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Subsumption Architecture for Enabling Strategic Coordination of Robot Swarms in a Gaming Scenario

机译:在游戏场景中实现机器人群体战略协调的包容性体系结构

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The field of swarm robotics breaks away from traditional research by maximizing the performance of a group - swarm - of limited robots instead of optimizing the intelligence of a single robot. Similar to current-generation strategy video games, the player controls groups of units - squads - instead of the individual participants. These individuals are rather unintelligent robots, capable of little more than navigating and using their weapons. However, clever control of the squads of autonomous robots by the game players can make for intense, strategic matches. The gaming framework presented in this article provides players with strategic coordination of robot squads. The developed swarm intelligence techniques break up complex squad commands into several commands for each robot using robot formations and path finding while avoiding obstacles. These algorithms are validated through a 'Capture the Flag' gaming scenario where a complex squad command is split up into several robot commands in a matter of milliseconds.
机译:群机器人技术领域突破了传统研究,其方法是最大化一组有限机器人的性能,而不是优化单个机器人的智能。类似于当前的策略视频游戏,玩家控制的单位组-小队-而不是单个参与者。这些人是相当笨拙的机器人,除了导航和使用武器外,还具有其他功能。但是,游戏者对自动机器人小队的巧妙控制可以进行激烈的战略比赛。本文介绍的游戏框架为玩家提供了机器人小队的战略协调。先进的群体智能技术利用机器人的编队和寻路功能,同时避开障碍物,将复杂的小队命令分解成针对每个机器人的多个命令。这些算法通过“捕获旗帜”游戏场景进行了验证,其中复杂的小队命令在几毫秒内被分解为多个机器人命令。

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