基于函数逼近的资产防御任务快速分配方法
Fast Assignment in Asset-Guarding Engagements using Function Approximation
AI总结：
针对资产防御任务中追击-目标分配的代价矩阵构建计算量大的问题，提出用离线训练的神经网络逼近最短拦截时间，实现代价矩阵实时在线构建，且分配精度良好。
摘要：
本文研究由n个追击者尝试拦截n个目标的分配问题，涵盖静止目标以及向某一资产机动的目标两类场景。该分配算法依赖一个n×n代价矩阵，其中元素(i,j)代表追击者i拦截目标j的最短时间，而矩阵的每个元素都需求解一个非线性最优控制子问题，这类子问题计算量极大，因此分配任务的计算成本主要由代价矩阵的构建过程主导。我们提出采用神经网络对最短拦截时间进行函数逼近，神经网络采用离线训练方式，可实现代价矩阵的实时在线构建；此外，该函数逼近器具备足够精度，能得到合理的分配问题求解结果，在大多数场景下，逼近器得到的分配方案可实现最优的最坏情况拦截时间。我们通过多个追击者与目标数量递增的算例验证了所提方法的有效性。
英文摘要：
This letter considers assignment problems consisting of n pursuers attempting to intercept n targets. We consider stationary targets as well as targets maneuvering toward an asset. The assignment algorithm relies on an n x n cost matrix where entry (i, j) is the minimum time for pursuer i to intercept target j. Each entry of this matrix requires the solution of a nonlinear optimal control problem. This subproblem is computationally intensive and hence the computational cost of the assignment is dominated by the construction of the cost matrix. We propose to use neural networks for function approximation of the minimum time until intercept. The neural networks are trained offline, thus allowing for real-time online construction of cost matrices. Moreover, the function approximators have sufficient accuracy to obtain reasonable solutions to the assignment problem. In most cases, the approximators achieve assignments with optimal worst case intercept time. The proposed approach is demonstrated on several examples with increasing numbers of pursuers and targets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
