融合共赢 智造未来

一滴水,融进大海,永不枯竭。CIMT2019盛会,有如美丽的海湾,科技的海洋。母机航群借'团标'东风再次扬帆起航,沿着协会航标指引航行,机床工业之互联云海,航进智造海洋,不息生态。

Identification and Comparison of Aircraft Industry Clusters in China and United States

Aircraft industry is very important to the economy and security of a country,and aircraft industry clusters have already existed in the world.Based on Input-Output data and Czamanski's method,the aircraft industry clusters in China and USA were identified quantitatively in this paper.Furthermore,this article carried out comparison analyses of the identification results.The research finds out:1) a mature aircraft industry cluster would be generally composed of 7 industrial subgroups,including aircraft industry,metal making and products manufacturing industry,machinery and equipment industry,electronics industry,automobile industry,material industry and others,and electronics industrial subgroup will play a more and more important role in the cluster;2) in the range of industry-covering,the level of industry-linkage,and the economic performance,there is a tremendously large gap between the aircraft industry cluster of China and that of USA;3) the spatial evolution of these clusters or centers is highly consistent with the diffusion of a country's industrialization.Finally,based on those findings,the paper gives some advice on how to improve Czamanski's method and what China should do to develop its own competitive aircraft industry:1) China should employ institutional innovation,and turn to be market-oriented;2) China should abandon the traditional pattern of closed-development,and strengthen the interaction and collaboration between aircraft industry and related industries,especially the electronics industry;3) China should rectify and perfect its spatial development planning of aircraft industry.

Coordinated Path Planning for UAVs Based on Sheep Optimization

Using the traditional swarm intelligence algorithm to solve the cooperative path planning problem for multi-UAVs is easy to incur the problems of local optimization and a slow convergence rate.A cooperative path planning method for multi-UAVs based on the improved sheep optimization is proposed to tackle these.Firstly,based on the three-dimensional planning space,a multi-UAV cooperative cost function model is established according to the path planning requirements,and an initial track set is constructed by combining multiple-population ideas.Then an improved sheep optimization is proposed and used to solve the path planning problem and obtain multiple cooperative paths.The simulation results show that the sheep optimization can meet the requirements of path planning and realize the cooperative path planning of multi-UAVs.Compared with grey wolf optimizer(GWO),improved gray wolf optimizer(IGWO),chaotic gray wolf optimizer(CGWO),differential evolution(DE)algorithm,and particle swam optimization(PSO),the convergence speed and search accuracy of the improved sheep optimization are significantly improved.

Collaboration Optimization of Flight Schedule in Beijing⁃Tianjin⁃Hebei Airport Group

The coordinated and integrated development of regional airport group system has been identified as an important research topic in the field of air traffic management in China.However,due to the clear limitation on airspace resources and severe traffic congestion,it is necessary to further study the problem of flight schedule coordination optimization for airport clusters.We take the Beijing-Tianjin-Hebei airport Group as an example and construct an optimization model of flight schedule with the minimum adjustment and delay.The design of the implementation algorithm is proposed.As demonstrated by the simulation results,the flight delay in the Beijing-Tianjin-Hebei multi-airport system is noticeably reduced by applying both the optimization model and the algorithm proposed in this paper.

Dynamic collision avoidance for cooperative fixed-wing UAV swarm based on normalized artificial potential field optimization

Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.

Inter-satellite Link Topology Design and Relative Navigation for Satellite Clusters

A distributed relative navigation approach via inter-satellite sensing and communication for satellite clusters is proposed. The inter-satellite link(ISL)is used for ranging and exchanging data for the relative navigation,which can improve the autonomy of the satellite cluster. The ISL topology design problem is formulated as a multi-objective optimization problem where the energy consumption and the navigation performance are considered. Further,the relative navigation is performed in a distributed fashion,where each satellite in the cluster makes observations and communicates with its neighbors via the ISL locally such that the transmission consumption and the computational complexity for the navigation are reduced. The ISL topology optimization problem is solved via the NSGA-Ⅱ algorithm,and the consensus Kalman filter is used for the distributed relative navigation. The proposed approach is flexible to varying tasks,with satellites joining or leaving the cluster anytime,and is robust to the failure of an individual satellite. Numerical simulations are presented to verify the feasibility of the proposed approach.

