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A survey of safety separation management and collision avoidance approaches of civil UAS operating in integration national airspace system

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Chinese Journal of Aeronautics 2020年第11期33卷 2851-2863页

作者： xiangmin guan Renli LYU Hongxia SHI Jun CHEN Department of General Aviation Civil Aviation Management Institute of ChinaBeijing 100102China General Aviation Institute of Zhejiang JianDe Hangzhou 311600China School of Engineering and Materials Science Queen Mary University of LondonLondon E14NSUnited Kingdom

Recent years have witnessed a booming of the industry of civil Unmanned Aircraft System(UAS).As an emerging industry,the UAS industry has been attracting great attention from governments of all countries and the aviation *** are highly digitalized,informationized,and intelligent;therefore,their integration into the national airspace system has become an important trend in the development of civil ***,the complexity of UAS operation poses great challenges to the traditional aviation regulatory system and technical *** to prevent collisions between UASs and between UAS and manned aircraft to achieve safe and efficient operation in the integrated operating airspace has become a common challenge for industry and academia around the *** recent years,the international community has carried out a great amount of work and experiments in the air traffic management of UAS and some of the key *** paper attempts to make a review of the UAS separation management and key technologies in collision avoidance in the integrated airspace,mainly focusing on the current situation of UAS Traffic Management(UTM),safety separation standards,detection system,collision risk prediction,collision avoidance,safety risk assessment,etc.,as well as an analysis of the bottlenecks that the current researches encountered and their development trends,so as to provide some insights and references for further research in this ***,this paper makes a further summary of some of the research highlights and challenges.

关键词： Air transportation Collision avoidance Safety Separation control Unmanned aircraft system

A large-scale flight multi-objective assignment approach based on multi-island parallel evolution algorithm with cooperative coevolutionary

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Science China(Information Sciences) 2016年第7期59卷 185-201页

作者： Renli L xiangmin guan Xueyuan LI Inseok HWANG Department of General Aviation Civil Aviation Management Institute of China School of Electronic and Information Engineering Beihang University School of Aeronautics and Astronautics Purdue University

Due to the rapid increase of air traffic demand, the large-scale flight assignment plays a crucial role in reducing airspace congestion and economic losses via reasonably regulating the air traffic flow of China. In this paper, the large-scale flight assignment problem is formulated as a multi-objective model with consideration of the reduction of airspace congestion and flight delay. However, it is a large-scale combinatorial optimization problem with complex constraints and tightly coupled decision variables, which is difficult to deal ***, an effective multi-objective optimization algorithm is proposed based on the multi-island parallel evolution framework(PEA) with a left-right probability migration topology. Multi-island PEA employs multiple evolution populations for solving the problem simultaneously, and the left-right probability migration topology for exchange individuals among populations to improve the efficiency of the cooperation of populations. Then the cooperative co-evolution(CC) algorithm is introduced for each population to further improve the searching capability. Simulation results using the real traffic data from the China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multi-objective genetic algorithm, the well-known multi-objective evolutionary algorithm based on decomposition, a CC-based multi-objective algorithm as well as other two parallel evolution algorithms with different migration topologies.

关键词： air traffic flow management flight assignment multi-island parallel evolution algorithm migration topology cooperative co-evolution

Collision free 4D path planning for multiple UAVs based on spatial refined voting mechanism and PSO approach

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Chinese Journal of Aeronautics 2019年第6期32卷 1504-1519页

作者： Yang LIU Xuejun ZHANG Yu ZHANG xiangmin guan School of Information Science&Electric Engineering Shandong Jiaotong UniversityJinan 250357China School of Electronic&Information Engineering Beihang UniversityBeijing 100083China College of Air Traffic Control Civil Aviation University of ChinaTianjin 300350China College of Transportation Engineering Tongji UniversityShanghai 200092China School of General Aviation Civil Aviation Management Institute of ChinaBeijing 100102China

