异构型多UAV协同侦察最优化任务决策研究
Optimal Task Decision-Making for Heterogeneous Multi-UAV Cooperation Reconnaissance

针对异构型多无人机（unmanned aerial vehicle，UAV）协同侦察任务决策问题，研究给定一组具有不同侦察载荷能力的UAV对任务场景中的多个任务区进行信息侦察，如何进行非重叠的任务分配从而在给定限制下使UAV获取到的侦察信息增益最大。首先构建了任务区侦察决策问题的"资源-需求"矩阵，根据多任务区协同侦察的特点，提出了考虑多UAV总飞行航程与UAV到达任务区距离影响因素的UAV到达时间指标与"UAV-任务区"时间指标，建立了多UAV协同多任务区侦察决策模型。最后提出了一种分布式的扩展CBBA（extension consensus-based bundle algorithm，ECBBA）算法并进行了仿真分析。仿真结果表明ECBBA算法能够快速高效的得到各个UAV的任务集以及相应的任务执行路径，仿真运行时间明显优越传统的启发式算法，为多UAV多任务区的遍历侦察问题提供了科学的决策依据。
This paper considers the problem of decentralized task assignment in a network of heterogeneous Unmanned Aerial Vehicles (UAVs). Given a set of task-areas need to be reconnoitered and a set of UAVs with different capabilities, this problem concerns the non-overlapping allocation of task-areas to UAVs maximizing the sum of the UAV's utility functions. We present a decentralized algorithm to solve this task allocation problem, which can be viewed as an extension of the consensus-based bundle algorithm (CBBA) that can easily converges to a conflict free and feasible solution. First of all, based on the characteristics of UAVs and task-areas resources needed, the "resources-demand" matrix is put forward. Next, the UAV arrival time index and the UAV-task time index considering the UAV flight distance factor and the linear distance factor from UAV to task-areas are consider. All this indexes be integrated into the standard CBBA algorithm. Numerical simulation results demonstrate that the proposed methods can obtain the maximum rewards more efficiently than the traditional heuristic algorithm