Data-Driven Robust Barrier Functions for Safe, Long-Term Operation
Data-Driven Robust Barrier Functions for Safe, Long-Term Operation
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数据驱动的强大屏障功能可实现安全、长期运行
DOI：
10.1109/tro.2021.3118965
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发表时间：
2021
影响因子：
7.8
通讯作者：
M. Egerstedt
中科院分区：
文献类型：
--
作者：
Y. Emam;Paul Glotfelter;S. Wilson;Gennaro Notomista;M. Egerstedt
需要多机器人系统在未知或非结构化环境中长时间独立运行的应用面临着一系列挑战，例如硬件退化、天气模式变化或不熟悉的地形。为了在这些不断变化的条件下有效运行，为长期自治应用开发的算法需要更加关注鲁棒性。因此，这项工作认为，以满足操作的关键约束的干扰系统的模块化的方式，这意味着不同的系统目标和干扰表示的兼容性的能力。为此，本文介绍了一种利用控制障碍函数（CBFs）的扰动控制仿射动力系统的约束满足的优化综合方法。上述框架是通过将扰动建模为凸壳的联合并利用先前关于微分包含的CBF的工作来构建的。这种干扰建模赠款不同的干扰估计方法兼容。例如，这项工作演示了如何通过高斯过程学习的干扰可以在所提出的框架中使用。这些估计的干扰被纳入建议的机器人合成框架，然后在不同的情况下进行测试的机器人舰队。
Applications that require multirobot systems to operate independently for extended periods of time in unknown or unstructured environments face a broad set of challenges, such as hardware degradation, changing weather patterns, or unfamiliar terrain. To operate effectively under these changing conditions, algorithms developed for long-term autonomy applications require a stronger focus on robustness. Consequently, this work considers the ability to satisfy the operation-critical constraints of a disturbed system in a modular fashion, which means compatibility with different system objectives and disturbance representations. Toward this end, this article introduces a controller-synthesis approach to constraint satisfaction for disturbed control-affine dynamical systems by utilizing control barrier functions (CBFs). The aforementioned framework is constructed by modeling the disturbance as a union of convex hulls and leveraging previous work on CBFs for differential inclusions. This method of disturbance modeling grants compatibility with different disturbance-estimation methods. For example, this work demonstrates how a disturbance learned via a Gaussian process may be utilized in the proposed framework. These estimated disturbances are incorporated into the proposed controller-synthesis framework which is then tested on a fleet of robots in different scenarios.
从反例和演示中学习李亚普诺夫（势）函数
DOI：
10.15607/rss.2017.xiii.049
发表时间：
2017
期刊：
Robotics: Science and Systems
影响因子：
--
作者：
Ravanbakhsh, Hadi;Sankaranarayanan, Sriram
通讯作者：
Sankaranarayanan, Sriram
影响因子：
3
作者：
Glotfelter, Paul;Cortes, Jorge;Egerstedt, Magnus
通讯作者：
Egerstedt, Magnus