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Chinese Control Conference (CCC)

作者： Peng Yu Lijin Fang Yian Qian College of Information Science and Engineering Northeastern University Shenyang P. R. China Faculty of Robot Science and Engineering Northeastern University Shenyang P. R. China

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 (PI 2 ) 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.

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A Resource Elastic Scheduling Algorithm of Service Platform for Cloud robotics 32

A Resource Elastic Scheduling Algorithm of Service Platform ...

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第32届中国控制与决策会议

作者： Ji Peng Chu Hao Chi Jianning Jiang Jingqi Faculty of Robot Science and Engineering Northeastern University

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158556;9781728158563

Cloud computing can offer the robots high performance computing resources,and the big data stored on cloud platform can enhance significantly autonomy and collaborative capacity of ***,a platform as a service level cloud platform is essential,which should provide the ability of reusing the achievements of robotics and support that multi robots can access the cloud services on *** ensure that the robots functional services can have a better performance of computing in high or low loads,and improve the resource utilization efficiency of service platform,there is a flexible container capacity scheduling algorithm in the resource management *** the actual operation situation of different robotic services,this algorithm calculates the average load according to the CPU usage,memory usage and network usage of containers,and performs capacity elastic scheduling operation by preset upper and lower *** the period of resource flexibility expansion,users will not perceive the dynamic scheduling of functional service capacity,which ensures the stability of the platform.

关键词： Cloud robotics Docker container technology Service Platform Resource elastic scheduling Load balancing

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Conflict-Based Search and Improvement Strategies for Solving a New Lexicographic Bi-Objective Multi-Agent Path Finding Problem

Conflict-Based Search and Improvement Strategies for Solving...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Li, Siyi Li, Xingyang Zhou, Mengchu Zhao, Ziyan Liu, Shixin Northeastern University College of Information Science and Engineering Shenyang110819 China Northeastern University Foshan Graduate School of Innovation Foshan528311 China Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Macao Inst. of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macau University of Science and Technology 999078 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Multi-Agent Path Finding (MAPF) is an important problem with a variety of applications. Its aim is to find collision-free paths for agents having separate start and goal positions. This work proposes a new lexicographic bi-objective MAPF considering different task types, where agents are divided into two kinds to perform critical and acritical tasks. This is common in practical intelligent warehousing scenarios where a critical/acritical-task-performing agent (called c-agent and a-agent, respectively) may represent a full-load/no-load one or the one conducting urgent/non-urgent tasks. The primary objective is to minimize the sum-of-costs of c-agents, while the secondary objective is to minimize the sum-of-costs of a-agents. Two MAPF algorithms are modified to fit and solve the concerned problem for the first time. Moreover, four improvement strategies are embedded to the proposed algorithms and proved to be effective in solving MAPF problems with different task types. © 2022 IEEE.

关键词： Multi agent systems

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Application of an Improved Fast Corner Detection Algorithm in ORB-SLAM2

Application of an Improved Fast Corner Detection Algorithm i...

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International Conference on Autonomous Unmanned Systems, ICAUS 2021

作者： Huo, Xiaochuang Zhang, Lei Liu, Guitao Du, Zhenjun Liu, Mingmin Faculty of Robot Science and Engineering Northeastern University Shenyang China SIASUN Robot & Automation Co. Ltd. Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9789811694912

Visual SLAM provides navigation and positioning functions for mobile robots. The accuracy of positioning has a profound impact on the quality of the robot's work. It is of great significance to study SLAM positioning. ORB-SLAM2 is a visual SLAM framework with excellent positioning performance. It uses FAST algorithm as feature detection module. After a large number of experiments, this paper finds that the FAST algorithm has the defect of detecting false corners, and believes that ORB-SLAM2 has room to improve positioning accuracy. After analyzing the causes of false corners, this paper proposes an improved FAST corner detection algorithm, called FAST-Ring corner detection algorithm. The FAST-Ring corner detection algorithm constructs a new feature extraction template, and the point is considered as a corner when it meets the template. In order to verify the effectiveness of the FAST-Ring algorithm, a comparison experiment of feature extraction from a single image and a comparison experiment of ORB-SLAM2 positioning accuracy are performed in this paper. It is experimentally verified that the FAST-Ring algorithm proposed in this paper can filter out some false corner points extracted by the original FAST algorithm, and the average absolute positioning error of ORB-SLAM2 using KITTI dataset is reduced by 5 cm compared with the original ORB-SLAM2. The experimental results prove that the performance of the FAST-Ring algorithm is better than the FAST algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Abnormal Object Detection System in Cloud Server Centers Based on Transformer

