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Uncertainty-Aware Task Allocation for Distributed Autonomous Robots
arXiv - CS - Systems and Control Pub Date : 2021-07-21 , DOI: arxiv-2107.10350 Liang Sun, Leonardo Escamilla
arXiv - CS - Systems and Control Pub Date : 2021-07-21 , DOI: arxiv-2107.10350 Liang Sun, Leonardo Escamilla
This paper addresses task-allocation problems with uncertainty in situational
awareness for distributed autonomous robots (DARs). The uncertainty propagation
over a task-allocation process is done by using the Unscented transform that
uses the Sigma-Point sampling mechanism. It has great potential to be employed
for generic task-allocation schemes, in the sense that there is no need to
modify an existing task-allocation method that has been developed without
considering the uncertainty in the situational awareness. The proposed
framework was tested in a simulated environment where the decision-maker needs
to determine an optimal allocation of multiple locations assigned to multiple
mobile flying robots whose locations come as random variables of known mean and
covariance. The simulation result shows that the proposed stochastic task
allocation approach generates an assignment with 30% less overall cost than the
one without considering the uncertainty.
中文翻译:
分布式自主机器人的不确定性任务分配
本文解决了分布式自主机器人 (DAR) 态势感知不确定性的任务分配问题。任务分配过程中的不确定性传播是通过使用使用 Sigma-Point 采样机制的 Unscented 变换来完成的。它具有用于通用任务分配方案的巨大潜力,因为无需修改现有的任务分配方法,该方法已在不考虑态势感知中的不确定性的情况下开发。所提出的框架在模拟环境中进行了测试,其中决策者需要确定分配给多个移动飞行机器人的多个位置的最佳分配,这些位置作为已知均值和协方差的随机变量出现。
更新日期:2021-07-23
中文翻译:
分布式自主机器人的不确定性任务分配
本文解决了分布式自主机器人 (DAR) 态势感知不确定性的任务分配问题。任务分配过程中的不确定性传播是通过使用使用 Sigma-Point 采样机制的 Unscented 变换来完成的。它具有用于通用任务分配方案的巨大潜力,因为无需修改现有的任务分配方法,该方法已在不考虑态势感知中的不确定性的情况下开发。所提出的框架在模拟环境中进行了测试,其中决策者需要确定分配给多个移动飞行机器人的多个位置的最佳分配,这些位置作为已知均值和协方差的随机变量出现。