In this paper, a four-dimensional coordinated path planning algorithm for multiple UAVs is proposed, in which time variable is taken into account for each UAV as well as collision free and obstacle avoidance. A Spatial Refined Voting Mechanism(SRVM) is designed for standard Particle Swarm Optimization(PSO) to overcome the defects of local optimal and slow *** each generation candidate particle positions are recorded and an adaptive cube is formed with own adaptive side length to indicate occupied regions. Then space voting begins and is sorted based on voting results, whose centers with bigger voting counts are seen as sub-optimal positions. The average of all particles of corresponding dimensions are calculated as the refined solutions. A time coordination method is developed by generating specified candidate paths for every UAV, making them arrive the same destination with the same time consumption. A spatial-temporal collision avoidance technique is introduced to make collision free. Distance to destination is constructed to improve the searching accuracy and velocity of particles. In addition, the objective function is redesigned by considering the obstacle and threat avoidance, Estimated Time of Arrival(ETA), separation maintenance and UAV self-constraints. Experimental results prove the effectiveness and efficiency of the algorithm.

关键词： 4D path planning Collision free Multiple UAVs Obstacle avoidance Particle swarm optimization Spatial refined voting mechanism

Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval

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Chinese Journal of Aeronautics 2015年第2期28卷 556-563页

作者： Zhang Xuejun guan xiangmin Zhu Yanbo Lei Jiaxing School of Electronic and Information Engineering Beihang University National Key Laboratory of CNS/ATM Beihang University Aviation Data Communication Corporation

The continuous growth of air traffic has led to acute airspace congestion and severe delays, which threatens operation safety and cause enormous economic loss. Flight assignment is an economical and effective strategic plan to reduce the flight delay and airspace congestion by rea- sonably regulating the air traffic flow of China. However, it is a large-scale combinatorial optimiza- tion problem which is difficult to solve. In order to improve the quality of solutions, an effective multi-objective parallel evolution algorithm （MPEA） framework with dynamic migration interval strategy is presented in this work. Firstly, multiple evolution populations are constructed to solve the problem simultaneously to enhance the optimization capability. Then a new strategy is pro- posed to dynamically change the migration interval among different evolution populations to improve the efficiency of the cooperation of populations. Finally, the cooperative co-evolution （CC） algorithm combined with non-dominated sorting genetic algorithm II （NSGA-II） is intro- duced for each population. Empirical studies using the real air traffic data of the Chinese air route network and daily flight plans show that our method outperforms the existing approaches, multi- objective genetic algorithm （MOGA）, multi-objective evolutionary algorithm based on decom- position （MOEA/D）, CC-based multi-objective algorithm （CCMA） as well as other two MPEAs with different migration interval strategies.

关键词： Air traffic flow management Cooperative co-evolution Dynamic migration intervalstrategy Flight assignment Parallel evolution algorithm

A Formation Reconfiguration Algorithm for Multi-UAVs Based on Distributed Cooperative Coevolutionary with an Adaptive Grouping Strategy

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Chinese Journal of Electronics 2020年第5期29卷 841-851页

作者： LIU Huaxian LIU Feng ZHANG Xuejun guan xiangmin CHEN Jun School of Electronic and Information Engineering Beihang University General Aviation Institute of Zhejiang JianDe Department of General Aviation Civil Aviation Management Institute of China School of Engineering and Materials Science Queen Mary University of London

