复杂任务场景无人机集群自组织侦察建模与仿真

基于agent的作战建模与仿真是无人机集群侦察效能评估的重要方法,但以往方法中agent行为机制 和模型结构难以描述复杂任务场景下的自组织侦察行为。以反海盗侦察任务为例,提出一种面向集群自组织 侦察的作战建模与仿真方法。设计多agent分层复合行为机制和可组合模块化agent模型结构,通过简单行为 的组合描述复杂行为;通过信息素图模块与航路管理模块的交互,实现自组织航路规划与飞行控制;并通过仿 真算例,来验证方法的可行性,对比分析不同航路规划方法的侦察效能。结果表明:自组织航路规划对中断航 路侦察的动态事件的适应性更强;对于大区域、长时间、低目标密度的区域覆盖侦察任务,仅通过改进航路规划 方法来降低访问间隔难以显著提高目标发现概率。

Modelling and Simulation of UAV Swarm Self-organized Surveillance in Complex Mission Scenarios

Agent-based combat modelling and simulation is an important method for UAV swarm surveillance effectiveness evaluation. The agent behaviour mechanisms and model structures in previous agent-based combat modelling and simulation studies are difficult to describe self-organized surveillance behaviours in complex mission scenarios. In this paper, taking a counter-piracy surveillance mission as an example, a combat modelling and simulation method oriented for swarm self-organized surveillance is proposed. By the hierarchical composite behaviour mechanism and the composable modular model structure, complex behaviours are described by the composition of simple behaviours, and the self-organized path planning and flight control are realized by the interaction of the pheromone map module and the path management module. Simulation experiments are conducted to demonstrate the feasibility of the proposed method, and compare the surveillance effectiveness with different path planning methods. The results show that, self-organized path planning is more adaptive to dynamic events interrupting surveillance. In large-area, long-duration and low-target-density area coverage surveillance missions, merely decreasing revisit intervals by improving path planning methods cannot significantly increase the probability of finding targets.