Unmanned Syst.最新文献

Editorial: Special Issue on Perception, Decision and Control of Unmanned Systems Under Complex Conditions 社论:复杂条件下无人系统的感知、决策和控制特刊

Unmanned Syst. Pub Date : 2023-01-28 DOI: 10.1142/s2301385023020016

Bin Xin

引用次数: 0

Modeling and Quantitative Evaluation Method of Environmental Complexity for Measuring Autonomous Capabilities of Military Unmanned Ground Vehicles 军用无人地面车辆自主能力环境复杂性建模与定量评价方法

Unmanned Syst. Pub Date : 2022-08-24 DOI: 10.1142/s2301385023500176

Shaobin Wu, Shihao Li, Jian-wei Gong, Zexin Yan

引用次数: 1

Recent Developments in Event-Triggered Control of Nonlinear Systems: An Overview 非线性系统事件触发控制的最新进展:综述

Unmanned Syst. Pub Date : 2022-08-18 DOI: 10.1142/s2301385023310039

Pengpeng Zhang, Tengfei Liu, Jie Chen, Zhong-Ping Jiang

引用次数: 1

Physical Modeling, Simulation and Validation of Small Fixed-Wing UAV 小型固定翼无人机物理建模、仿真与验证

Unmanned Syst. Pub Date : 2022-08-06 DOI: 10.1142/s2301385023500152

E. H. Kapeel, Ehab Safwat, A. Kamel, M. Khalil, Y. Elhalwagy, H. Hendy

引用次数: 0

An Improved RRT* UAV Formation Path Planning Algorithm Based on Goal Bias and Node Rejection Strategy 基于目标偏差和节点拒绝策略的改进RRT*无人机编队路径规划算法

Unmanned Syst. Pub Date : 2022-07-25 DOI: 10.1142/s2301385023500140

Haiying Liu, Jing Chen, J. Feng, Haiping Zhao

引用次数: 5

Hardware Implementation of Attitude Estimation Methods Using Multiple GPS Receivers 多GPS接收机姿态估计方法的硬件实现

Unmanned Syst. Pub Date : 2022-07-15 DOI: 10.1142/s2301385023500139

Djamel Dhahbane, S. Sakhi, A. Nemra

引用次数: 0

A Noise-Excitation Generative Adversarial Network for Actuator Fault Diagnosis of Multi-legged Robot 基于噪声激励生成对抗网络的多足机器人执行器故障诊断

Unmanned Syst. Pub Date : 2022-07-07 DOI: 10.1142/s2301385023410042

Liling Ma, Jian Guo, Jiehao Li, Junzheng Wang

{"title":"A Noise-Excitation Generative Adversarial Network for Actuator Fault Diagnosis of Multi-legged Robot","authors":"Liling Ma, Jian Guo, Jiehao Li, Junzheng Wang","doi":"10.1142/s2301385023410042","DOIUrl":"https://doi.org/10.1142/s2301385023410042","url":null,"abstract":"This research provides a novel approach for detecting multi-legged robot actuator faults. The most significant concept is to design the Fault Diagnosis Generative Adversarial Network (FD-GAN) to fully adapt to the fault diagnosis problem with insufficient data. We found that it is difficult for methods based on classification and prediction to learn failure patterns without enough data. A straightforward solution is to use massive amounts of normal data to drive the diagnostic model. We introduce frequency-domain information and fuse multi-sensor data to increase the features and expand the difference between normal data and fault data. A GAN-based framework is designed to calculate the probability that the enhanced data belongs to the normal category. It uses a generator network as a feature extractor, and uses a discriminator network as a fault probability evaluator, which creates a new use of GAN in the field of fault diagnosis. Among the many learning strategies of GAN, we find that a key point that can distinguish the two types of data is to use the hidden layer noise with appropriate discrimination as the excitation. We also design a fault location method based on binary search, which greatly improves the search efficiency and engineering value of the entire method. We have conducted a lot of experiments to prove the diagnostic effectiveness of our architecture in various road conditions and working modes. We compared FD-GAN with popular diagnostic methods. The results show that our method has the highest accuracy and recall rate.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132456165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Multi-Uncertainty Captured Multi-Robot Lidar Odometry and Mapping Framework for Large-Scale Environments 面向大尺度环境的多不确定多机器人激光雷达测程与制图框架

Unmanned Syst. Pub Date : 2022-07-01 DOI: 10.1142/s2301385023410030

Guang-ming Xiong, Junyi Ma, Huilong Yu, Jingyi Xu, Jiahui Xu

{"title":"Multi-Uncertainty Captured Multi-Robot Lidar Odometry and Mapping Framework for Large-Scale Environments","authors":"Guang-ming Xiong, Junyi Ma, Huilong Yu, Jingyi Xu, Jiahui Xu","doi":"10.1142/s2301385023410030","DOIUrl":"https://doi.org/10.1142/s2301385023410030","url":null,"abstract":"Multi-robot simultaneous localization and mapping (MR-SLAM) is of great importance for enhancing the efficiency of large-scale environment exploration. Despite remarkable advances in schemes for cooperation, there is a critical lack of approaches to handle multiple uncertainties inherent to MR-SLAM in large-scale environments. This paper proposes a multi-uncertainty captured multi-robot lidar odometry and mapping (MUC-LOAM) framework, to quantify and utilize the uncertainties of feature points and robot mutual poses in large-scale environments. A proposed hybrid weighting strategy for pose update is integrated into MUC-LOAM to handle feature uncertainty from distance changing and dynamic objects. A devised Bayesian Neural Network (BNN) is proposed to capture mutual pose uncertainty. Then the covariance propagation of quaternions to Euler angles conversion is leveraged to filter out unreliable mutual poses. Another covariance propagation through coordinate transformations in nonlinear optimization improves the accuracy of map merging. The feasibility and enhanced robustness of the proposed framework for large-scale exploration are validated on both public datasets and real-world experiments.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130860746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Student T-Based Maximum Correntropy Unscented Kalman Filter for UAV Target Tracking 基于学生的最大熵无嗅卡尔曼滤波器用于无人机目标跟踪

Unmanned Syst. Pub Date : 2022-06-27 DOI: 10.1142/s2301385023500127

Xiaoxue Feng, Shuhui Li, Yue Wen, Feng Pan

引用次数: 0

Reinforcement Learning Applications in Unmanned Vehicle Control: A Comprehensive Overview 强化学习在无人驾驶车辆控制中的应用综述

Unmanned Syst. Pub Date : 2022-06-27 DOI: 10.1142/s2301385023310027

Hao Liu, Bahare Kiumarsi, Yusuf Kartal, A. T. Koru, H. Modares, F. Lewis

引用次数: 5