Sensor Fusion Based Discrete Kalman Filter for Outdoor Robot Navigation
The objective of the presented work is to implement the Kalman Filter into an application that reduces the influence of the environmental changes over the robot expected to navigate over a terrain of varying friction properties. The Discrete Kalman Filter is used to estimate the robot position, project the estimated current state ahead at time through time update and adjust the projected estimated state by an actual measurement at that time via the measurement update using the data coming from the infrared sensors, ultrasonic sensors and the visual sensor respectively. The navigation test has been performed in a real world environment and has been found to be robust.
[1] A. Pallegedara, L. Udawatta and D. Jayathilake. "Fault Tolerance
Sensor Fusion Approach to Mobile Robot Navigation".
Proceedings of the International Conference on Information and
Automation, pp.298-303, 2005.
[2] C.J.C.H. Wathinks. "Learning from Delayed Rewards". PhD Thesis,
King-s College, University of Cambridge, May 1989.
[3] T.Kohonen. " Self organizing formation of topologically correct feature
maps". Biological Cybernetics, 43:59-69, 1982.
[4] Z. Mbaitiga, Adaptive Fuzzy Knowledge Based Controller for
Autonomous Robot Motion Control, Journal of Computer Science
Vol.6, Issue10, pp: 1048-1055, 2010, Science Publications, New York,
USA.
[5] E.N. Skoundriamos and S.G Tzafesta, "Fault diagnosis vial local neural
networks". Mathematics Computer Simulation, vol.60, No.2002.
[6] F. Lizarralde et al."Mobile Robot Navigation using Sensor Fusion".
Proceedings of the IEEE International Conference on Robotics and
Automation(Taipei), pp:1-6, 2003.
[7] Z. Mbaitiga , Security Guard Robot Detecting Human Using Gaussian
Distribution Histogram Method. Journal of Computer Science Vol.6,
Issue10, pp: 1144-1150, 2010, Science Publications, New York, USA.
[8] F. Lizarralde, J.Wen and L.Hsu. "Feedback stabilization of nonholonomic
systems based on path space iteration". Proceedings of the 2nd
International Symposium on Methods and Models in Automation and
Robotics (MMAR-95),(Warsaw Poland) 1995.
[9] F. Lizarralde , J.Wen and L.Hsu. "A New Model predictive control
strategy for affine nonlinear control systems ". Proceedings of American
control conference,(San Diego (CA)), 1999.
[10] J.Tae-Seok,L,Jan-Myung,L.Bing Lam and S.K,Tso."A study on
mult-sensor fusion for mobile robot navigation in an indoor environment"
the 8th IEEE conference on mechanics and machine vision in practice,
pp:455-460, 2001,Hong Kong.
[11] J.Borenstein and Y. Koren. "The vector field histogram-fast obstacle
avoidance for mobile robots". IEEE transactions on robotics and
automation, 7(3):278-288,1991.
[1] A. Pallegedara, L. Udawatta and D. Jayathilake. "Fault Tolerance
Sensor Fusion Approach to Mobile Robot Navigation".
Proceedings of the International Conference on Information and
Automation, pp.298-303, 2005.
[2] C.J.C.H. Wathinks. "Learning from Delayed Rewards". PhD Thesis,
King-s College, University of Cambridge, May 1989.
[3] T.Kohonen. " Self organizing formation of topologically correct feature
maps". Biological Cybernetics, 43:59-69, 1982.
[4] Z. Mbaitiga, Adaptive Fuzzy Knowledge Based Controller for
Autonomous Robot Motion Control, Journal of Computer Science
Vol.6, Issue10, pp: 1048-1055, 2010, Science Publications, New York,
USA.
[5] E.N. Skoundriamos and S.G Tzafesta, "Fault diagnosis vial local neural
networks". Mathematics Computer Simulation, vol.60, No.2002.
[6] F. Lizarralde et al."Mobile Robot Navigation using Sensor Fusion".
Proceedings of the IEEE International Conference on Robotics and
Automation(Taipei), pp:1-6, 2003.
[7] Z. Mbaitiga , Security Guard Robot Detecting Human Using Gaussian
Distribution Histogram Method. Journal of Computer Science Vol.6,
Issue10, pp: 1144-1150, 2010, Science Publications, New York, USA.
[8] F. Lizarralde, J.Wen and L.Hsu. "Feedback stabilization of nonholonomic
systems based on path space iteration". Proceedings of the 2nd
International Symposium on Methods and Models in Automation and
Robotics (MMAR-95),(Warsaw Poland) 1995.
[9] F. Lizarralde , J.Wen and L.Hsu. "A New Model predictive control
strategy for affine nonlinear control systems ". Proceedings of American
control conference,(San Diego (CA)), 1999.
[10] J.Tae-Seok,L,Jan-Myung,L.Bing Lam and S.K,Tso."A study on
mult-sensor fusion for mobile robot navigation in an indoor environment"
the 8th IEEE conference on mechanics and machine vision in practice,
pp:455-460, 2001,Hong Kong.
[11] J.Borenstein and Y. Koren. "The vector field histogram-fast obstacle
avoidance for mobile robots". IEEE transactions on robotics and
automation, 7(3):278-288,1991.
@article{"International Journal of Information, Control and Computer Sciences:53863", author = "Mbaitiga Zacharie", title = "Sensor Fusion Based Discrete Kalman Filter for Outdoor Robot Navigation", abstract = "The objective of the presented work is to implement the Kalman Filter into an application that reduces the influence of the environmental changes over the robot expected to navigate over a terrain of varying friction properties. The Discrete Kalman Filter is used to estimate the robot position, project the estimated current state ahead at time through time update and adjust the projected estimated state by an actual measurement at that time via the measurement update using the data coming from the infrared sensors, ultrasonic sensors and the visual sensor respectively. The navigation test has been performed in a real world environment and has been found to be robust.
", keywords = "Kalman filter, sensors fusion, robot navigation.", volume = "5", number = "8", pages = "874-5", }
