A Modeling Tool for Reconfigurable Skills in ROS

D. Bozhinoski, Esther Aguado, M. Oviedo, C. H. Corbato, R. Sanz, A. Wąsowski
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引用次数: 5

Abstract

Known attempts to build autonomous robots rely on complex control architectures, often implemented with the Robot Operating System platform (ROS). The implementation of adaptable architectures is very often ad hoc, quickly gets cumbersome and expensive. Reusable solutions that support complex, runtime reasoning for robot adaptation have been seen in the adoption of ontologies. While the usage of ontologies significantly increases system reuse and maintainability, it requires additional effort from the application developers to translate requirements into formal rules that can be used by an ontological reasoner. In this paper, we present a design tool that facilitates the specification of reconfigurable robot skills. Based on the specified skills, we generate corresponding runtime models for self-adaptation that can be directly deployed to a running robot that uses a reasoning approach based on ontologies. We demonstrate the applicability of the tool in a real robot performing a patrolling mission at a university campus.
ROS中可重构技能的建模工具
构建自主机器人的已知尝试依赖于复杂的控制体系结构,通常使用机器人操作系统平台(ROS)实现。可适应架构的实现通常是临时的,很快就会变得麻烦和昂贵。支持机器人适应的复杂运行时推理的可重用解决方案已经在本体的采用中得到了体现。虽然本体的使用显著地提高了系统的重用性和可维护性,但它需要应用程序开发人员进行额外的工作,将需求转换为可由本体推理器使用的正式规则。在本文中,我们提出了一个设计工具,有助于规范可重构的机器人技能。基于指定的技能,我们生成相应的自适应运行时模型,这些模型可以直接部署到使用基于本体的推理方法的运行机器人中。我们在一个大学校园执行巡逻任务的真实机器人中演示了该工具的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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