基于随机策略搜索的多机三维路径规划方法

Multi-UAVs 3D Path Planning Method Based on Random Strategy Search

针对传统无能耗约束的多无人机路径规划方法难以适应复杂山地作业环境的应急救援要求,提出了一种基于LSTM-DPPO(long short-term memory-distributed proximal policy optimization)框架的多无人机三维路径规划算法。利用LSTM长短期记忆神经网络提取出多无人机在各自飞行过程中的重要特征状态信息序列,经过多次迭代更新后得到一个最优网络参数模型,结合能耗生成最优的三维探测路径。实验结果表明:该方法相对于传统路径规划方法效果显著,能在能耗最小的前提下规划出最优探测路径。

In view of the difficulty of the traditional path planning method without energy consumption constraints to meet the emergency rescue requirements in the complex mountain operation environment,a three-dimensional path planning algorithm for multi-UAVs is proposed based on LSTM-DPPO(long short-term memory-distributed proximal policy optimization)framework.The LSTM long and short-term memory neural network is used to extract the important characteristic state information sequence of the multiple unmanned aerial vehicles in their respective flight process.After repeated iteration and updating,an optimal network parameter model is obtained.Combined with the energy consumption,the optimal 3D detection path is generated.Simulation experiments verify that the proposed method is more effective than the traditional path planning method and can plan the optimal detection path with the minimum energy consumption.