The Formation reconfiguration problem plays a crucial role in the implementation of complex tasks for multiple unmanned aerial vehicles, which attracted increasing attention in the past decade. Taking into consideration the control parameters and time discretization of the multi-UAVs in the 3 Dimensional(3-D) space, the formation reconfiguration problem can be formulated as a large-scale combinatorial optimization problem with complex constraints and tight couplings between variables. The problem results in the reduction in efficiency and effectiveness using classic bio-inspired algorithms. In this paper, a formation reconfiguration method based on cooperative coevolutionary algorithm is proposed along with a new decomposition strategy to improve the optimization capability and prevent premature convergence. In the proposed approach, variables of multi-UAV are divided into several sub-groups based on an adaptive grouping strategy. The proposed strategy groups the variables in order to better deal with the tight coupling among them, taking into account the variables’ variance and multi-UAVs characteristics of the formation reconfiguration problem. Therefore, each subgroup can adopt the Self-adaptive differential evolution strategy with neighborhood search(SaNSDE) with the aim to optimize the UAV’s control inputs using multithreaded programming. SaNSDE contributes to calculating the results in a fully distributed and paralleled *** solution is then obtained through cooperation and coordination with all subcomponents. Simulation results based on extreme scenarios adopted by previous researches demonstrate that the proposed algorithm outperformed the existing approaches including Particle swarm optimization(PSO), Differential evolution(DE),and the cooperative coevolution algorithms with different well-known grouping strategies.

关键词： Formation reconfiguration Multi-UAV Grouping strategy Cooperative coevolutionary Distributed

A hybrid distributed-centralized conflict resolution approach for multi-aircraft based on cooperative co-evolutionary

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Science China(Information Sciences) 2013年第12期56卷 281-296页

作者： ZHANG XueJun guan xiangmin HWANG Inseok CAI KaiQuan School of Electronic and Information Engineering Beihang University

Conflict resolution(CR)plays a crucial role in safe air trafc management(ATM).In this paper,we propose a new hybrid distributed-centralized tactical CR approach based on cooperative co-evolutionary named the CCDG(cooperative co-evolutionary with dynamic grouping)strategy to overcome the drawbacks of the current two types of approaches,the totally centralized approach and distributed ***,aircraft are divided into several sub-groups based on their ***,a dynamic grouping strategy is proposed to group the aircraft to better deal with the tight coupling among *** sub-groups are adjusted dynamically as new conflicts appear after each ***,a fast genetic algorithm(GA)is used by each sub-group to optimize the paths of the aircraft ***,the aircraft’s optimal paths are obtained through cooperation among diferent sub-groups based on cooperative co-evolutionary(CC).An experimental study on two illustrative scenarios is conducted to compare the CCDG method with some other existing approaches and it is shown that CCDG which can get the optimal solution efectively and efciently in near real time,outperforms most of the existing approaches including Stratway,the fast GA,a general evolutionary path planner,as well as three well-known cooperative co-evolution algorithms.

关键词： multi-aircraft conflict resolution hybrid distributed-centralized dynamic grouping strategy cooperative co-evolutionary

A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation

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Science China(Information Sciences) 2017年第11期60卷 198-210页

作者： xiangmin guan Xuejun ZHANG Renli LV Jun CHEN Weiszer MICHAL Department of General Aviation Civil Aviation Management Institute of China School of Electronic and Information Engineering Beihang University School of Engineering University of Lincoln

Recently, the long-term conflict avoidance approaches based on large-scale flights scheduling have attracted much attention due to their ability to provide solutions from a global point of view. However, current approaches which focus only on a single objective with the aim of minimizing the total delay and the number of conflicts, cannot provide controllers with variety of optional solutions, representing different tradeoffs. Furthermore, the flight track error is often overlooked in the current research. Therefore, in order to make the model more realistic, in this paper, we formulate the long-term conflict avoidance problem as a multi-objective optimization problem, which minimizes the total delay and reduces the number of conflicts simultaneously. As a complex air route network needs to accommodate thousands of flights, the problem is a large-scale combinatorial optimization problem with tightly coupled variables, which make the problem difficult to deal with. Hence,in order to further improve the search capability of the solution algorithm, a cooperative co-evolution(CC)algorithm is also introduced to divide the complex problem into several low dimensional sub-problems which are easier to solve. Moreover, a dynamic grouping strategy based on the conflict detection is proposed to improve the optimization efficiency and to avoid premature convergence. The well-known multi-objective evolutionary algorithm based on decomposition(MOEA/D) is then employed to tackle each sub-problem. Computational results using real traffic data from the Chinese air route network demonstrate that the proposed approach obtained better non-dominated solutions in a more effective manner than the existing approaches, including the multi-objective genetic algorithm(MOGA), NSGAII, and MOEA/D. The results also show that our approach provided satisfactory solutions for controllers from a practical point of view.

