Unmanned Syst.最新文献_第4页

Multi-UAV Coverage Path Planning for Gas Distribution Map Applications 天然气分布图应用的多无人机覆盖路径规划

Unmanned Syst. Pub Date : 2021-11-19 DOI: 10.1142/s2301385022500170

Abdelwahhab Bouras, Y. Bouzid, M. Guiatni

引用次数: 3

An Overview of Recent Advances in Distributed Coordination of Multi-Agent Systems 多智能体系统分布式协调研究进展综述

Unmanned Syst. Pub Date : 2021-10-27 DOI: 10.1142/s2301385021500199

Ruohan Yang, Lu Liu, Gang Feng

引用次数: 17

Co-adaptive Human-Robot Cooperation: Summary and Challenges 人机自适应合作:总结与挑战

Unmanned Syst. Pub Date : 2021-09-17 DOI: 10.1142/s230138502250011x

Sofie Ahlberg, Agnes Axelsson, Pian Yu, Wenceslao Shaw Cortez, Yuan Gao, Ali Ghadirzadeh, Ginevra Castellano, D. Kragic, Gabriel Skantze, Dimos V. Dimarogonas

引用次数: 2

Automated Playbook for UAV Traffic Management Based on Spatiotemporal Scenario Data 基于时空场景数据的无人机交通管理自动化剧本

Unmanned Syst. Pub Date : 2021-09-16 DOI: 10.1142/s2301385022500145

Chenyuan He, Yan Wan, Junfei Xie

引用次数: 0

Mathematical Modeling and Designing a Heavy Hybrid-Electric Quadcopter, Controlled by Flaps 襟翼控制的重型混合动力四轴飞行器的数学建模与设计

Unmanned Syst. Pub Date : 2021-09-16 DOI: 10.1142/s2301385022500133

M. S. A. Isaac, A. Ragab, Enrique Caballero Garcés, M. A. Luna, P. F. Peña, P. Cervera

引用次数: 4

Parametric and Implicit Features-Based UAV-UGVs Time-Varying Formation Tracking: Dynamic Approach 基于参数和隐式特征的uav - ugv时变编队跟踪:动态方法

Unmanned Syst. Pub Date : 2021-09-16 DOI: 10.1142/s2301385022500066

Ahmed Allam, A. Nemra, M. Tadjine

{"title":"Parametric and Implicit Features-Based UAV-UGVs Time-Varying Formation Tracking: Dynamic Approach","authors":"Ahmed Allam, A. Nemra, M. Tadjine","doi":"10.1142/s2301385022500066","DOIUrl":"https://doi.org/10.1142/s2301385022500066","url":null,"abstract":"Flexible and robust Time-Varying Formation (TVF) tracking of Unmanned Ground Vehicles (UGVs) guided by an Unmanned Aerial Vehicle (UAV) is considered in this paper. The UAV–UGVs system control model is based on leader-follower approach, where the control scheme consists of two consecutive tasks, namely, deployment task and TVF tracking. Accordingly, two novel nonlinear controllers are proposed for controlling the UGVs formation. First, unlike the classical frameworks on UGVs formation tracking, for which only particular shapes are handled (e.g. circle, square, ellipse), we propose a UGVs deployment-controller ensuring to reach free-formation shapes. The key feature is in using the estimated implicit representation of the desired formation shape as a potential function to generate the UGVs reference trajectory. Second, in the TVF tracking task, a robust cascaded velocity/torque controller for UGVs is proposed based on kinematic and dynamic models. Differently from the classical backstepping framework, the key idea is in introducing an auxiliary control input, in such a way that the overall UGV dynamics is converted into a simpler and modular control structure. As such, the auxiliary input is used to control indirectly the actual UGVs velocity vector. A signum term is added to the torque-input to compensate for the unknown external disturbances and unmodeled dynamics. Numerical simulation shows the effectiveness of the proposed formation controllers compared with the case when the perfect velocity-tracking assumption holds. Experimental results are further provided using three festos Robtino robots to show the validity of the proposed TVF tracking velocity-control scheme.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130862321","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

Comparison Between A* and RRT Algorithms for 3D UAV Path Planning A*与RRT算法在无人机三维路径规划中的比较

