未知环境下异构多无人机协同搜索打击中的联盟组建

为了提高多架异构无人机在未知环境下协同执行搜索打击任务时的效能,提出了一种未知环境下的异构多无人机协同搜索打击中的联盟组建方法,研究了实时性较高且适应于未知环境下的任务分配机制。以最小化目标打击时间和最小化联盟规模为优化指标,以满足同时打击和资源需求为约束条件,建立了联盟组建模型;为了提高联盟组建的实时性,提出了一种分阶次优联盟快速组建算法(MSOCFA)。算法复杂度分析说明了该算法是一个多项式时间算法,并且通过与粒子群优化算法进行仿真对比,验证了该算法具有较低的计算复杂度,满足实时性要求。为了使得多架无人机能自主协同完成搜索打击任务,设计了基于有限状态机(FSM)的多无人机分布式自主协同控制策略。仿真验证了未知环境下的异构多无人机协同搜索打击中的联盟组建方法的合理性和可行性。使用蒙特卡洛法验证了无人机数量和目标数量对联盟组建的影响,即无人机数量越多,目标数量越少,其平均任务完成时间越短。

Coalition Formation of Multiple Heterogeneous Unmanned Aerial Vehicles in Cooperative Search and Attack in Unknown Environment

A novel method for coalition formation of multiple heterogeneous unmanned aerial vehicles in cooperative search and attack in unknown environment is presented to improve the cooperative search and attack effectivenesses of multiple heterogeneous unmanned aerial vehicles. A coalition formation model is established based on the minimum target attack time and the minimum coalition scale with the constraints of required resources and simultaneous strike. A multistage sub-optimal coalition formation algorithm (MSOCFA) that has low computational complexity is proposed to solve the optimization problem of coalition formation. The performances of MSOCFA and partical swarm optimization algorithms are compared in terms of mission performance, complexity of algorithm and time taken to form the coalitions. In order to enable the multiple cooperative unmanned aerial vehicles to accomplish the search and attack missions autonomously, a distributed autonomous control strategy is proposed, which is based on the finite-state machine (FSM). The simulation results show the rationality, validity and high real-time performance of the proposed method for the coalition formation of multiple heterogeneous unmanned aerial vehicles in cooperative search and attack in the unknown environment. Monte Carlo method is employed to validate the impact of the number of unmanned aerial vehicles and targets on coalition formation. The reduced average mission completion time relates to the decreased number of targets and the increased the number of unmanned aerial vehicles.