School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China.
Shaanxi Key Laboratory of Integrated and Intelligent Navigation, Xi'an 710068, China.
Sensors (Basel). 2018 Aug 12;18(8):2645. doi: 10.3390/s18082645.
This paper presents a sampling-based approximation for multiple unmanned aerial vehicle (UAV) task allocation under uncertainty. Our goal is to reduce the amount of calculations and improve the accuracy of the algorithm. For this purpose, Gaussian process regression models are constructed from an uncertainty parameter and task reward sample set, and this training set is iteratively refined by active learning and manifold learning. Firstly, a manifold learning method is used to screen samples, and a sparse graph is constructed to represent the distribution of all samples through a small number of samples. Then, multi-points sampling is introduced into the active learning method to obtain the training set from the sparse graph quickly and efficiently. This proposed hybrid sampling strategy could select a limited number of representative samples to construct the training set. Simulation analyses demonstrate that our sampling-based algorithm can effectively get a high-precision evaluation model of the impact of uncertain parameters on task reward.
本文提出了一种基于采样的不确定性下多架无人机（UAV）任务分配近似方法。我们的目标是减少计算量并提高算法的准确性。为此，从不确定性参数和任务奖励样本集中构建高斯过程回归模型，通过主动学习和流形学习对该训练集进行迭代细化。首先，使用流形学习方法对样本进行筛选，通过少量样本构建稀疏图来表示所有样本的分布。然后，将多点采样引入主动学习方法中，以便从稀疏图中快速高效地获取训练集。所提出的混合采样策略可以选择有限数量的有代表性的样本来构建训练集。仿真分析表明，我们的基于采样的算法可以有效地获得不确定性参数对任务奖励影响的高精度评估模型。
