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arXiv 2023年

作者： Wu, Yanmin Zhang, Yunzhou Zhu, Delong Deng, Zhiqiang Sun, Wenkai Chen, Xin Zhang, Jian Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China College of Information Science and Engineering Northeastern University Shenyang110819 China The Department of Electronic Engineering The Chinese University of Hong Kong Shatin N.T. Hong Kong The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

Object SLAM is considered increasingly significant for robot high-level perception and decision-making. Existing studies fall short in terms of data association, object representation, and semantic mapping and frequently rely on additional assumptions, limiting their performance. In this paper, we present a comprehensive object SLAM framework that focuses on object-based perception and object-oriented robot tasks. First, we propose an ensemble data association approach for associating objects in complicated conditions by incorporating parametric and nonparametric statistic testing. In addition, we suggest an outlier-robust centroid and scale estimation algorithm for modeling objects based on the iForest and line alignment. Then a lightweight and object-oriented map is represented by estimated general object models. Taking into consideration the semantic invariance of objects, we convert the object map to a topological map to provide semantic descriptors to enable multi-map matching. Finally, we suggest an object-driven active exploration strategy to achieve autonomous mapping in the grasping scenario. A range of public datasets and real-world results in mapping, augmented reality, scene matching, relocalization, and robotic manipulation have been used to evaluate the proposed object SLAM framework for its efficient performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Augmented reality

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The Network of Attention-Aware Multimodal fusion for RGB-D Indoor Semantic Segmentation Method

The Network of Attention-Aware Multimodal fusion for RGB-D I...

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第34届中国控制与决策会议

作者： Qiankun Zhao Yingcai Wan Lijin Fang Huaizhen Wang Faculty of Robot Science and Engineering Northeastern University Institute of Shandong New Generation Information Industry Technology Inspur group

In recent years,convolutional neural networks have effectively improved the accuracy of semantic segmentation ***,semantic segmentation of indoor scenes is still a challenging problem due to the complexity of indoor *** the advent of depth sensors,the use of depth information to improve semantic segmentation has been *** of the previous studies simply use equal-weight stitching or summation operations to fuse RGB features and depth features,failing to make full use of the complementary information between RGB features and depth *** this paper,we propose the network of attention-aware multimodal fusion for RGB-D indoor semantic segmentation *** designing an attention-aware multimodal fusion module,the multilevel RGB features and depth features are effectively *** cross-modal attention mechanism is also introduced in the multimodal fusion module,and the features are fused at different scales so that RGB features and depth features can guide and optimize each other using complementary information,thus extracting feature representations rich in spatial location *** method can effectively promote the synergistic interaction of multimodal features and further improve the effect of semantic *** results on publicly available datasets SUN RGB-D,NYU Depth v2 and ScanNet show that our designed algorithm outperforms other RGB-D image semantic segmentation algorithms.

关键词： Convolutional neural network Multimodal fusion Attention-aware mechanism Deep learning

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Linear Active Disturbance Rejection Anti-Backlash Control for Dual-Motor Coaxial Driving robotic Joint

Linear Active Disturbance Rejection Anti-Backlash Control fo...

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第34届中国控制与决策会议

作者： Jiqian Xu Lijin Fang Qiankun Zhao Huaizhen Wang Faculty of Robot Science and Engineering Northeastern University Institute of Shandong New Generation Information Industry Technology Inspur group

This paper describes a linear active disturbance rejection anti-backlash controller for a dual-motor coaxial driving robotic *** type of robotic joint is an over-actuated system that dual motors collaboratively drive load each ***,a dual-motor driving model is built considering backlash and then a simplified control-orient model is proposed based on the principle of dual-motor anti-backlash ***,a linear active disturbance rejection control-based dual-motor position tracking framework is developed in accordance with the control-orient ***,in order to deal with backlash coupling with unknown external torque,an admittance control-based adaptive position compensator is *** other control strategies,it practically achieves high tracking performance without requiring any backlash and external torque *** effectiveness and simplicity of proposed control strategy is verified through comparative experiments.

关键词： Anti-backlash Dual-motor Linear active disturbance rejection control Admittance control robotic joint

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A New Non-Line-of-Sight Localization Algorithm for Wireless Sensor Network 6

A New Non-Line-of-Sight Localization Algorithm for Wireless ...

