Data-Driven Robust Barrier Functions for Safe, Long-Term Operation

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 condi...

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Vydáno v:IEEE transactions on robotics Ročník 38; číslo 3; s. 1671 - 1685
Hlavní autoři: Emam, Yousef, Glotfelter, Paul, Wilson, Sean, Notomista, Gennaro, Egerstedt, Magnus
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Collision avoidance
Compatibility
Computational geometry
Control systems
Controllers
Convexity
Dynamical systems
Estimation
Gaussian process
Gaussian processes
Inclusions
learning and adaptive systems
Modelling
Modular systems
Multiple robots
multirobot systems
robot safety
Robots
robust/adaptive control of robotic systems
Synthesis
Trajectory
Uncertainty
ISSN:1552-3098, 1941-0468
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Shrnutí: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.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2021.3118965