Abnormal Object Detection System in Cloud Server Centers Bas...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Ziheng Li Yang Jiang Bin Zhao Faculty of Robot Science and Engineering Northeastern University Shenyang China School of Information Science & Engineering Northeastern University Shenyang China Liaoning University of Technology College of Interdisciplinary Sciences Jinzhou China SIASUN Semiconductor Equipment Co. LTD Shenyang China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

In order to solve the problem of foreign object detection in the cloud server center, this paper proposes an abnormal object detection system based on a transformer network, aiming to identify and warn abnormal objects in the computer room environment. Firstly, this paper constructs an anomaly object dataset (NEU AID) to contain information about the computer room environment, and it improves the diversity and generalization ability through data enhancement technology. Secondly, an improved Dual Flow Transformer (DFT-NET) is proposed, which can extract both global and local features to optimize the detection effect. This paper introduces transfer learning and uses the COCO data set to pre-train the network. Finally, through practical application verification, the proposed DFT-NET network model shows the high accuracy identification ability of unknown abnormal items when the inspection robot performs the abnormal items detection task in the computer room, proving the model's effectiveness and reliability in practical application scenarios.

关键词： Deep learning Adaptation models Technological innovation Accuracy Computational modeling Transfer learning Object detection Feature extraction Transformers Servers

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The Transfer Learning Method for Reducing the Influence of Electrode Shift

The Transfer Learning Method for Reducing the Influence of E...

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

作者： Shaoqing Gao Shiguang Wen Chuanjian Ren Jing Liu Qi Wu Lu Han Fei Wang Faculty of Information Science and Engineering Northeastern University ShenYang LiaoNing CHINA Faculty of Robot Science and Engineering Northeastern University ShenYang LiaoNing CHINA College of Information Science and Engineering Northeastern University ShenYang LiaoNing CHINA LiaoNing Disabled Person's Service Center Shen Yang LiaoNing CHINA

In systems based on pattern recognition using surface electromyography (sEMG) signals, there exists a problem of electrode shift during the process of donning and doffing. Directly using models trained on the source domain may result in limited prediction ability and a decrease in recognition accuracy. In this paper, a Domain-Adversarial Neural Networks(DANN) network is constructed based on CNN RES and the last layer of the feature extraction network and label predictor is fine-tuned. The experimental dataset used in the study was SeNic-main, proposed by the Shenyang Institute of Automation, Chinese Academy of sciences. The experimental results were compared with the baseline network CNN_RES(CNN_ResNet), and eight tests were conducted on data with offsets of 10 degrees and -10 degrees. The results showed that the average testing accuracy was increased by 15.47% and 13.96% respectively, compared to CNN_RES.

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ASML-VDIO: Visual-Depth-Inertial Odometry using Selected Accurate and Stable Multi-Modal Landmarks in Structural Environments

ASML-VDIO: Visual-Depth-Inertial Odometry using Selected Acc...

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Xingjian Luo Chenglin Pang Xuankang Wu Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In complex indoor structural scenes such as shopping centers and malls, camera pose estimation using pure point features is easy to fail due to the difficulty in extracting sufficient and stable point features from weak textures or dynamic environments. Recent works have attempted to address these challenges by introducing line features. However, the addition of line features increases the number of parameters and landmarks for BA (Bundle Adjustment), leading to efficiency reduction. This is a common issue in multi-modal SLAM (Simultaneous Localization And Mapping). To address this issue, this paper proposes a novel visual-depth-inertial odometry (ASML-VDIO) framework by combining RGB-D and IMU sensors. To improve the efficiency of BA, the proposed landmark classification method classifies 3D landmarks into accurate landmarks and other landmarks based on spatial consistency verification and depth range limitation. Then, accurate landmarks are fixed, and only other landmarks are optimized in the optimization of BA. Furthermore, to remove line features extracted from dynamic objects (pedestrian, shopping-car, etc), we propose a dynamic line removal method that combines geometric constraints and motion constraints of line features. Finally, the method is evaluated on public and author-collected datasets, showing competitive accuracy and robustness in complex indoor structural scenes while 71% speedup on optimization thread with same constraints.

