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机器学习论文:基于贪婪的多机器人系统任务分配示例(Sample Greedy Based Task Allocation for Multiple Robot

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632956335 发表于 2019-1-11 11:49:50 | 显示全部楼层 |阅读模式
632956335 2019-1-11 11:49:50 104 0 显示全部楼层
机器学习论文:基于贪婪的多机器人系统任务分配示例(Sample Greedy Based Task Allocation for Multiple Robot Systems)本文讨论了多机器人系统的任务分配问题。任务分配问题的主要问题是固有的复杂性,这使得在合理的时间内找到最佳解决方案几乎是不可能的。为了解决这个问题,本文开发了一种可以通过利用子模块概念和抽样过程进行分散的任务分配算法。理论分析表明,该算法可以为单调子模块情况提供$ 1/2 $的近似保证,并且可以提供$ 1/4 $ forthe平均意义上的非单调子模块情形,具有多项式时间复杂性。为了检验所提算法的性能并验证理论分析结果,我们设计了一个任务分配问题并进行了数值模拟。仿真结果证实,所提出的算法实现了解决方案质量,这与单调情况下的现有技术算法相当,并且非单调内容的质量更好,计算复杂度显着降低。
This paper addresses the task allocation problem for multi-robot systems.Themain issue with the task allocation problem is inherent complexity that makesfinding an optimal solution within a reasonable time almost impossible.To handthe issue, this paper develops a task allocation algorithm that can bedecentralised by leveraging the submodularity concepts and sampling process.The theoretical analysis reveals that the proposed algorithm can provideapproximation guarantee of $1/2$ for the monotone submodular case and $1/4$ forthenon-monotone submodular case in average sense with polynomial timecomplexity.To examine the performance of the proposed algorithm and validatethe theoretical analysis results, we design a task allocation problem andperform numerical simulations.The simulation results confirm that the proposedalgorithm achieves solution quality, which is comparable to a state-of-the-artalgorithm in the monotone case, and much better quality in the non-monotonecase with significantly less computational complexity.机器学习论文:基于贪婪的多机器人系统任务分配示例(Sample Greedy Based Task Allocation for Multiple Robot Systems) yiwXov70Wvxiip88.jpg
URL地址:https://arxiv.org/abs/1901.03258     ----pdf下载地址:https://arxiv.org/pdf/1901.03258    ----机器学习论文:基于贪婪的多机器人系统任务分配示例(Sample Greedy Based Task Allocation for Multiple Robot Systems)
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