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UCAV Formation Online Collaborative Trajectory Planning Using hp Adaptive Pseudospectral Method

机译:使用hp自适应伪谱方法的UCAV编队在线协同弹道规划

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摘要

In this paper, a novel approach to solving the formation online collaborative trajectory planning for fixed-wing Unmanned Combat Aerial Vehicles (UCAVs) is proposed. In order to describe the problem, the formation attack process which consists of communication framework and synergy elements is analyzed. The collaborative trajectory planning model which is based on avoiding the threat zones, reducing the execution time, and accomplishing the mission combines kinematics/dynamics model of UCAV with formation relative motion model to establish the optimal control problem. The approach based on hp adaptive pseudospectral method is presented to generate formation trajectory that satisfies the collaborative constraints. When a trigger event is detected, based on the offline planning, the online collaborative trajectory replanning using rolling horizon strategy is carried out. Simulated experiments which are divided into offline scenarios and online scenarios demonstrate that the proposed approach can generate trajectories which can meet the actual flight constraints, and the results verify the feasibility and stability of the proposed approach.
机译:本文提出了一种解决固定翼无人战斗机编队在线协同弹道规划的新方法。为了描述问题,分析了由通信框架和协同元素组成的编队攻击过程。基于避开威胁区域,减少执行时间并完成任务的协作轨迹规划模型将UCAV的运动学/动力学模型与编队相对运动模型相结合,以建立最优控制问题。提出了基于hp自适应伪谱方法的方法,以生成满足协同约束的地层轨迹。当检测到触发事件时,基于离线计划,使用滚动视野策略进行在线协作轨迹的重新计划。仿真实验分为离线情景和在线情景,实验结果表明所提方法能够产生满足实际飞行约束的航迹,结果验证了所提方法的可行性和稳定性。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第14期|3719762.1-3719762.25|共25页
  • 作者单位

    Air Force Engn Univ, Aeronaut Engn Coll, Xian 710038, Shaanxi, Peoples R China;

    Air Force Engn Univ, Aeronaut Engn Coll, Xian 710038, Shaanxi, Peoples R China;

    Air Force Engn Univ, Aeronaut Engn Coll, Xian 710038, Shaanxi, Peoples R China;

    Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Shaanxi, Peoples R China;

    Air Force Engn Univ, Aeronaut Engn Coll, Xian 710038, Shaanxi, Peoples R China;

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