Homogeneity Analysis of Multiairport System Based on Airport Attributed Network Representation Learning

The homogeneity analysis of multi-airport system can provide important decision-making support for the route layout and cooperative operation.Existing research seldom analyzes the homogeneity of multi-airport system from the perspective of route network analysis,and the attribute information of airport nodes in the airport route network is not appropriately integrated into the airport network.In order to solve this problem,a multi-airport system homogeneity analysis method based on airport attribute network representation learning is proposed.Firstly,the route network of a multi-airport system with attribute information is constructed.If there are flights between airports,an edge is added between airports,and regional attribute information is added for each airport node.Secondly,the airport attributes and the airport network vector are represented respectively.The airport attributes and the airport network vector are embedded into the unified airport representation vector space by the network representation learning method,and then the airport vector integrating the airport attributes and the airport network characteristics is obtained.By calculating the similarity of the airport vectors,it is convenient to calculate the degree of homogeneity between airports and the homogeneity of the multi-airport system.The experimental results on the Beijing-Tianjin-Hebei multi-airport system show that,compared with other existing algorithms,the homogeneity analysis method based on attributed network representation learning can get more consistent results with the current situation of Beijing-Tianjin-Hebei multi-airport system.

Path planning for UAVs formation reconfiguration based on Dubins trajectory

Multiple UAVs are usually deployed to provide robustness through redundancy and to accomplish surveillance,search,attack and rescue missions.Formation reconfiguration was inevitable during the flight when the mission was adjusted or the environment varied.Taking the typical formation reconfiguration from a triangular penetrating formation to a circular tracking formation for example,a path planning method based on Dubins trajectory and particle swarm optimization(PSO)algorithm is presented in this paper.The mathematic model of multiple UAVs formation reconfiguration was built firstly.According to the kinematic model of aerial vehicles,a process of dimensionality reduction was carried out to simplify the model based on Dubins trajectory.The PSO algorithm was adopted to resolve the optimization problem of formation reconfiguration path planning.Finally,the simulation and vehicles flight experiment are executed.Results show that the path planning method based on the Dubins trajectory and the PSO algorithm can generate feasible paths for vehicles on time,to guarantee the rapidity and effectiveness of formation reconfigurations.Furthermore,from the simulation results,the method is universal and could be extended easily to the path planning problem for different kinds of formation reconfigurations.

Reconfiguration of Spacecraft Cluster Based on Distributed Local Information

The reconstruction of spacecraft cluster based on local information and distributed strategy is investigated.Each spacecraft is an intelligent individual that can detect information within a limited range and can determine its behavior based on surrounding information.The objective of the cluster is to achieve the formation reconstruction with minimum fuel consumption.Based on the principle of dual pulse rendezvous maneuver,three target selection strategies are designed for collision avoidance.Strategy-1 determines the target point’s attribution according to the target’s distance when the target point conflicts and uses a unit pulse to avoid a collision.Strategy-2 changes the collision avoidance behavior.When two spacecraft meet more than once,the strategy switches the target points of the two spacecraft.In Strategy-3,the spacecraft closer to the target has higher priority in target allocation.Strategy-3 also switches the target points when two spacecraft encounter more than once.The three strategies for a given position,different completion times,and random position are compared.Numerical simulations show that all three strategies can accomplish the spacecraft cluster's reconfiguration under the specified requirements.Strategy-3 is better than Strategy-1 in all simulation cases in the sense of less fuel consumption with different completion times and given location,and it is more effective than Strategy-2 in most of the completion time.With a random initial position and given time,Strategy-3 is better than Strategy-1 in about 70%of the cases and more stable.

4D ConflictFree Trajectory Planning for Fixed-Wing UAV

Four-dimensional trajectory based operation(4D-TBO)is believed to enhance the planning and execution of efficient flights,reduce potential conflicts and resolve upcoming tremendous flight demand.Most of the 4D trajectory planning related studies have focused on manned aircraft instead of unmanned aerial vehicles(UAVs).This paper focuses on planning conflict-free 4D trajectories for fixed-wing UAVs before the departure or during the flight planning.A 4D trajectory generation technique based on Tau theory is developed,which can incorporate the time constraints over the waypoint sequence in the flight plan.Then the 4D trajectory is optimized by the particle swarm optimization(PSO)algorithm.Further simulations are performed to demonstrate the effectiveness of the proposed method,which would offer a good chance for integrating UAV into civil airspace in the future.

