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Quantized Communications in Target Enclosing of Autonomous Surface Vehicles with Prescribed Performance Control: Theory and Experiment

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Hao Peng, Yuhan Wang, Yueying Northeastern University Faculty of Robot Science and Engineering Shenyang110819 China Northeastern University Foshan Graduate School of Innovation Foshan528311 China Tongji University School of Computer Science and Technology Shanghai201804 China

This paper investigates the problem of target enclosing control for autonomous surface vehicles (ASVs) that a follower tracking around a moving target. Specifically, the abilities of prescribed performance control (PPC) and quantized communications are injected into the target enclosing controller. Main features of the proposed algorithm are that: First, for individual tracking level, the target enclosing radius distance and angular errors are constrained within user-defined bounds, to guarantee these errors not escape the predefined regions. Second, for coordination control level, the communications between the ASVs are quantized, which significantly reducing the communication load and saving network bandwidth. Third, only the yaw rate is needed to complete the target enclosing mission, and do not need the kinetics information, which is an advantage for practical applications. Furthermore, Lyapunov stability theory is used to prove the boundedness of the system. Both theoretical analysis and experimental test are given to demonstrate the validity of the presented target enclosing control strategy. © 1967-2012 IEEE.

关键词： Bandwidth

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Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 268-281页

作者： Tian, Ye Pan, Jingwen Yang, Shangshang Zhang, Xingyi He, Shuping Jin, Yaochu Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230088 China Anhui University School of Computer Science and Technology Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Hefei230601 China Anhui University Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Hefei230601 China Bielefeld University Faculty of Technology Bielefeld33619 Germany

The sparse adversarial attack has attracted increasing attention due to the merit of a low attack cost via changing a small number of pixels. However, the generated adversarial examples are easily detected in vision since the perturbation to each pixel is relatively large. To achieve imperceptible and sparse adversarial attacks, this article formulates a bi-objective constrained optimization problem, simultaneously minimizing the 0 and 2 distances to the original image, and proposes a dual-population-based constrained evolutionary algorithm to solve it. The proposed method solves the optimization problem by evolving two populations, where one population is responsible for finding feasible solutions (i.e., successful attacks) and the other one is to minimize both the 0 and 2 distances. Moreover, a population initialization strategy and two genetic operators are customized to accelerate the convergence speed. Experimental results indicate that the proposed method can achieve high success rates with low attack costs, and strikes a better balance between the 0 and 2 distances than state-of-the-art methods. © 2020 IEEE.

关键词： Evolutionary algorithms

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Fault Diagnosis of Harmonic Reducers Based On a FDG-Resnet Joint Methodology

Fault Diagnosis of Harmonic Reducers Based On a FDG-Resnet J...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Jing Lv Zhuo Long Juan Wang Xiaoguang Ma College of Electronics and Information Engineering Heilongjiang University of Science and Technology Harbin China Faculty of Robot Science Engineering and also with the Foshan Graduate School Northeastern University Northeastern University Foshan China

Harmonic reducers(HRs) are widely used in various industrial fields due to its advantages of high precision, high transmission efficiency and compact structure. However, the vibration signal of the HRs are complex, making it difficult to diagnose faults accurately. In this paper, a method based on frequency domain Gramian angular field (FDG) and Deep residual network was proposed to classify the fault states of the HRs. First, the vibration signals were converted into twodimensional images using Gramian angular field (GAF) coding, and a two-dimensional discrete Fourier transform (2D-DFT) were performed to transform the images from the spatial domain to the frequency domain; The training accuracy of traditional models is not very high due to their low number of network layers. The deep residual network not only has a very deep network layer, but also can guarantee the accuracy of the model under certain conditions, so the deep residual network is introduced to train the model. Finally, experiments were designed using data from a real industrial HRs health monitoring platform to validate the proposed method. Under fixed operating conditions, the proposed method achieved an accuracy of 99%, which was an improvement of 0.3% compared to ResNet50, 3.5% compared to VGGNet-16, and 2.9% compared to the Swin-Tiny model. Under variable load conditions, it achieved an accuracy of 99.4%, an increase of 0.7% compared to ResNet50, 5.5% compared to VGGNet-16, and 5.6% compared to Swin-Tiny. Under variable speed conditions, it achieved an accuracy of 94.3%, an improvement of 0.9% compared to ResNet50, 0.1% compared to VGGNet-16, and 9.3% compared to Swin-Tiny.

