Distributed Interacting Multiple Filters for Fault Diagnosis of Navigation Sensors in a Robotic System
提出一种分布式交互多模型方法，用于机器人系统中惯性传感器和相机传感器的故障检测与隔离，通过分解系统为可观测子系统并设计分布式卡尔曼滤波器，实验验证了其有效性。
In this paper, a distributed interacting multiple model-based fault detection and isolation (FDI) scheme is presented for FDI of navigation sensors composed of inertial (accelerometers and gyroscopes) and camera sensors in a robotic system in which noisy and erroneous measurements of microelectro mechanical system (MEMS)-based inertial sensors are fused with a photogrammetric camera. Multiple models are employed to describe different scenarios of hard faults (failures) in the sensors where the models are different in each scenario because inertial sensor drifts (as soft or partial faults) are also modeled and augmented to the motion state parameters. Having several faulty modes due to the possibility of single and multiple failures in the sensors, it is proposed in this paper to decompose the system to interacting observable subsystems with reduced size and decoupled model sets. The system and the corresponding model set are decomposed using a graph theoretic decomposition approach. Distributed interacting multiple Kalman and extended Kalman filters are then designed for the purpose of FDI. Experimental results based on data from a 3-D MEMS inertial measurement unit and 3-D camera system are used to demonstrate the efficiency of the method.