关键词： Accuracy Simultaneous localization and mapping Dynamics Pose estimation Feature extraction Cameras Robustness Sensors Odometry Optimization

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EverySync: An Open Hardware Time Synchronization Sensor Suite for Common Sensors in SLAM

EverySync: An Open Hardware Time Synchronization Sensor Suit...

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Xuankang Wu Haoxiang Sun Rongguang Wu Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Multi-sensor fusion systems have been widely applied in various fields, including mobile robot, simultaneous localization and mapping (SLAM), and autonomous driving. For a tightly coupled multi-sensor fusion system, strict time synchronization between sensors will improve the accuracy of the system. However, there is currently a lack of open-source and general-purpose hardware synchronization systems for Cameras, IMUs, LiDARs, GNSS/RTK in the academic community. Therefore, we propose EverySync, an open hardware time synchronization system to address this gap. The synchronization accuracy of the system was evaluated through multiple experiments, achieving an accuracy of less than 1 ms. And, real-world experiments proved that hardware time synchronization improves the accuracy of the SLAM system. This open-source system is available on GitHub.

关键词： Simultaneous localization and mapping Accuracy Laser radar robot vision systems Sensor fusion Cameras Hardware Sensor systems Synchronization Software development management

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FE-Fusion-VPR: Attention-based Multi-Scale Network Architecture for Visual Place Recognition by Fusing Frames and Events

arXiv

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arXiv 2022年

作者： Hou, Kuanxu Kong, Delei Jiang, Junjie Zhuang, Hao Huang, Xinjie Fang, Zheng Faculty of Robot Science and Engineering Northeastern University Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

Traditional visual place recognition (VPR), usually using standard cameras, is easy to fail due to glare or high-speed motion. By contrast, event cameras have the advantages of low latency, high temporal resolution, and high dynamic range, which can deal with the above issues. Nevertheless, event cameras are prone to failure in weakly textured or motionless scenes, while standard cameras can still provide appearance information in this case. Thus, exploiting the complementarity of standard cameras and event cameras can effectively improve the performance of VPR algorithms. In the paper, we propose FE-Fusion-VPR, an attention-based multi-scale network architecture for VPR by fusing frames and events. First, the intensity frame and event volume are fed into the two-stream feature extraction network for shallow feature fusion. Next, the three-scale features are obtained through the multi-scale fusion network and aggregated into three sub-descriptors using the VLAD layer. Finally, the weight of each sub-descriptor is learned through the descriptor re-weighting network to obtain the final refined descriptor. Experimental results show that on the Brisbane-Event-VPR and DDD20 datasets, the Recall@1 of our FE-Fusion-VPR is 29.26% and 33.59% higher than Event-VPR and Ensemble-EventVPR, and is 7.00% and 14.15% higher than MultiRes-NetVLAD and NetVLAD. To our knowledge, this is the first end-to-end network that goes beyond the existing event-based and frame-based SOTA methods to fuse frame and events directly for VPR. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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Geometry-aided Underwater 3D Mapping Using Side-scan Sonar

Geometry-aided Underwater 3D Mapping Using Side-scan Sonar

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IEEE/RSJ International Conference on Intelligent robots and Systems (IROS)

作者： Yiqiao Yang Chenglin Pang Chengdong Wu Zheng Fang Faculty of Robot Science and Engineering Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In recent years, the interest in underwater exploration with Autonomous Underwater Vehicles (AUVs) equipped with side-scan sonars (SSS) has grown considerably. However, state-of-the-art SSS Simultaneous Localization and Mapping (SLAM) systems encounter challenges in data association across large viewpoint changes. Additionally, these systems assume that the seabed is a flat surface, leading to significant mapping error in uneven underwater terrains. To address these challenges, we propose a framework that leverages the side-scan sonar geometry to facilitate data association and improve mapping accuracy. The framework begins with a preprocessing module that extracts feature points and provides initial estimates of the elevation angles of the landmarks. Then, a non-consecutive data association module applies epipolar line search to establish correspondences between the current and historical frames. Finally, the mapping module uses side-scan sonar bundle adjustment to recover the positions of the landmarks. The proposed method is evaluated using an underwater terraced fields dataset. Our method achieves over 90% matching rate and reduces the average mapping error from 3.799 to 0.134.

关键词： Geometry Bundle adjustment Autonomous underwater vehicles Simultaneous localization and mapping Accuracy Geometric modeling Feature extraction Side-scan sonar Optimization Intelligent robots

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