Unmanned Syst. Pub Date : 2021-09-16 DOI: 10.1142/s2301385022500078

C. Zammit, E. Kampen

{"title":"Comparison Between A* and RRT Algorithms for 3D UAV Path Planning","authors":"C. Zammit, E. Kampen","doi":"10.1142/s2301385022500078","DOIUrl":"https://doi.org/10.1142/s2301385022500078","url":null,"abstract":"This paper aims to present a comparative analysis of the two most utilized graph-based and sampling-based algorithms and their variants, in view of 3D UAV path planning in complex indoor environment. The findings of this analysis outline the usability of the methods and can assist future UAV path planning designers to select the best algorithm with the best parameter configuration in relation to the specific application. An extensive literature review of graph-based and sampling-based methods and their variants is first presented. The most utilized algorithms which are the A* for graph-based methods and Rapidly-Exploring Random Tree (RRT) for the sampling-based methods, are defined. A set of variants is also developed to mitigate with inherent shortcomings in the standard algorithms. All algorithms are then tested in the same scenarios and analyzed using the same performance measures. The A* algorithm generates shorter paths with respect to the RRT algorithm. The A* algorithm only explores volumes required for path generation while the RRT algorithms explore the space evenly. The A* algorithm exhibits an oscillatory behavior at different resolutions for the same scenario that is attenuated with the novel A* ripple reduction algorithm. The Multiple RRT generated longer unsmoothed paths in shorter planning times but required more smoothing over RRT. This work is the first attempt to compare graph-based and sampling-based algorithms in 3D path planning of UAVs. Furthermore, this work addresses shortcomings in both A* and RRT standard algorithms by developing a novel A* ripple reduction algorithm, a novel RRT variant and a specifically designed smoothing algorithm.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116977425","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: 14

FAST RRT* 3D-Sliced Planner for Autonomous Exploration Using MAVs 快速RRT* 3d切片规划自主探索使用MAVs

Unmanned Syst. Pub Date : 2021-09-15 DOI: 10.1142/s2301385022500108

Álvaro Martínez Novo, Liang Lu, P. Campoy

引用次数: 0

Analysis of Unmanned Aircraft Systems Sightings Reports: Determination of Factors Leading to High Sighting Reports 无人机系统目击报告分析:确定导致高目击报告的因素

Unmanned Syst. Pub Date : 2021-09-10 DOI: 10.1142/s2301385022500121

S. Pitcher

引用次数: 1

COAA* - An Optimized Obstacle Avoidance and Navigational Algorithm for UAVs Operating in Partially Observable 2D Environments COAA* -部分可观测二维环境下无人机的避障与导航优化算法

Unmanned Syst. Pub Date : 2021-09-03 DOI: 10.1142/s2301385022500091

Jun Jet Tai, S. K. Phang, Felicia Yen Myan Wong

{"title":"COAA* - An Optimized Obstacle Avoidance and Navigational Algorithm for UAVs Operating in Partially Observable 2D Environments","authors":"Jun Jet Tai, S. K. Phang, Felicia Yen Myan Wong","doi":"10.1142/s2301385022500091","DOIUrl":"https://doi.org/10.1142/s2301385022500091","url":null,"abstract":"Obstacle avoidance and navigation (OAN) algorithms typically employ offline or online methods. The former is fast but requires knowledge of a global map, while the latter is usually more computationally heavy in explicit solution methods, or is lacking in configurability in the form of artificial intelligence (AI) enabled agents. In order for OAN algorithms to be brought to mass produced robots, more specifically for multirotor unmanned aerial vehicles (UAVs), the computational requirement of these algorithms must be brought low enough such that its computation can be done entirely onboard a companion computer, while being flexible enough to function without a prior map, as is the case of most real life scenarios. In this paper, a highly configurable algorithm, dubbed Closest Obstacle Avoidance and A* (COAA*), that is lightweight enough to run on the companion computer of the UAV is proposed. This algorithm frees up from the conventional drawbacks of offline and online OAN algorithms, while having guaranteed convergence to a global minimum. The algorithms have been successfully implemented on the Heavy Lift Experimental (HLX) UAV of the Autonomous Robots Research Cluster in Taylor’s University, and the simulated results match the real results sufficiently to show that the algorithm has potential for widespread implementation.","PeriodicalId":164619,"journal":{"name":"Unmanned Syst.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123450693","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: 1