Particle filtering for state estimation in industrial robotic systems

State estimation is a major problem in industrial systems. To this end, Gaussian and non-parametric filters have been developed. In this paper the extended Kalman filter which assumes Gaussian measurement noise is compared to 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 mounted on the end-effector of the robotic manipulator and from the encoders of the joint 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.

[1]  Gerasimos Rigatos,et al.  Adaptive fuzzy control for the ship steering problem , 2006 .

[2]  George W. Irwin,et al.  Understanding wireless networked control systems through simulation , 2005 .

[3]  Karl Henrik Johansson,et al.  An experimental study of exploiting multipath fading for robot communications , 2007, Robotics: Science and Systems.

[4]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[5]  François Le Gland,et al.  Particle and cell approximations for nonlinear filtering , 1995 .

[6]  M. Simandl,et al.  Sampling Densities of Particle Filter: A Survey and Comparison , 2007, 2007 American Control Conference.

[7]  Patrick Pérez,et al.  Data fusion for visual tracking with particles , 2004, Proceedings of the IEEE.

[8]  M. Blauer,et al.  State and parameter estimation for robotic manipulators using force measurements , 1987 .

[9]  W. Tian,et al.  Particle filter for sensor fusion in a land vehicle navigation system , 2005 .

[10]  Fredrik Gustafsson,et al.  Particle filters for positioning, navigation, and tracking , 2002, IEEE Trans. Signal Process..

[11]  M. Norrlof,et al.  Bayesian position estimation of an industrial robot using multiple sensors , 2004, Proceedings of the 2004 IEEE International Conference on Control Applications, 2004..

[12]  C. S. G. Lee,et al.  Robotics: Control, Sensing, Vision, and Intelligence , 1987 .

[13]  Arnaud Doucet,et al.  A survey of convergence results on particle filtering methods for practitioners , 2002, IEEE Trans. Signal Process..

[14]  Dan S. Necsulescu,et al.  Extended Kalman filter-based sensor fusion for operational space control of a robot arm , 2002, IEEE Trans. Instrum. Meas..

[15]  Spyros G. Tzafestas,et al.  Learning algorithms for neural networks with the Kalman filters , 1990, J. Intell. Robotic Syst..

[16]  Hassan K. Khalil,et al.  Closed-Loop Behavior of a Class of Nonlinear Systems Under EKF-Based Control , 2007, IEEE Transactions on Automatic Control.

[17]  Manuel Davy,et al.  Particle Filtering for Multisensor Data Fusion With Switching Observation Models: Application to Land Vehicle Positioning , 2007, IEEE Transactions on Signal Processing.

[18]  Johann Fichou,et al.  Particle-based methods for parameter estimation and tracking: Numerical experiments , 2004 .

[19]  Ren C. Luo,et al.  Multisensor integration and fusion in intelligent systems , 1989, IEEE Trans. Syst. Man Cybern..

[20]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[21]  Thomas B. Schön,et al.  Fast particle filters for multi-rate sensors , 2007, 2007 15th European Signal Processing Conference.

[22]  Spyros G. Tzafestas,et al.  Mobile robot motion control in partially unknown environments using a sliding-mode fuzzy-logic controller , 2000, Robotics Auton. Syst..

[23]  E. Kamen,et al.  Introduction to Optimal Estimation , 1999 .

[24]  Fabien Campillo,et al.  FILTRAGE PARTICULAIRE & MODÈLES DE MARKOV CACHÉS , 2006 .

[25]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[26]  Gerasimos G. Rigatos,et al.  Fuzzy stochastic automata for intelligent vehicle control , 2003, IEEE Trans. Ind. Electron..

[27]  Riku Jäntti,et al.  Simulation case studies of wireless networked control systems , 2007, PM2HW2N '07.

[28]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[29]  Frank L. Lewis,et al.  Neural Network Control Of Robot Manipulators And Non-Linear Systems , 1998 .

[30]  Gerasimos Rigatos,et al.  Extended Kalman filtering for fuzzy modelling and multi-sensor fusion , 2007 .

[31]  John Odentrantz,et al.  Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues , 2000, Technometrics.

[32]  Spyros G. Tzafestas,et al.  H α tracking of uncertain SISO nonlinear systems: an observer-based adaptive fuzzy approach , 2007, Int. J. Syst. Sci..

[33]  Petar M. Djuric,et al.  Resampling algorithms and architectures for distributed particle filters , 2005, IEEE Transactions on Signal Processing.

[34]  S. Fabri,et al.  Particle filters for recursive model selection in linear and nonlinear system identification , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[35]  Neil J. Gordon,et al.  A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking , 2002, IEEE Trans. Signal Process..

[36]  Spyros G. Tzafestas,et al.  Neural and Neurofuzzy FELA Adaptive Robot Control Using Feedforward and Counterpropagation Networks , 1998, J. Intell. Robotic Syst..

[37]  Joaquín Míguez,et al.  Analysis of parallelizable resampling algorithms for particle filtering , 2007, Signal Process..

[38]  Visakan Kadirkamanathan,et al.  Particle filtering based likelihood ratio approach to fault diagnosis in nonlinear stochastic systems , 2001, IEEE Trans. Syst. Man Cybern. Part C.