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Experimental Study on 3-DoF Motion Control of Unmanned Water-powered Aerial Vehicle Using Nozzle Rotation Mechanism

Experimental Study on 3-DoF Motion Control of Unmanned Water...

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International Conference on Control, Automation and Systems ( ICCAS)

作者： Cao-Tri Dinh Thien-Dinh Nguyen Thinh Huynh Dong-Hun Lee Yong-Woon Choi Young-Bok Kim Department of Intelligent Robot Engineering Pukyong National University Busan Korea Department of Smart Mobility Engineering Pukyong National University Busan Korea Department of Information System Science Faculty of Engineering Soka University Tokyo Japan

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

Specialized missions around water bodies, such as firefighting, rescue, and exploration, always pose a challenge to operators with low visibility, dense smoke, and extreme temperatures. To minimize the potential risks that may affect the operator, this paper proposed an unmanned water-powered aerial vehicle using a nozzle rotation mechanism. This mechanism allows the vehicle to have a wide range of forces and torques in multiple directions under constant mass flowrate condition. In the control system design, a robust controller is designed to regulate the altitude, roll, and pitch motions of the vehicle. In detail, to come up with the control law, the following steps are taken. Firstly, a mathematical model is derived to reflect the vehicle dynamics. Secondly, an integral sliding mode controller is applied to control the three-degree-of-freedom motion of the vehicle. Thirdly, experiments are conducted to confirm the effectiveness and feasibility of the controller and vehicle, respectively. Moreover, a well-known proportional-derivative-integral controller is applied to compare with the proposed control system. The results demonstrate that the vehicle could fly following the desired altitude in the presence of external disturbances.

关键词： Automation 3-DOF Control systems Mathematical models Trajectory Vehicle dynamics Motion control

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Handling Collaborative robot System for Grasping Medical Cotton Cloth Porous with Bernoulli Gripper

Handling Collaborative Robot System for Grasping Medical Cot...

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IEEE International Conference on Mechatronics and Automation

作者： Kitti Suwanratchatamanee Hideyuki Sawada Shuji Hashimoto Department of Engineering Robotics Research and Development Center Golden Robot Co. Ltd Banchang Rayong Thailand Department of Applied Physics Graduate School of Advanced Science and Engineering Faculty of Science and Engineering Waseda University Tokyo Japan Waseda University Tokyo Japan

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

This paper presents the handling collaborative robot (Cobot) system for grasping the medical cotton cloth porous that were arranged in matching layers from one point to another point by arranged them in matching layers again. The important is to not grasping all layer objects together in once, but must be separately for each layer by layer. As the object to be grasped is the cotton cloth porous to be used in the medical purpose, thus the cleanness and must not leave any wrinkles or marks when handled the fabric are necessity required before packaging process. The proposed handling collaborative robot system contains the novel tooling called "Bernoulli Gripper" can achieve this handling task perfectly with 100% success rate of experiments. This gripper was developed to lifts the porous fabric sheet up in such a way that it does not grasping touch by utilizing four points Bernoulli principle elements like no other grippers that need to do the touching forces and always leave wrinkles or marks when handled the fabric, such as pneumatic suction cup, robot hand and fingers. Moreover, the research objective is to produce the handling robot system that can achieve the non-contact grasping and do collaborative tasks together with human in the same environment. As the result, our proposed system can be used for working together with human worker as the collaborative partner to arrange these medical cotton cloth porous products with about 6.5 seconds per sheet in the real production line suitably.

关键词： Mechatronics Grasping Collaborative robots Production Pneumatic systems Packaging Fabrics

