Title :
Particle Filtering for State Estimation in Nonlinear Industrial Systems
Author :
Rigatos, Gerasimos G.
Author_Institution :
Ind. Autom. Unit, Ind. Syst. Inst., Patras, Greece
Abstract :
State estimation is a major problem in industrial systems, particularly in industrial robotics. To this end, Gaussian and nonparametric filters have been developed. In this paper, the extended Kalman filter, which assumes Gaussian measurement noise, is compared with the particle filter, which does not make any assumption on the measurement noise distribution. As a case study, the estimation of the state vector of an industrial robot is used when measurements are available from an accelerometer that was mounted on the end effector of the robotic manipulator and from the encoders of the joints´ motors. It is shown that, in this kind of sensor fusion problem, the particle filter outperforms the extended Kalman filter, at the cost of more demanding computations.
Keywords :
Kalman filters; acceleration control; accelerometers; end effectors; industrial manipulators; nonlinear control systems; sensor fusion; state estimation; Gaussian filter; accelerometer measurement; end effector; extended Kalman filter; industrial robotics; joint motors encoding; nonlinear industrial systems; nonparametric filter; particle filtering; robotic manipulator; sensor fusion; state estimation; Extended Kalman filter (EKF); Gaussian filters; industrial robotic manipulator; nonparametric filters; particle filter (PF); sensor fusion; state estimation;
Journal_Title :
Instrumentation and Measurement, IEEE Transactions on
DOI :
10.1109/TIM.2009.2021212