关键词： air traffic management conflict avoidance combinatorial optimization multi-objective coopera tive co-evolution

认知行为护理应用在甲状腺结节患者的术后护理中对其心理状态的影响分析

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中外医疗 2024年第16期43卷 121-124页

作者：关祥敏李祥梅杨军滕州市中医医院重症医学科山东枣庄277599

目的分析对甲状腺结节手术患者施以认知行为护理对其心理状态的改善作用。方法简单随机选取2021年9月—2022年8月滕州市中医医院接诊的70例甲状腺结节手术患者为研究对象,随机数表法分为两组,每组35例。对照组接受常规护理,研究组接... 详细信息

目的分析对甲状腺结节手术患者施以认知行为护理对其心理状态的改善作用。方法简单随机选取2021年9月—2022年8月滕州市中医医院接诊的70例甲状腺结节手术患者为研究对象,随机数表法分为两组,每组35例。对照组接受常规护理,研究组接受认知行为护理,比较两组心理状态、自护能力、认知水平、护理满意度、生活质量。结果研究组护理后抑郁自评量表(36.13±5.28)分、焦虑自评量表(35.47±4.37)分低于对照组,自护能力评分(88.23±8.41)分、(38.58±4.76)分、(45.83±3.16)分以及认知水平评分均高于对照组,差异有统计学意义(t=6.543、6.674、6.518、4.614、11.384、15.521、13.279,P均<0.05)。研究组患者护理总满意度(97.14%)高于对照组(80.00%),差异有统计学意义(P<0.05)。护理后,研究组患者生活质量评分高于对照组,差异有统计学意义(P<0.05)。结论对甲状腺结节手术患者施以认知行为护理效果良好,不但可使心理状态、自护能力、认知水平提高,同时可提高护理满意度,利于生活质量提高。

关键词：甲状腺结节术后认知行为护理心理状态

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A strategic flight conflict avoidance approach based on a memetic algorithm

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Chinese Journal of Aeronautics 2014年第1期27卷 93-101页

作者： guan xiangmin Zhang Xuejun Han Dong Zhu Yanbo Lv Ji Su Jing School of Electronic and Information Engineering Beihang University National Key Laboratory of CNS/ATM Beihang University Aviation Data Communication Corporation

Conflict avoidance （CA） plays a crucial role in guaranteeing the airspace safety. The cur- rent approaches, mostly focusing on a short-term situation which eliminates conflicts via local adjust- ment, cannot provide a global solution. Recently, long-term conflict avoidance approaches, which are proposed to provide solutions via strategically planning traffic flow from a global view, have attracted more attentions. With consideration of the situation in China, there are thousands of flights per day and the air route network is large and complex, which makes the long-term problem to be a large-scale combinatorial optimization problem with complex constraints. To minimize the risk of premature convergence being faced by current approaches and obtain higher quality solutions, in this work, we present an effective strategic framework based on a memetic algorithm （MA）, which can markedly improve search capability via a combination of population-based global search and local improve- ments made by individuals. In addition, a specially designed local search operator and an adaptive local search frequency strategy are proposed to improve the solution quality. Furthermore, a fast genetic algorithm （GA） is presented as the global optimization method. Empirical studies using real traffic data of the Chinese air route network and daily flight plans show that our approach outper- formed the existing approaches including the GA .based approach and the cooperative coevolution based approach as well as some well-known memetic algorithm based approaches.

关键词： Air traffic control Combinatorial optimization Conflict avoidance Genetic algorithm Memetic algorithm