knee *** knee exoskeleton is designed based on our custom quasi-direct drive actuation composed of a DC motor unit and a transmission mechanism encompassing three *** online gait generation method based on gait step calculation and direct measurement using Inertial Measurement Unit(IMU)sensors is proposed to provide continuous assistance during *** on the generated gait trajectory online,Active Disturbance Rejection Control(ADRC)incorporating the feedforward compensation approach is proposed to help the human leg move in consideration of external *** developed knee exoskeleton has been employed successfully to assist impaired users with knee dysfunction to walk in a health recovery *** experimental results indicate that the online gait generation method and the proposed control method are suitable for the knee *** maximum value of the target knee angular position is approximately 50°and the mean of the control torque for the knee joint is located in the interval of[−2 Nm,4 Nm].The developed knee exoskeleton has the potential to regain normal gait to improve strength and endurance during walking in their activities of daily life.

关键词： Exoskeleton Knee dysfunction Quasi-direct drive Active disturbance rejection control Gait generation

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A self-supervised deep learning framework for seismic facies segmentation

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Expert Systems with Applications 2025年 288卷

作者： Ming Li Xue-song Yan Qing-hua Wu School of Computer Science China University of Geosciences Wuhan 430078 China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan 430074 China Faculty of Computer Science and Engineering WuHan Institute of Technology Wuhan 430205China Hubei Provincial Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan 430205 China

Seismic facies classification plays a vital role in subsurface exploration and geological interpretation, but the dependence on manually labeled seismic data can hinder scalability and efficiency. In this paper, we propose SSFS (Self-supervised Seismic Facies Segmentation), a novel framework that combines deep clustering with self-supervised learning to achieve seismic facies segmentation without the need for labeled data. Traditional methods rely on costly manual labeling or simplistic clustering, while supervised deep learning struggles with limited labeled data. SSFS addresses these limitations by deep clustering and self-supervised learning. The SSFS framework consists of three key phases: (1) Deep Clustering, where seismic data is sliced into overlapping patches of varying sizes and features are extracted using an attention-based auto-encoder, followed by clustering the features in latent space using K-means; (2) Cluster Merging, where initial clusters are iteratively merged based on cosine similarity to refine the clustering results; and (3) Facies Segmentation, where the merged clusters serve as pseudo-labels for training a segmentation model to refine the facies classification. On the Netherlands F3 dataset, SSFS achieves an average mean Intersection over Union (mIoU) of 83.76% and average pixel accuracy (PA) of 88.43% on four test seismic profiles. Moreover, we show that SSFS can enhance supervised seismic facies analysis by initializing popular segmentation models with the pre-trained weights from the SSFS framework. Quantitative validations across two datasets demonstrate that SSFS-initialized models consistently improve class accuracy (CA) by 2–5% compared to models trained from scratch for minority facies classes. The effectiveness of SSFS is quantitatively validated through several metrics, highlighting its potential for both self-supervised and supervised seismic facies classification tasks. Our results suggest that SSFS provides a promising solution

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Trajectory Planning of robot End-Effector Position and Orientation Based on Cubic Spline 5

Trajectory Planning of Robot End-Effector Position and Orien...

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5th World robot Conference Symposium on Advanced robotics and Automation, WRC SARA 2023

作者： Liu, Yang Du, Zhenjun Liu, Mingmin Tao, Yongliang Li, Chenpeng Northeastern University Faculty of Robot Science and Engineering Liaoning Shenyang110169 China Siasun Robot & Automation Co. Ltd Liaoning Shenyang110168 China

ISBN: (纸本)9798350307320

Trajectory planning for the end effector of industrial robots has been a hot topic in the field of robotics. A continuous and smooth trajectory of the end effector's pose is beneficial for avoiding unnecessary impacts and vibrations, thereby improving the performance and lifespan of industrial robots. In recent years, researchers have predominantly focused on the trajectory planning of multi-directional variations in orientation using quaternions, while giving less attention to the coordinated planning of position and orientation. To address this issue, this paper proposes an efficient trajectory planning method based on improved spline curve interpolation. The orientation of key pose points, characterized by multi-directional variations, is represented using quaternions. By employing a quaternion dimension reduction algorithm, these quaternions are transformed into vector forms in three-dimensional space, allowing for unified representation of position and orientation within the same dimension. Taking the chord length between key pose points as a parameter, the position and orientation components are subjected to cubic spline curve interpolation with chord length as the independent variable. The proposed method is applicable for coordinated planning of position and orientation trajectories, ensuring high-order continuity of the pose trajectory. The effectiveness and rationality of the proposed method are validated through a set of key pose points featuring multi-directional variations. Finally, a comparative analysis is conducted between this set of key pose points and existing trajectory planning algorithms. Experimental results demonstrate that the proposed method not only guarantees high-order continuity of the trajectory but also exhibits higher smoothness. © 2023 IEEE.

关键词： Trajectories

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Semantic Range Image for Loop Closure of 3D Lidar SLAM 13

Semantic Range Image for Loop Closure of 3D Lidar SLAM

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

作者： Li, Fang Shen, Yehui Zou, Fengshan Du, Zhenjun Liu, Mingmin Northeastern University Faculty of Robot Science and Engineering Liaoning Shenyang110169 China SIASUN Robot & Automation Co. Ltd Liaoning Shenyang110168 China

ISBN: (纸本)9798350315196

The purpose of SLAM loop closure detection is to reduce the cumulative error in the system by identifying the same scene that the robot passed through. Traditional loop detection methods detect loops based on physical information. These methods use basic physical features to identify loop closures and pay less attention to the role of semantic information as high-level information in loop closure detection. This paper regards semantic information as one of the criteria for confirming loop closure, so that semantic information can be deeply involved in the process of loop closure detection. We construct a global descriptor based on range image and semantic information, which is called SeRI. The descriptor encodes the semantic and geometric features of the surrounding environment. Loop closure candidates are determined by matching between pairs of semantic range images. We evaluate our method on the KITTI dataset. Experiments have shown that the proposed method can effectively identify loop closure candidates correctly with high precision and recall rate. © 2023 IEEE.

关键词： Semantics

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