Multi-Robot Task Allocation Games in Dynamically Changing Environments
Multi-Robot Task Allocation Games in Dynamically Changing Environments
复制标题
动态变化环境中的多机器人任务分配博弈
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Naomi Ehrich Leonard
中科院分区：
文献类型：
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作者：
Shinkyu Park;Yaofeng Desmond Zhong;Naomi Ehrich Leonard
我们提出了一种博弈论的多机器人任务分配框架，使大型机器人团队能够在动态变化的环境中优化分配任务。作为我们的主要贡献，我们设计了一种决策算法，定义机器人如何选择要执行的任务以及它们如何根据环境的变化反复修改任务选择。我们的收敛分析表明，该算法使机器人能够学习并渐进地实现最佳的固定任务分配。通过对多机器人垃圾收集应用程序进行实验，我们评估了算法对不断变化的环境的响应能力以及对单个机器人故障的恢复能力。
We propose a game-theoretic multi-robot task allocation framework that enables a large team of robots to optimally allocate tasks in dynamically changing environments. As our main contribution, we design a decision-making algorithm that defines how the robots select tasks to perform and how they repeatedly revise their task selections in response to changes in the environment. Our convergence analysis establishes that the algorithm enables the robots to learn and asymptotically achieve the optimal stationary task allocation. Through experiments with a multi-robot trash collection application, we assess the algorithm’s responsiveness to changing environments and resilience to failure of individual robots.