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SELF-MANAGEMENT IN A CONTROL ARCHITECTURE FOR PARALLEL KINEMATIC ROBOTS

机译:并联运动机器人的控制架构中的自我管理

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摘要

Maintainability, extendibility and reusability of components in the design of robot control architectures is a major challenge. Parallel kinematic robots feature a wide variety of structures and applications. They are subject to easy reconfiguration because of the passive structure limbs. This class of robots requires more extensive calculations in their control laws than serial manipulators. During complex motion tasks, such as the ones required in assembly sequences, the algorithmic load may also vary over time. However, no generic control approach exists in order to reduce the complexity of control design for these kind of robots. In this paper the authors introduce an architecture for handling and assembly applications featuring self-management techniques as an approach to tackle these problems. The existing architecture features a modular and layered design. Concepts of self-management and self-optimization applied to this architecture are outlined. These properties are realized by the integration of self-managers within crucial system components. The mechanisms are extended for a future distributed version of the architecture. Real-time properties are guaranteed by an online formal analysis that verifies planned adaptations before realizing them.
机译:在机器人控制架构的设计中,组件的可维护性,可扩展性和可重用性是一个重大挑战。并联运动机器人具有多种结构和应用。由于采用被动结构肢体,因此易于重新配置。与串行机械手相比,此类机器人在控制律方面需要进行更广泛的计算。在复杂的运动任务中(例如装配顺序中要求的任务),算法负荷也可能随时间变化。但是,不存在通用的控制方法来降低这类机器人的控制设计的复杂性。在本文中,作者介绍了一种具有自我管理技术的处理和组装应用程序体系结构,以解决这些问题。现有的体系结构具有模块化和分层设计。概述了应用于此体系结构的自我管理和自我优化的概念。这些特性是通过在关键系统组件中集成自我管理器来实现的。这些机制针对体系结构的未来分布式版本进行了扩展。实时属性是通过在线形式分析来保证的,该形式分析会在实现之前对计划的适应进行验证。

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