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Supervisory control of multi-robot systems by disaggregation and scheduling of quasi-natural language commands

机译:通过准自然语言命令的分解和调度对多机器人系统进行监督控制

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Natural language is a very intuitive and efficient way to interact with robotic systems and to control mobile multi-robot systems (MRS). Because of this we designed a graphical user interface (GUI) which allows the creation of orders as sentences. The interface allows only sentences in a quasi-natural language that follow production rules and keeps them unambiguous. Because of these properties those sentences can be easily processed by a computer. This concept allows an intuitive input of high level tasks while we avoid natural language processing problems like complex, ambiguous sentences. In this paper we show how those high level tasks can be mapped into basic robot commands. This mapping can require complex conditions, e.g., spatial or temporal constrains, and depends on the capabilities of the MRS. In this paper we focus on how the result of this mapping can be scheduled. We used genetic algorithms to determine which task should be executed by which robot and in what order. A planning algorithm calculates the expected start and end times of every task and tries to satisfy the given constrains.
机译:自然语言是与机器人系统进行交互并控制移动多机器人系统(MRS)的一种非常直观有效的方法。因此,我们设计了一个图形用户界面(GUI),可以将订单创建为句子。该界面仅允许使用准自然语言的句子,这些句子遵循生产规则,并使其清晰。由于这些属性,这些句子可以很容易地由计算机处理。这个概念可以直观地输入高级任务,同时避免自然语言处理问题,例如复杂,歧义的句子。在本文中,我们展示了如何将这些高级任务映射到基本的机器人命令中。该映射可能需要复杂的条件,例如空间或时间约束,并且取决于MRS的功能。在本文中,我们重点介绍如何安排此映射的结果。我们使用遗传算法来确定应该由哪个机器人以什么顺序执行哪个任务。计划算法会计算每个任务的预期开始时间和结束时间,并尝试满足给定的约束。

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