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Hierarchical Planning for Self-reconfiguring Robots Using Module Kinematics

机译:使用模块运动学进行分层规划自我重新配置机器人

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Reconfiguration allows a self-reconfiguring modular robot to adapt to its environment. The reconfiguration planning problem is one of the key algorithmic challenges in realizing self-reconfiguration. Many existing successful approaches rely on grouping modules together to act as meta-modules. However, we are interested in reconfiguration planning that does not impose fixed meta-module relationships but instead forms cooperative relationships between modules dynamically. This approach avoids the need to hand-code meta-module motions and potentially allows reconfiguration with fewer modules. In this paper we present a general two-level reconfiguration framework. The top level plans in module-connector space using distributed dynamic programming. The lower level accepts a transition function for the kinematic model of the chosen module type as input. As an example, we implement such a transition function for the 3R, SuperBot-style module. Although not explored in this paper, this general approach is naturally extended to consider power use, clock time, or other quantities of interest.
机译:重新配置允许自我重新配置的模块化机器人适应其环境。重新配置计划问题是实现自我重新配置的关键算法挑战之一。许多现有成功的方法依赖于将模块分组,以充当元模块。但是,我们对重新配置规划感兴趣,不施加固定的元模块关系,而是动态地在模块之间形成合作关系。这种方法避免了需要手机代码元模块运动,并且可能允许用更少的模块重新配置。在本文中,我们展示了一般的双层重新配置框架。使用分布式动态编程模块连接器空间中的顶级计划。较低级别接受所选模块类型作为输入的所选模块类型的运动模型的过渡功能。作为示例,我们为3R,SuperBot式模块实现了这种转换功能。虽然没有探索本文,但这种一般方法自然地延伸以考虑电力使用,时钟时间或其他兴趣。

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