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Neuro-Planner: A 3D Visual Navigation Method for MAV with Depth Camera based on Neuromorphic Reinforcement Learning

arXiv

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arXiv 2022年

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

Traditional visual navigation methods of micro aerial vehicle (MAV) usually calculate a passable path that satisfies the constraints depending on a prior map. However, these methods have issues such as high demand for computing resources and poor robustness in face of unfamiliar environments. Aiming to solve the above problems, we propose a neuromorphic reinforcement learning method (Neuro-Planner) that combines spiking neural network (SNN) and deep reinforcement learning (DRL) to realize MAV 3D visual navigation with depth camera. Specifically, we design spiking actor network based on two-state LIF (TS-LIF) neurons and its encoding-decoding schemes for efficient inference. Then our improved hybrid deep deterministic policy gradient (HDDPG) and TS-LIF-based spatio-temporal back propagation (STBP) algorithms are used as the training framework for actor-critic network architecture. To verify the effectiveness of the proposed Neuro-Planner, we carry out detailed comparison experiments with various SNN training algorithm (STBP, BPTT and SLAYER) in the software-in-the-loop (SITL) simulation framework. The navigation success rate of our HDDPG-STBP is 4.3% and 5.3% higher than that of the original DDPG in the two evaluation environments. To the best of our knowledge, this is the first work combining neuromorphic computing and deep reinforcement learning for MAV 3D visual navigation task. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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

arXiv

引用

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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Research on Reflector Navigation Algorithm and Optimization Smoothing Critical Path Trajectory

Research on Reflector Navigation Algorithm and Optimization ...

引用

Chinese Automation Congress (CAC)

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

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.

关键词： Location awareness Smoothing methods Nonlinear equations Navigation Inverse problems Heuristic algorithms Lasers

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DRMAF-Transformer: An End-to-End Adaptive Fusion Framework for Fault Diagnosis of Harmonic Drivers

DRMAF-Transformer: An End-to-End Adaptive Fusion Framework f...

引用

Sensors and Information Technology (ICSI), International Conference on

作者： Kang Xu Shaowei Yang Xiaoguang Ma Zhuo Long College of Information Science and Engineering Foshan Graduate School of Innovation Northeastern University Shenyang Liaoning Foshan Guangdong China Faculty of Robot Science and Engineering Foshan Graduate School of Innovation Northeastern University Shenyang Liaoning Foshan Guangdong China School of Electrical and Information Engineering Changsha University of Science and Technology Hunan Changsha China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

Conventional fault diagnosis methods of Harmonic Drives(HDs) could not work satisfactorily due to their poor performance in complex working environments such as variable loads and speeds. In order to achieve high robustness and accuracy in HDs fault diagnosis under multiple abnormal situations, this paper proposed a fault diagnosis framework based on dimension reconstruction and multi-channel adaptive fusion Transformer (DRMAF-Transformer), which was an a network architecture that combined Fast Fourier Transform (FFT) and improved Vision Transformer (VIT), consisting of three stages: dimension reconstruction based on FFT, two-stage feature extraction, and adaptive multi-channel fusion. The DRMAF-Transformer was experimentally validated on four datasets of HDs from real manufacturing lines. The experimental results showed that, compared to other SOTA fault diagnosis methods, the DRMAF-Transformer could achieve higher recognition accuracy under both fixed and composite working condition data conditions.

关键词： Fault diagnosis Employee welfare Computer vision Accuracy Adaptive systems Fast Fourier transforms Transformers Feature extraction Harmonic analysis Manufacturing

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An Extended Coronavirus Optimization Algorithm for Trajectory Planning of a Model-Based Racing Track

An Extended Coronavirus Optimization Algorithm for Trajector...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Rana E. Aly Youmna A. Abu-Krisha Omar M. Fatehy Ahmed Y. Ali Catherine M. Elias Omar M. Shehata Multi-Robot Systems (MRS) Research Group Cairo Egypt Mechatronics Department Faculty of Engineering and Materials Science German University in Cairo Egypt

In light of the latest pandemic, many researchers proposed a nature-inspired Coronavirus algorithm to aid in tackling different optimization problems. Many meta-heuristic techniques were conducted to handle the trajectory planning of vehicles on track; however, no implementation or comparison is formulated using the aforementioned new technique. This paper proposes a modified version of the Coronavirus optimization algorithm to aid the drivers offline prior to entering the track. To ensure this algorithm's capability, a comparative study is conducted between the extended Coronavirus Optimization Algorithm (CVOA) and other meta-heuristic techniques such as: Genetic Algorithm (GA) and Grey Wolf Optimization Algorithm (GWO). Using MATLAB, two different tracks were visualized and the aforementioned techniques were employed to generate a feasible trajectory plan. All equations are represented using a real vehicle model to verify the algorithms' outputs with real case scenarios.

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