期限(时间)
计算机科学
控制理论(社会学)
控制工程
人工智能
工程类
控制(管理)
物理
量子力学
作者
Yousef Emam,Paul Glotfelter,Sean M. Wilson,Gennaro Notomista,Magnus Egerstedt
出处
期刊:IEEE Transactions on Robotics
[Institute of Electrical and Electronics Engineers]
日期:2021-01-01
卷期号:38 (3): 1671-1685
被引量:2
标识
DOI:10.1109/tro.2021.3118965
摘要
Applications that require multi-robot 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 paper 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 modelling 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.
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