面向林火持续侦察的多无人机分布式控制方法

为解决目前面向林火持续侦察多无人机（UAV）协同控制实用性与自主性不足的问题，基于蔓延速度诱导元胞自动机（SVICA）林火蔓延算法、无人机与传感器建模，构建了较为真实的三维多无人机火场侦察仿真环境与侦察效能指标，提出了一种面向林火持续侦察的多无人机双层分布式控制架构，在行动层基于强化学习训练的人工神经网络（ANN），实现了有风条件下无人机自主火场环绕与地形跟随功能，在策略层设计通过时域均匀分布算法进行各无人机空速的离散自主调节，最终达到多无人机林火持续侦察时域分布的均匀性与即时性目的。通过一系列数值仿真实验，验证了所提出的无人机分布控制策略在无人机损失和补充突发情况下的自适应性。基于无人机数量与侦察效能指标关系的实验与研究，定义了无人机出动阈值并验证了无人机长时间出动回收策略。最终实验结果表明，针对林火持续侦察任务，所提出的多无人机分布式控制方法具备一定的有效性与实用性。

Distributed control method of multiple UAVs for persistent wildfire surveillance

In order to improve the practicality and autonomy in multiple UAV collaborative control for persistent wildfire surveillance, based on the Spread Vector Induced Celluar Automata (SVICA) fire spread algorithm, UAV kinematic, and sensor modeling, a more realistic three-dimension simulation environment for multiple UAV fire surveillance and the surveillance effectiveness indicators are constructed. A two-layer distributed control architecture for multiple UAV is proposed. Based on the reinforcement learning trained Artificial Neural Networks (ANN), the operational layer control realized the autonomous wildfire surrounding and terrain following under windy conditions. In the tactical layer, through the temporal even-distribution algorithm, the discrete airspeed adjustment of each UAV is performed to achieve the uniformity and immediacy of temporal distribution of UAVs in persistent wildfire surveillance. Then through a series of numerical experiments, the adaptability of the proposed distribution control method is verified under sudden UAV loss and supplement during the surveillance. In addition, based on the research on the relationship between the number of UAVs and the surveillance effectiveness, the UAV dispatch threshold is defined and enduring UAV dispatch and recycling strategy is examined. The final simulation results show that the proposed multiple UAV distributed control algorithm is effective and practical for persistent surveillance of wildfire.