Nonlinear Unknown Input and State Estimation Algorithm in Mobile Robots
This work addresses anomaly detection for mobile robots in real-world applications, but it appears incremental as it builds on prior research.
The authors tackled the problem of estimating unknown inputs and states in mobile robots with nonlinear dynamics and stochastic noise, resulting in the NUISE algorithm that detects and quantifies anomalies in sensors and actuators using sensor readings and control commands.
This technical report provides the description and the derivation of a novel nonlinear unknown input and state estimation algorithm (NUISE) for mobile robots. The algorithm is designed for real-world robots with nonlinear dynamic models and subject to stochastic noises on sensing and actuation. Leveraging sensor readings and planned control commands, the algorithm detects and quantifies anomalies on both sensors and actuators. Later, we elaborate the dynamic models of two distinctive mobile robots for the purpose of demonstrating the application of NUISE. This report serves as a supplementary document for [1].