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针对非确定环境下多无人机自主协同控制这一多约束、强耦合非线性优化问题,采用分层理论将其分解成三个相对独立子层,即协同感知层、环境态势理解层和协同全局重规划层.协同感知层借助“层协作感知”算子来进行多模信息融合,解决非确定环境下目标(包括静态确定目标和动态非确定目标)的感知与识别;环境态势理解层则是解决动态非确定环境更新,以及基于窗口势场法的障碍物(威胁目标)规避问题;而协同全局重规划层则是利用“层场景引擎”来实现多机非确定环境下的自主协同、路径快速寻优及状态决策.模拟结果显示构建的多机自主协同模型能较好地解决非确定环境下的路径寻优和状态决策问题.
Aiming at the multi-constrained and strongly coupled nonlinear optimization problem of multi-UAV autonomous cooperative control in uncertain environment, the hierarchical theory is used to decompose it into three relatively independent sub-layers, that is, cooperative perception layer, environmental situation understanding layer and coordination Global re-planning layer.Combining the perception layer with multi-mode information fusion through “layer collaboration perception ” operator to solve the perception and recognition of targets (including static and dynamic non-deterministic targets) in uncertain environment; understanding of environmental situation Layer is to solve the problem of dynamic nondeterministic environment update and obstacle (threat target) avoidance based on the window potential field method; and the coordinated global heavy programming layer is to use “layer scene engine ” to realize multi-machine non-deterministic environment Autonomic coordination, fast path optimization and state decision-making.The simulation results show that the proposed multi-machine autonomous collaborative model can better solve the problem of path optimization and state decision under non-deterministic environment.