检索结果-内蒙古大学图书馆

Electrical Automation and Artificial Intelligence (ICEAAI), International Conference on

作者： Ariano Da Conceicao Lianfang Tian Qiliang Du Ling Yuan School of Automation Science and Engineering South China University of Technology Guangzhou China Research Institute of Modern Industrial Innovation South China University of Technology Guangzhou China Guangdong Engineering Research Center of Cloud-Edge-End Collaboration Technology for Smart City Guangzhou China Key Laboratory of Autonomous Systems and Network Control of Ministry of Education Guangzhou China Key Laboratory of Large-Model Embodied-Intelligent Humanoid Robot (2024KSYS004) Guangzhou China Sino-Singapore International Joint Research Institute Guangzhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Indoor mobile robots require reliable solutions for mapping, localization, and navigation tasks. This paper presents a mobile robot system that implements the ability to realize mapping using visual SLAM with ORB-SLAM3 and LiDAR-based mapping with Gmapping for indoor perception and navigation through a modular ROS architecture. The framework also implements autonomous navigation and an autonomous Rapidly-exploring Random Trees (RRT) exploration module with adaptive reset mechanisms. The system operates on a ROS-based platform equipped with an Intel RealSense D435i RGB-D camera and LDS-02 LiDAR scanner. Experimental validation in simulated indoor environments demonstrates the system's capabilities and the presented architecture achieves computational efficiency through selective utilization of mapping modules while maintaining mapping accuracy through complementary sensor modalities.

关键词： Visualization Simultaneous localization and mapping Accuracy Laser radar Navigation Service robots Heuristic algorithms Computer architecture Mobile robots autonomous robots

来源：评论

学校读者我要写书评

暂无评论

Distributed Optimization and Scaling Design for Solving Sylvester Equations

引用

Journal of Systems Science & Complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

来源：评论

学校读者我要写书评

暂无评论

Scene image recognition with knowledge transfer for drone navigation

引用

Journal of Systems Engineering and Electronics 2023年第5期34卷 1309-1318页

作者： DU Hao WANG Wei WANG Xuerao ZUO Jingqiu WANG Yuanda School of Automation Southeast UniversityNanjing 210096China Autonomous Control Robot Laboratory Jiangsu Zhongke Institute of Applied Research on Intelligent Science and TechnologyChangzhou 213164China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing 210044China

In this paper,we study scene image recognition with knowledge transfer for drone *** divide navigation scenes into three macro-classes,namely outdoor special scenes(OSSs),the space from indoors to outdoors or from outdoors to indoors transitional scenes(TSs),and ***,there are difficulties in how to recognize the TSs,to this end,we employ deep convolutional neural network(CNN)based on knowledge transfer,techniques for image augmentation,and fine tuning to solve the ***,there is still a novelty detection prob-lem in the classifier,and we use global navigation satellite sys-tems(GNSS)to solve it in the prediction *** results show our method,with a pre-trained model and fine tun-ing,can achieve 91.3196%top-1 accuracy on Scenes21 dataset,paving the way for drones to learn to understand the scenes around them autonomously.

关键词： scene recognition convolutional neural network knowledge transfer global navigation satellite systems(GNSS)-aided

来源：评论

学校读者我要写书评

暂无评论

autonomous landing scene recognition based on transfer learning for drones

引用

Journal of Systems Engineering and Electronics 2023年第1期34卷 28-35页

作者： DU Hao WANG Wei WANG Xuerao WANG Yuanda School of Automation Southeast UniversityNanjing 210096China Autonomous Control Robot Laboratory Jiangsu Zhongke Institute of Applied Research on Intelligent Science and TechnologyChangzhou 213164China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing 210044China

In this paper, we study autonomous landing scene recognition with knowledge transfer for drones. Considering the difficulties in aerial remote sensing, especially that some scenes are extremely similar, or the same scene has different representations in different altitudes, we employ a deep convolutional neural network(CNN) based on knowledge transfer and fine-tuning to solve the problem. Then, LandingScenes-7 dataset is established and divided into seven classes. Moreover, there is still a novelty detection problem in the classifier, and we address this by excluding other landing scenes using the approach of thresholding in the prediction stage. We employ the transfer learning method based on ResNeXt-50 backbone with the adaptive momentum(ADAM) optimization algorithm. We also compare ResNet-50 backbone and the momentum stochastic gradient descent(SGD) optimizer. Experiment results show that ResNeXt-50 based on the ADAM optimization algorithm has better performance. With a pre-trained model and fine-tuning, it can achieve 97.845 0% top-1 accuracy on the LandingScenes-7dataset, paving the way for drones to autonomously learn landing scenes.

