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Secure Multi-Robot Systems: Threat Identification and Countermeasure Development

NCJ Number
Date Published
145 pages

In this dissertation, the authors deal with problems concerning the security of multi-robot systems, covering key aspects such as threat identification methodologies and countermeasure developments.


Specifically, this dissertation proposes attack diagnosis methods and attack resilient control architectures to ensure the security of multi-robot systems with the following objectives: i) to introduce types of attacks that reveal specific vulnerabilities of robotic systems and corresponding design requirements to describe how a cyberthreat may affect the system functionality; ii) to develop an attack diagnosis scheme that enables each robot in multi-robot systems to continuously monitor undesirable actions caused by cyberthreat; iii) to design an attack-resilient control framework that adapts to undesirable situations so that the overall performance is guaranteed, thus mitigating the impact of attacks; and iv) to evaluate the performance of the proposed systems with both numerical simulations and experiments using real robots. 

With these objectives, the methodologies proposed in this dissertation are consist of two phases -- attack detection and countermeasure. The attack detection phase is to determine if any agent in multi-robot systems is being attacked. Any abrupt change or unexpected dynamic behavior is identified by a stochastic process-based local diagnosis system. The countermeasure phase is to protect the whole team from adversarial attacks. Attack resilient control algorithms are exploited to recover desirable performance and ensure continuous safe operation of the system. Thus, the proposed methods ensure the desired control performance of multi-robot systems in the presence of different types of attacks. This approach is also capable of providing attack resilient control algorithms of multi-vehicle systems in which each vehicle is modeled as a nonlinear system. (Published abstract provided)

Date Published: January 1, 2021