临空经济视域下高职院校航旅服务类专业的人才培养实施路径

临空经济是当前经济发展的重点内容。自贵阳临空经济示范区由国家发展改革委、中国民航局批准设立为中国第九个国家级临空经济示范区以来,贵州省积极推动临空经济的发展。当前,贵州临空经济发展迅速,但临空人才培养的相关研究较少,本文结合国内外相关研究的学术梳理与研究动态,探讨临空经济发展现状和贵州临空经济发展人才培养现状,同时深入分析临空经济发展背景下的岗位新需求和贵州省高职旅游管理服务临空经济人才培养的路径。

Improved S Surface Controller and Semi-physical Simulation for AUV

S surface controllers have been proven to provide effective motion control for an autonomous underwater vehicle (AUV).However, it is difficult to adjust their control parameters manually.Choosing the optimum parameters for the controller of a particular AUV is a significant challenge.To automate the process, a modified particle swarm optimization (MPSO) algorithm was proposed.It was based on immune theory, and used a nonlinear regression strategy for inertia weight to optimize AUV control parameters.A semi-physical simulation system for the AUV was developed as a platform to verify the proposed control method, and its structure was considered.The simulation results indicated that the semi-physical simulation platform was helpful, the optimization algorithm has good local and global searching abilities, and the method can be reliably used for an AUV.

Dynamic formation control for autonomous underwater vehicles

Path planning and formation structure forming are two of the most important problems for autonomous underwater vehicles(AUVs) to collaborate with each other.In this work,a dynamic formation model was proposed,in which several algorithms were developed for the complex underwater environment.Dimension changeable particle swarm algorithm was used to find an optimized path by dynamically adjusting the number and the distribution of the path nodes.Position relationship based obstacle avoidance algorithm was designed to detour along the edges of obstacles.Virtual potential point based formation-keeping algorithm was employed by incorporating dynamic strategies which were decided by the current states of the formation.The virtual potential point was used to keep the formation structure when the AUV or the formation was deviated.Simulation results show that an optimal path can be dynamically planned with fewer path nodes and smaller fitness,even with a concave obstacle.It has been also proven that different formation-keeping strategies can be adaptively selected and the formation can change its structure in a narrow area and restore back after passing the obstacle.

A GPS/BDS dual-mode positioning algorithm for a train based on CIPSO_EKF

When using global positioning system/BeiDou navigation satellite(GPS/BDS)dual-mode navigation system to locate a train,Kalman filter that is used to calculate train position has to be adjusted according to the features of the dual-mode observation.Due to multipath effect,positioning accuracy of present Kalman filter algorithm is really low.To solve this problem,a chaotic immune-vaccine particle swarm optimization_extended Kalman filter(CIPSO_EKF)algorithm is proposed to improve the output accuracy of the Kalman filter.By chaotic mapping and immunization,the particle swarm algorithm is first optimized,and then the optimized particle swarm algorithm is used to optimize the observation error covariance matrix.The optimal parameters are provided to the EKF,which can effectively reduce the impact of the observation value oscillation caused by multipath effect on positioning accuracy.At the same time,the train positioning results of EKF and CIPSO_EKF algorithms are compared.The eastward position errors and velocity errors show that CIPSO_EKF algorithm has faster convergence speed and higher real-time performance,which can effectively suppress interference and improve positioning accuracy.

Optimal control of a spacecraft with deployable solar arrays using particle swarm optimization algorithm

The optimal control problem of the multibody dynamics of a spacecraft in space, modeled as a central body with one-sided connected deployable solar arrays, is investigated. The dynamical equations of motion of the spacecraft with solar arrays are derived using the multibody dynamics method. The control of the attitude motion of a spacecraft system can be transformed into the motion planning problem of nonholonomic system when the initial angular momentum is zero. These are then used to investigate the motion planning of the spacecraft during solar arrays deployment via particle swarm optimization (PSO) and results are obtained with the optimal control input and the optimal trajectory. The results of numerical simulation show that this approach is effective for the control problem of the attitude of a spacecraft during the deployment process of its solar arrays.