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RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm

Computers, Materials & Continua 2024年第2期78卷 2111-2136页

作者： Lieping Zhang Xiaoxu Shi Liu Tang Yilin Wang Jiansheng Peng Jianchu Zou Key Laboratory of Advanced Manufacturing and Automation Technology(Guilin University of Technology) Education Department of Guangxi Zhuang Autonomous RegionGuilin541006China College of Mechanical and Control Engineering Guilin University of TechnologyGuilin541006China Guilin Mingfu Robot Technology Company Limited Guilin541199China Key Laboratory of AI and Information Processing(Hechi University) Education Department of Guangxi Zhuang Autonomous RegionYizhou546300China

A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile ***,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random *** that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT *** algorithm simulation platform based on the Gazebo platform was *** simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more ***,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different *** experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.

关键词： Autonomous detection RRT algorithm mobile robot ROS Karto SLAM algorithm

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Hyperbolic cosine transformer for LiDAR 3D object detection

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Optoelectronics Letters 2025年

作者： Tong Jigang Yang Fanhang Yang Sen Du Shengzhi Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and Intelligent Robot Laboratory， Tianjin University of Technology Department of Electrical Engineering， Tshwane University of Technology

Recently， Transformer has achieved great success in computer vision. However， it is constrained because the spatial and temporal complexity grows quadratically with the number of large points in 3D object detection applications from point clouds. Previous point-wise methods are suffering from time consumption and limited receptive fields to capture information among points. To address these limitations， we propose the cosh-attention， which reduces the computation complexity of space and time from the quadratic order to linear order with respect to the number of points. In the cosh-attention， the traditional softmax operator is replaced by non-negative ReLU activation and hyperbolic-cosine-based operator with re-weighting mechanism. Then based on the key component， cosh-attention， we present a two-stage hyperbolic cosine transformer (ChTR3D) for 3D object detection from point clouds. It refines proposals by applying cosh-attention in linear computation complexity to encode rich contextual relationships among points. Extensive experiments on the widely used KITTI dataset and Waymo Open Dataset demonstrate that， compared with vanilla attention， the cosh-attention significantly improves the inference speed with competitive performance. Experiment results show that， among two-stage state-of-the-art methods using point-level features to refine proposals， the proposed ChTR3D is the fastest one.

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Leveraging Semantics in Appearance based Loop Closure Detection for Long-Term Visual SLAM

Leveraging Semantics in Appearance based Loop Closure Detect...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Saba Arshad Gon-Woo Kim Department of Control and Robot Engineering Chungbuk National University Cheongju South Korea

In simultaneous localization and mapping, the detection of true loop closure benefits in relocalization and increased map accuracy. However, its performance is largely affected by variation in light conditions, viewpoints, seasons, and presence of dynamic objects. Focusing the advantages of visual semantics, this research proposes a semantics aided bag-of-wordsmodel overcoming the limitations of conventional BoW model which performs appearance-based loop detection and has been gold standard for many years. The proposed method reduces the computation time while matching current frame with the database image by segmenting the search space. Further it enhances the true loop detection rate.

关键词： Visualization Image segmentation Simultaneous localization and mapping Navigation Databases Computational modeling Semantics

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NCV-PWM Method for the Phase Current Reconstruction With DC Link Single Sensor

NCV-PWM Method for the Phase Current Reconstruction With DC ...

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作者： Shen, Yongpeng Wu, Kexuan Yuan, Xiaofang Wu, Chengzhong Liang, Weihua Chen, Zhiwei The College of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China The Department of Electric and Information Engineering Hunan University Changsha410082 China The National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University Changsha410082 China

The DC link current sampling with a single sensor is a promising solution in the electric drive field due to its low cost and small volume. However, limited by the minimum sampling time, the current reconstruction unobservable area is the most intractable challenge in the traditional single sensor reconstruction method, especially in sector boundary areas, where the phase current cannot be reconstructed completely. In this article, a new non-zero compensation vector pulse width modulation (NCV-PWM) method is proposed to eliminate the current unobservable area. Firstly, by introducing the definition of minimum sampling time, the voltage space vector plane is accurately divided into different modulation areas. Then, by applying non-zero compensation vector in the sector boundary, the current sampling window is extended, and the sampling time requirement for the DC link current is satisfied. Finally, the practicability and accuracy of the NCV-PWM method are verified by experiments, and results show that the current reconstruction error is less than 2.86%, which provides accurate current feedback for high-performance motor control. © 2023 IEEE.

关键词： Electric drives

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Improved Double Deep Q Network Algorithm Based on Average Q-Value Estimation and Reward Redistribution for robot Path Planning

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Computers, Materials & Continua 2024年第11期81卷 2769-2790页

作者： Yameng Yin Lieping Zhang Xiaoxu Shi Yilin Wang Jiansheng Peng Jianchu Zou Key Laboratory of Advanced Manufacturing and Automation Technology Guilin University of TechnologyEducation Department of Guangxi Zhuang Autonomous RegionGuilin541006China Guangxi Key Laboratory of Special Engineering Equipment and Control Guilin University of Aerospace TechnologyGuilin541004China Guilin Mingfu Robot Technology Company Limited Guilin541199China Key Laboratory of AI and Information Processing Education Department of Guangxi Zhuang Autonomous RegionHechi UniversityYizhou546300China

By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile ***,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality *** those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was ***,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q ***,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory ***,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality ***,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random *** experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average *** performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random *** not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation.

