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研究了一种基于在线支持向量机的无人机航路规划方法,以保证无人机在完成任务时候能以最小的被发现概率以及最短航程安全到达目标点。首先建立多约束的无人机航路规划数学模型,并进行分析。应用A*算法产生初始航迹获取样本数据,在此基础上应用在线支持向量机具有在线训练、模型精确度高、需要样本少、泛化能力强等特点,实现无人机航路优化。最后将所研究的方法应用于无人机的航路规划仿真,仿真结果表明所研究的基于在线支持向量机的无人机航路规划方法是有效的。
A method of UAV route planning based on online support vector machine is studied in order to ensure that the UAV can reach the target point safely with the minimum probability of finding and the shortest flight when completing the mission. Firstly, a mathematic model of multi-constraint UAV route planning is established and analyzed. A * algorithm is used to generate the initial track to obtain the sample data. On this basis, the online support vector machine has the advantages of online training, high precision of model, less sample required and strong generalization ability. Finally, the method is applied to the UAV route planning simulation. The simulation results show that the UAV route planning method based on SVM is effective.