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Fan, Zhibo Chu, Hao Wang, Fei Lu, Jian College of Information Science and Engineering Northeastern University Shenyang China Northeastern University Faculty of Robot Science and Engineering Shenyang China

ISBN: (纸本)9781728186351

In recent years, node localization technology based on Wireless sensor network (WSN) plays an increasingly important role in production and life. The accuracy of node localization largely depends on the distance measurement between the target node and the beacon node. When there are many obstacles, the signal transmission is greatly disturbed, and the positioning accuracy is reduced. To solve this problem, a new non-line-of-sight (NLOS) localization method is proposed. Firstly, a statistical method based on Anderson-Darling (AD) test is employed to test the signal propagation state. After that, we construct the location function with constraints by LOS measurements and NLOS measurements. Next, we propose an improved particle swarm optimization (PSO) algorithm to determine the target location. Simulation results show that the proposed algorithm can effectively improve the positioning accuracy of the target node in complex environment. © 2020 IEEE.

关键词： Particle swarm optimization (PSO)

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STTracker: Spatio-Temporal Tracker for 3D Single Object Tracking

arXiv

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arXiv 2023年

作者： Cui, Yubo Li, Zhiheng Fang, Zheng Faculty of Robot Science and Engineering Northeastern University Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Ministry of Education Shenyang110819 China

3D single object tracking with point clouds is a critical task in 3D computer vision. Previous methods usually input the last two frames and use the predicted box to get the template point cloud in previous frame and the search area point cloud in the current frame respectively, then use similarity-based or motion-based methods to predict the current box. Although these methods achieved good tracking performance, they ignore the historical information of the target, which is important for tracking. In this paper, compared to inputting two frames of point clouds, we input multi-frame of point clouds to encode the spatio-temporal information of the target and learn the motion information of the target implicitly, which could build the correlations among different frames to track the target in the current frame efficiently. Meanwhile, rather than directly using the point feature for feature fusion, we first crop the point cloud features into many patches and then use sparse attention mechanism to encode the patch-level similarity and finally fuse the multi-frame features. Extensive experiments show that our method achieves competitive results on challenging large-scale benchmarks (62.6% in KITTI and 49.66% in NuScenes). © 2023, CC BY.

关键词： Encoding (symbols)

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A Broadband Hybrid Energy Harvester with Displacement Amplification Decoupling Structure for Ultra-Low Vibration Energy Harvesting

SSRN

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SSRN 2023年

作者： Cong, Moyue Gao, Yongzhuo Wang, Weidong He, Long Mao, Xiwang Long, Yi Dong, Wei School of Mechanical and Electrical Engineering Harbin Institute of Technology Harbin150001 China Zhiyuan Research Institute Hangzhou310024 China Faculty of Robot Science and Engineering Northeastern University Foshan528311 China

Abnormal vibration of electrical equipment requires a large number of distributed sensors to monitor. However, there are issues that require maintenance and power supply. This work proposes a working mode for harvesting the low-grade vibration energy of electrical equipment, which can replace vibration sensors and achieve self-powered monitoring of equipment vibration. Additionally, we present a novel energy harvesting module that solves the issue of converting random vibration into linear vibration. The introduction of laminated hinges achieves large displacement output under small amplitude vibration and energy harvesting from random low amplitude vibration within a bandwidth of 2–23 Hz. The impact of geometric parameters on the proposed system is evaluated through parameter analysis. Experimental findings reveal that the device can achieve a maximum power output of 17.84 mW at an ultra-low 0.05g acceleration., resulting in a peak power density of approximately 44.87 W/m3. Compared to traditional methods, peak power has increased by 266.2%. Wireless vibration online monitoring systems for visualizing vibration status are created by combining the equipment with repeaters and phones. This research introduces a unique strategy for remote online vibration monitoring that may be used in distributed sensor systems to identify malfunctioning machinery. © 2023, The Authors. All rights reserved.

关键词： Energy harvesting

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Asymmetry Stagger Array Structure Ultra-Wideband Vibration Harvester Integrating Magnetically Coupled Nonlinear Effects

