不可预测环境下机器人任务行为的鲁棒退化与增强

Nicolás D'Ippolito, V. Braberman, Daniel Sykes, Sebastián Uchitel
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引用次数: 2

摘要

基于时间逻辑的自动生成控制器的方法已被证明对运动、监视和导航等任务级规划非常有用。这些方法严重依赖于用于合成的环境模型的有效性。然而,为了降低复杂性和提供任务级别的保证,简化假设是不可避免的;在一个一切都可能出错的世界模型中,没有任何计划可以保证结果。在本文中,我们展示了我们的方法是如何通过引入模型堆栈来减少对单个模型的依赖,从而赋予系统基于日益增强的假设的增量保证,当环境不像预期的那样运行时支持优雅的退化,当环境像预期的那样运行时支持渐进的增强。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust degradation and enhancement of robot mission behaviour in unpredictable environments
Temporal logic based approaches that automatically generate controllers have been shown to be useful for mission level planning of motion, surveillance and navigation, among others. These approaches critically rely on the validity of the environment models used for synthesis. Yet simplifying assumptions are inevitable to reduce complexity and provide mission-level guarantees; no plan can guarantee results in a model of a world in which everything can go wrong. In this paper, we show how our approach, which reduces reliance on a single model by introducing a stack of models, can endow systems with incremental guarantees based on increasingly strengthened assumptions, supporting graceful degradation when the environment does not behave as expected, and progressive enhancement when it does.
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