关键词： Double Deep Q Network path planning average Q-value estimation reward redistribution mechanism reward-prioritized experience selection method

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Grain-level visualization of stress-strain distribution on fatigue crack via self-magnetic flux density: a brief experimental investigation

Grain-level visualization of stress-strain distribution on f...

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IEEE Instrumentation and Measurement Technology Conference

作者： I Dewa Made Oka Dharmawan Jinvi Lee Deparment of Control Intrumentation and Robot Engineering Chosun University Gwangju Republic of Korea Department of Electronic Engineering Chosun University Gwangju Republic of Korea

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Fatigue cracks exhibit unpredictable and rapid propagation, presenting challenges in comprehending their initiation, influenced by various physical and chemical parameters. To gain deeper insights into the nucleation and growth, understanding the convergence phenomenon in ferromagnetic materials requires visualization of the local stresses and phase changes around the defect. This necessitates a measurement system capable of not only imaging the defect but also stress mapping at high spatial resolution, ideally at grain-level. In this study, we explore how this can be achieved by integrating three-axis tunnel magnetoresistive (TMR) sensor with optical microscopy images. This integration was expected to facilitate the visualization of self-magnetic flux density and enable microstructure analysis on the material's surface. Employing this mapping technique, we observe and discuss the stress distribution around the crack at the grain level with three variation of defect depths direction: 0, 30, and 60 degrees.

关键词： Microscopy Data visualization Fatigue Tunneling magnetoresistance Spatial resolution Surface cracks Stress

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Boundary Gap Based Reactive Navigation in Unknown Environments

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 468-477页

作者： Zhao Gao Jiahu Qin Shuai Wang Yaonan Wang the Department of Automation University of Science and Technology of ChinaHefei 230027China the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha 410082China

Due to the requirements for mobile robots to search or rescue in unknown environments,reactive navigation which plays an essential role in these applications has attracted increasing ***,most existing reactive methods are vulnerable to local minima in the absence of prior knowledge about the *** paper aims to address the local minimum problem by employing the proposed boundary gap(BG)based reactive navigation ***,the narrowest gap extraction algorithm(NGEA)is proposed to eliminate the improper ***,we present a new concept called boundary gap which enables the robot to follow the obstacle boundary and then get rid of local ***,in order to enhance the smoothness of generated trajectories,we take the robot dynamics into consideration by using the modified dynamic window approach(DWA).Simulation and experimental results show the superiority of our method in avoiding local minima and improving the smoothness.

关键词： Boundary gap local minima reactive navigation unknown environments

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Long-Range Vehicle Detection of LiDAR using Adaboost Classification*

Long-Range Vehicle Detection of LiDAR using Adaboost Classif...

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IEEE/SICE International Symposium on System Integration

作者： Na-Young Lim Jeon-Hyeok Lee Tae-Hyoung Park Department of Intelligent Systems & Robotics Chungbuk National University Cheongju South Korea Department of Control & Robot Engineering Chungbuk National University Cheongju South Korea

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

In recent developments, autonomous racing has garnered attention as it aims to overcome the limitations of standard autonomous driving systems. Achieving safe racing conditions necessitates both fast and long-range perception. However, current 3D LiDAR object detection methods face challenges with high computational costs and limited detection ranges. These issues make them unsuitable for racing scenarios. To address these challenges, this paper proposes a clustering-based long-range vehicle detection method that relies solely on LiDAR. First, the method removes ground points and foreground points and clusters the remaining points. Subsequently, these clusters are classified as vehicles using AdaBoost, generating 2D bounding boxes in the range view. Experimental results demonstrate superior performance, achieving a computational efficiency of 53 Hz and a long-range detection accuracy of over 80%, compared to voxel-based and range-based methods. This approach offers a viable solution for autonomous racing environments.

关键词： Laser radar Three-dimensional displays Accuracy Vehicle detection System integration Object detection Computational efficiency Faces Standards Autonomous vehicles

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Effective distributed algorithm for solving linear matrix equations

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Science China(Information Sciences) 2023年第8期66卷 265-266页

作者： Songsong CHENG Jinlong LEI Xianlin ZENG Yuan FAN Yiguang HONG Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Department of Control Science and Engineering Tongji University Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

Dear editor,Solving linear matrix equations is a basic and important problem in many fields such as the computation of generalized inverses of matrices and(generalized) Sylvester equations. Also, the linear algebraic ...

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Classification of EEG signals based on CNN-Transformer model 20

Classification of EEG signals based on CNN-Transformer model

引用

20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Liu, Jianwei Dong, Enzeng Tong, Jigang Yang, Sen Du, Shengzhi Tianjin University of Technology Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems Intelligent Robot Laboratory 391 Binshui West Road Tianjin China Tshwane University of Technology Department of Electrical Engineering Pretoria0001 South Africa

ISBN: (纸本)9798350320831

Brain-computer interfaces (BCI) based on EEG have attracted extensive research and attention worldwide, while motor imagery (MI), mental arithmetic (MA), and P300 event-related potentials are a few of the more commonly used *** Transformer(ViT) is a new Transformer model that has superior global processing power compared to Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).In this study, we propose a hybrid CNN-Transformer based model that uses CNN to convolve EEG signals in time and space, followed by ViT for global processing, and finally optimizes the model using 10-run × 10-fold cross-validation and validates it on a publicly available dataset of 29 subjects. Final accuracies of 87.23% and 90.79% were achieved on the MI and MA tasks, respectively. Compared to other literature, the model achieved higher classification accuracies. © 2023 IEEE.

关键词： Brain computer interface

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