Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attacks Article Swipe

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Hongchao Zhang , Luyao Niu , Andrew Clark , Radha Poovendran ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2402.18677

Safety is a fundamental requirement of many robotic systems. Control barrier function (CBF)-based approaches have been proposed to guarantee the safety of robotic systems. However, the effectiveness of these approaches highly relies on the choice of CBFs. Inspired by the universal approximation power of neural networks, there is a growing trend toward representing CBFs using neural networks, leading to the notion of neural CBFs (NCBFs). Current NCBFs, however, are trained and deployed in benign environments, making them ineffective for scenarios where robotic systems experience sensor faults and attacks. In this paper, we study safety-critical control synthesis for robotic systems under sensor faults and attacks. Our main contribution is the development and synthesis of a new class of CBFs that we term fault tolerant neural control barrier function (FT-NCBF). We derive the necessary and sufficient conditions for FT-NCBFs to guarantee safety, and develop a data-driven method to learn FT-NCBFs by minimizing a loss function constructed using the derived conditions. Using the learned FT-NCBF, we synthesize a control input and formally prove the safety guarantee provided by our approach. We demonstrate our proposed approach using two case studies: obstacle avoidance problem for an autonomous mobile robot and spacecraft rendezvous problem, with code available via https://github.com/HongchaoZhang-HZ/FTNCBF.

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Computer Science

Fault (Geology)

Artificial Intelligence

Seismology

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Fault tolerance Computer science Fault (geology) Control (management) Distributed computing Artificial intelligence Geology Seismology

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2402.18677
PDF: https://arxiv.org/pdf/2402.18677
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4401066019

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DOI: https://doi.org/10.48550/arxiv.2402.18677

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Title: Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attacks

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-02-28

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Authors: Hongchao Zhang, Luyao Niu, Andrew Clark, Radha Poovendran

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Landing page: https://arxiv.org/abs/2402.18677

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Concepts: Fault tolerance, Computer science, Fault (geology), Control (management), Distributed computing, Artificial intelligence, Geology, Seismology

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Cited by: 0

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