Secure Event-Triggered Distributed Finite-Memory Localization for Multiple Mobile Robots Under Cyberattacks

This study proposes a novel event-triggered localization algorithm, based on finite-memory estimators (FMEs), which is robust against cyberattacks. A group of mobile robots was controlled by an operating system located at a remote site. Each robot transmitted measurement information to the operating system through wireless communication, where event-triggered transmission was adopted to save communication resources. The proposed FME-based novel event-triggering mechanism performed triggering effectively by predicting the performance of the remote localization system. Cyberattacks, such as denial of service and deception attacks, may occur and disturb the transmission of measurement information. To address this, we propose a secure localization algorithm by deriving a new FME using the Frobenius norm method, with the measurement model reflecting the effect of cyberattacks. The resulting algorithm, called secure event-triggered distributed finite-memory localization, is more robust against cyberattacks than existing algorithms. The algorithm's performance was demonstrated by comparing it with two existing event-triggered distributed localization algorithms, under the condition with/without cyberattacks, through experiments.