协同多无人机系统中多运动目标的公平观测

Junbo Zou, Dian-xi Shi
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引用次数: 0

摘要

多机器人运动目标协同观测(Cooperative Multi-robot observation of moving targets, CMOMMT)是指一组自主移动机器人在有限的传感器范围内对运动目标进行观测的问题。机器人协调规划目标的运动,以最大限度地延长每个目标出现在至少一个机器人的传感范围内的观察时间。提出了一种基于无人机的CMOMMT方案多目标优化模型,该模型将不同目标间的观测公平性作为附加目标。该模型使用四叉树数据结构对无人机的运动决策进行建模,以最大限度地提高观测的持续时间和可变质量(分辨率)。对于任何给定的未来时间,在贝叶斯框架中估计每个目标的概率占用图。实验结果表明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fair Observation of Multiple Moving Targets in Cooperative Multi-UAV Systems
Cooperative Multi-robot observation of moving the targets (CMOMMT) represents the problem in which a group of autonomous mobile robots equipped with a limited range of sensors that can be used to keep moving targets in observation. The robots plan the movement of the target in coordination to maximize the observation time during which each target appears within the sensing range of at least one robot. We present a novel multi-objective optimization model based on UAVs for CMOMMT schemes which features fairness of observation among different targets as an additional objective. The proposed model uses a quad-tree data structure to model the movement decisions of UAVs in order to maximize duration and the variable qualities (resolutions) of observations. For any given future time, a probabilistic occupancy map for each target is estimated in a Bayesian framework. The experiment results display the effectiveness of the proposed method.
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