An Efficient Sampling-Based Algorithms Using Active Learning and Manifold Learning for Multiple Unmanned Aerial Vehicle Task Allocation under Uncertainty.

Xiaowei Fu,Bin Li,Hui Wang,Xiaoguang Gao

doi:10.3390/s18082645

Abstract

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.

Highlights

Multiple unmanned aerial vehicles (UAVs) have received increasing attention for their accomplishments in both military and civil applications [1,2,3,4]
References [16,17,18] used the concepts of interval uncertainty to model the uncertain factors of task allocation problem and the traditional auction algorithm, genetic algorithm and particle swarm optimization (PSO) are separately used to solve multi-UAV task allocation problems under uncertainty
This paper focuses on the task assignment of multi-UAVs with time-window constrain, which considers the uncertainty of task duration, and evaluates the impact of uncertain parameters on the task’s reward

Summary

Introduction

Multiple unmanned aerial vehicles (UAVs) have received increasing attention for their accomplishments in both military and civil applications [1,2,3,4]. In order to enhance the robustness of the task assignment algorithm, multi-UAV task assignment methods in uncertain environments have become a hot topic [6,7,8]. A lot of task allocation problem models [5,9] and task assignment solving algorithms [10,11,12,13,14,15] have been developed to meet the respective needs of various situations. Some intelligent methods have been proposed for multi-UAV task allocation problem under uncertain situation [7,8,16,17,18,19].

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Results

Conclusion