关键词： landing scene recognition convolutional neural network(CNN) transfer learning remote sensing image

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A map accessibility analysis algorithm for mobile robot navigation in outdoor environment

A map accessibility analysis algorithm for mobile robot navi...

引用

2019 IEEE International Conference on Real-Time Computing and robotics, RCAR 2019

作者： Yu, Xiangyu Wang, Jingchuan Chen, Weidong Wang, Hesheng Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Medical Robotics Shanghai Jiao Tong University Autonomous Robot Lab Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728137261

autonomous mobile robots have been widely used to perform specific tasks in recent decades. Map accessibility analysis is necessary for those robots to achieve safe and efficient path planning and navigation, especially in outdoor environment where the terrain can be uneven. Researchers have done sufficient works on the influences of different factors. However, integrating these factors is still a great challenge since the information of some constraints are only needed for specific robot tasks. In this paper, we firstly build a Three Dimensional (3D) Occupancy Grid Map (OctoMap) using a 3D Lidar and encoder-based odometer, and then extract different factors from the OctoMap and real-time lidar data. In order to analyze the relationship between map accessibility and variety of constraints, we design an open system and propose a map accessibility analysis algorithm to integrate different factors which have influence on mobile robot navigation. The system and algorithm are demonstrated to be efficient via experiments. © 2018 IEEE

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Adaptive Fixed-time Fuzzy Tracking control of Uncertain robot 27

Adaptive Fixed-time Fuzzy Tracking Control of Uncertain Robo...

引用

27th International Conference on Automation and Computing, ICAC 2022

作者： Zhou, Beibei Zhu, Chengzhi Jiang, Yiming Guangzhou Jianxin Technology Co. Ltd Guangzhou510640 China South China University of Technology Key Laboratory of Autonomous Systems and Net-worked Control College of Automation Science and Engineering Guangzhou510640 China Hunan University National Engineering Laboratory for Robot Visual Perception and Control Hunan Changsha410082 China

ISBN: (数字)9781665498074

ISBN: (纸本)9781665498074

We propose a novel fixed-time fuzzy control strategy to achieve the trajectory tracking of the robot systems with uncertainties in this article. The tracking errors of the system can be convergent to a small area around zero with fixed-time convergence rate, combining with the fuzzy logic system and the backstepping technique. Besides, the boundedness of all the closed-loop system signals can be proved under the proposed controller. The superiority performance of the proposed control strategy has been shown by the simulation results. © 2022 IEEE.

关键词： Closed loop systems

来源：评论

学校读者我要写书评

暂无评论

A Map Accessibility Analysis Algorithm for Mobile robot Navigation in Outdoor Environment

A Map Accessibility Analysis Algorithm for Mobile Robot Navi...

引用

IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Xiangyu Yu Jingchuan Wang Weidong Chen Hesheng Wang Ministry of Education of China Key Laboratory of System Control and Information Processing and Institute of Medical Robotics Shanghai Jiao Tong University and Autonomous Robot Lab Shanghai China

ISBN: (数字)9781728137261

ISBN: (纸本)9781728137278

关键词： Three-dimensional displays Mobile robots Laser radar Navigation Task analysis Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Stiffness Estimation and Intention Detection for Human-robot Collaboration

Stiffness Estimation and Intention Detection for Human-Robot...

引用

IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xiongjun Chen Yiming Jiang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In this paper, we propose a stiffness estimation and intention detection method for human-robot collaboration. The human arm endpoint stiffness can be obtained according to the muscle activation levels of the upper arm and the human arm configurations. The estimated endpoint stiffness of human arm is matching to the robot arm joint stiffness through an appropriate mapping. The motion intention of human arm is detected based on the wrist configuration which is recognized by a Myo armband attached at the forearm of the operator. In order to reduce the time of feature engineering to ensure the performance of real-time collaboration, the wrist configuration recognition is realised based on the neural learning algorithm. The sEMG of the human forearm is directly fed into the neural network after processing by filters and sliding windows. The force sensor at the end of the robot arm is embedded in the feedback loop to make the robot arm better adapted to the operator's movement. The results of experiments performed on Baxter robot platform illustrate a good performance and verifies the proposed method.

关键词： Wrist Neural networks Collaboration Estimation robot sensing systems Manipulators robots

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：