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- W4287210988 abstract "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." @default.
- W4287210988 created "2022-07-25" @default.
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- W4287210988 date "2021-04-15" @default.
- W4287210988 modified "2023-09-26" @default.
- W4287210988 title "Data-Driven Robust Barrier Functions for Safe, Long-Term Operation" @default.
- W4287210988 doi "https://doi.org/10.48550/arxiv.2104.07592" @default.
- W4287210988 hasPublicationYear "2021" @default.
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