Real-time distributed non-myopic task selection for heterogeneous robotic teams
Real-time distributed non-myopic task selection for heterogeneous robotic teams
复制标题
异构机器人团队的实时分布式非近视任务选择
DOI：
--
复制
发表时间：
2018
影响因子：
3.5
通讯作者：
Geoffrey A. Hollinger
中科院分区：
文献类型：
--
作者：
Andrew J. Smith;Graeme Best;Javier Yu;Geoffrey A. Hollinger
In this paper we introduce a novel algorithm for online distributed non-myopic task-selection in heterogeneous robotic teams. Our algorithm uses a temporal probabilistic representation that allows agents to evaluate their actions in the team’s joint action space while robots individually search their own action space. We use Monte-Carlo tree search to asymmetrically search through the robot’s individual action space while accounting for the probable future actions of their team members using the condensed temporal representation. This allows a distributed team of robots to non-myopically coordinate their actions in real-time. Our developed method can be applied across a wide range of tasks, robot team compositions, and reward functions. To evaluate our coordination method, we implemented it for a series of simulated and fielded hardware trials where we found that our coordination method is able to increase the cumulative team reward by a maximum of 47.2%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$47.2\%$$\end{document} in the simulated trials versus a distributed auction-based coordination. We also performed several outdoor hardware trials with a team of three quadcopters that increased the maximum cumulative reward by 24.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$24.5\%$$\end{document} versus a distributed auction-based coordination.
In this paper we introduce a novel algorithm for online distributed non-myopic task-selection in heterogeneous robotic teams. Our algorithm uses a temporal probabilistic representation that allows agents to evaluate their actions in the team’s joint action space while robots individually search their own action space. We use Monte-Carlo tree search to asymmetrically search through the robot’s individual action space while accounting for the probable future actions of their team members using the condensed temporal representation. This allows a distributed team of robots to non-myopically coordinate their actions in real-time. Our developed method can be applied across a wide range of tasks, robot team compositions, and reward functions. To evaluate our coordination method, we implemented it for a series of simulated and fielded hardware trials where we found that our coordination method is able to increase the cumulative team reward by a maximum of 47.2%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$47.2\%$$\end{document} in the simulated trials versus a distributed auction-based coordination. We also performed several outdoor hardware trials with a team of three quadcopters that increased the maximum cumulative reward by 24.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$24.5\%$$\end{document} versus a distributed auction-based coordination.
DOI：
10.1109/tciaig.2014.2346997
发表时间：
2015-12
影响因子：
--
作者：
L. Schaefers;M. Platzner
通讯作者：
L. Schaefers;M. Platzner