基于目标信息估计的分布式局部协调任务分配方法

分布式决策是提高群体自主性的关键技术之一.以侦查类无人机(unmanned search aerial vehicles,USAV)和打击类无人机(unmanned combat aerial vehicles,UCAV)执行协同搜索、攻击灰色目标区域问题为背景,建立了一种考虑局部链式通信、无人机飞行性能和任务执行能力等多约束的分布式任务分配模型,基于贝叶斯定理将任务空间的连续/离散不确定量用任务收益值量化描述.然后,提出了一种基于一致性协调算法的在线协同策略,并利用一致协调理论建立了一种冲突调解规则,在此基础上,设计了一种分布式任务分配求解算法,能够实现多USAV,UCAV的协同多任务快速分配.最后,通过数值仿真,验证了本文算法求解不确定空间任务分配问题的可行性和快速性.

Distributed task assignment method based on local information consensus and target estimation

Distributed decision-making is one of the key components to improve the autonomy of robotic agents. Based on the problem of robust task assignment for a fleet of unmanned combat aerial vehicles (UCAV) and unmanned search aerial vehicles (USAV) to targets under environmental uncertainty, a distributed task assignment model which considering multi-constraints such as sparse connected network, UAV performance and task execution ability is given firstly. Bayes law is adopted to handle both continuous uncertainties and discrete uncertainties in mission space, then an on-line cooperative approach based on consensus algorithm is proposed for the given problem, and the decision rules for conflict resolution is given afterwards, and then a distributed task assignment algorithm which can simultaneously allocates UCAVs and USAVs fastly, is proposed. Finally, aspects of feasibility and rapidity of the proposed algorithm is verified according to the simulation results.