SSRN

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SSRN 2023年

Traditional energy harvesters often have limitations in terms of bandwidth and power output, resulting in poor performance. This study reports a multi-strategy ultra-wideband energy harvesting device that utilizes an asymmetry, stagger array, magnetic coupling, and nonlinearity strategies to achieve high power output over an ultra-wideband frequency range without the need for external power input. The energy harvesting device consists of a base, two elastic beams, and two sets of asymmetric palm-shaped piezoelectric cantilever beam structures, each with a magnet attached at the tip. By reasonably arranging the palm-shaped mechanism and magnets, a magnetic coupling effect is introduced to achieve high power density output at non-resonant frequencies. Numerical analysis is conducted to evaluate the performance of the proposed structure, assess the influence of structural parameters, and establish a dynamic model to analyze the energy harvesting device. Experimental results demonstrate that the structure achieves a maximum power output of 60.49 mW at 19.9 Hz and 0.49m/s2, with a peak power density of approximately 8.065×103W/m3. Within an ultra-wideband frequency range that spans 6 Hz to 64.2 Hz, the energy harvester maintains an output voltage which is more than 5 V. Furthermore, temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the energy harvesting device, eliminating the need for lithium batteries and ensuring stable signal transmission. The device can be utilized for condition monitoring in any unstable vibration environment, contributing to the realization of distributed monitoring in the Internet of Things (IoT). © 2023, The Authors. All rights reserved.

关键词： Electric power transmission

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Advancing Sustainable Energy: Analyzing Stochastic Algorithms and Q-Learning for Wind Farm Layout Optimization

Advancing Sustainable Energy: Analyzing Stochastic Algorithm...

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Artificial Intelligence, robotics and Control (AIRC), International Conference on

作者： Mohamed Elsheikh Amr Yonis Jessica Magdy Omar M. Shehata Computer Science Department Faculty of Media Engineering and Technology German University in Cairo Egypt Mechatronics Engineering Department Faculty of Engineering and Materials Science German University in Cairo Egypt Multi-Robot Systems (MRS) Research Group Cairo Egypt

ISBN: (数字)9798350385076

ISBN: (纸本)9798350385083

This paper presents a comprehensive study on the optimization of wind farm layouts, a critical aspect of enhancing the efficiency and effectiveness of wind energy production. We delve into various optimization techniques, including Simulated Annealing, Genetic Algorithm, Particle Swarm Optimization, Moth Flame Optimization, and Deep Q-Learning. Each optimization technique is explored through a series of case studies, where different configurations and parameters are applied to assess their effectiveness in optimizing wind farm layouts. These techniques are evaluated based on criteria such as computational complexity, optimality of solutions, and repeatability of results. The paper's findings offer insightful comparisons and analyses of each technique, providing an understanding of their respective strengths and limitations in the context of wind farm layout optimization.

关键词： Machine learning algorithms Runtime Wind energy Computational modeling Layout Wind farms Prediction algorithms

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Scale-free linear protocol design for global regulated state synchronization of discrete-time double-integrator multi-agent systems subject to actuator saturation

Scale-free linear protocol design for global regulated state...

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第35届中国控制与决策会议

作者： Zhenwei Liu Ali Saberi Anton A.Stoorvogel College of Information Science and Engineering Northeastern University School of Electrical Engineering and Computer Science Washington State University Faculty of Electrical Engineering Mathematics and Computer ScienceUniversity of Twente

This paper studies global regulated state synchronization of discrete-time double-integrator multi-agent systems subject to actuator saturation by utilizing localized information *** propose a scalable linear protocol that achieves global regulated state synchronization for any network with arbitrary number of agents and arbitrarily directed communication graph that has a path between each agent and exosystem which generates the reference trajectory.

关键词：

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A Novel Underwater Image Enhancement and Improved Underwater Biological Detection Pipeline

arXiv

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arXiv 2022年

作者： Liu, Zheng Zhuang, Yaoming Jia, Pengrun Wu, Chengdong Xu, Hongli Liu, Zhanlin Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China Northeastern University College of Information Science and Engineering Shenyang110819 China Warner Music Group New York10019 United States

For aquaculture resource evaluation and ecological environment monitoring, automatic detection and identification of marine organisms is critical. However, due to the low quality of underwater images and the characteristics of underwater biological, a lack of abundant features may impede traditional hand-designed feature extraction approaches or CNN-based object detection algorithms, particularly in complex underwater environment. Therefore, the goal of this paper is to perform object detection in the underwater environment. This paper proposed a novel method for capturing feature information, which adds the convolutional block attention module (CBAM) to the YOLOv5 backbone. The interference of underwater creature characteristics on object characteristics is decreased, and the output of the backbone network to object information is enhanced. In addition, the self-adaptive global histogram stretching algorithm (SAGHS) is designed to eliminate the degradation problems such as low contrast and color loss caused by underwater environmental information to better restore image quality. Extensive experiments and comprehensive evaluation on the URPC2021 benchmark dataset demonstrate the effectiveness and adaptivity of our methods. Beyond that, this paper conducts an exhaustive analysis of the role of training data on performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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