IQ-ASyMTRe: Synthesizing coalition formation and execution for tightly-coupled multirobot tasks
IQ-ASyMTRe: Synthesizing coalition formation and execution for tightly-coupled multirobot tasks
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IQ-ASyMTRe：为紧密耦合的多机器人任务综合联盟的形成和执行
DOI：
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发表时间：
2010
期刊：
影响因子：
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通讯作者：
L. Parker
中科院分区：
文献类型：
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作者：
Yu Zhang;L. Parker
本文提出了IQ-ASyMTRe体系结构，旨在解决紧耦合多机器人任务在单一框架内的联盟形成和执行问题。以前已经提出了许多任务分配算法，但没有显式地实现机器人能力的共享。受信息不变理论的启发，ASyMTRe被引入，它通过允许信息在不同机器人之间通过通信流动来实现感知和计算能力的共享。然而，ASyMTRe没有为联盟应该如何满足在执行所分配的任务时共享能力而引入的传感器约束提供解决方案。此外，不同信息类型1之间的转换是硬编码的，这限制了ASyMTRe的灵活性。此外，没有明确地捕捉实体(例如，机器人)和信息类型之间的关系，这可能从一开始就产生不可行解，因为所定义的信息类型可能不能很好地对应于当前环境设置。新的体系结构引入了完整的信息类型定义，以保证解决方案的可行性；它还显式地对信息转换进行建模。受我们以前工作的启发，IQ-ASyMTRe使用信息质量度量来指导机器人联盟在执行任务时满足传感器约束(通过能力共享引入)，从而提供了一个完整和通用的解决方案。我们在仿真和物理机器人上演示了该方法的能力，以形成和执行共享感官信息的联盟来实现紧密耦合的任务。
This paper presents the IQ-ASyMTRe architecture, which is aimed to address both coalition formation and execution for tightly-coupled multirobot tasks in a single framework. Many task allocation algorithms have been previously proposed without explicitly enabling the sharing of robot capabilities. Inspired by information invariant theory, ASyMTRe was introduced which enables the sharing of sensory and computational capabilities by allowing information to flow among different robots via communication. However, ASyMTRe does not provide a solution for how a coalition should satisfy sensor constraints introduced by the sharing of capabilities while executing the assigned task. Furthermore, conversions among different information types1 are hardcoded, which limits the flexibility of ASyMTRe. Moreover, relationships between entities (e.g., robots) and information types are not explicitly captured, which may produce infeasible solutions from the start, as the defined information type may not correspond well to the current environment settings. The new architecture introduces a complete definition of information type to guarantee the feasibility of solutions; it also explicitly models information conversions. Inspired by our previous work, IQ-ASyMTRe uses measures of information quality to guide robot coalitions to satisfy sensor constraints (introduced by capability sharing) while executing tasks, thus providing a complete and general solution. We demonstrate the capability of the approach both in simulation and on physical robots to form and execute coalitions that share sensory information to achieve tightly-coupled tasks.