bSLAM navigation of a Wheeled Mobile Robot in presence of uncertainty in indoor environment

In this paper, we propose a behavior-based Simultaneous Localization and Map building (bSLAM) approach to deal with the following navigation problem of a Wheeled Mobile Robot (WMR): the behavior fusion, the uncertainty from measurements and modeling and the WMR control. Considering the multiple control objects, i.e., goal approaching and navigation safety, the behavior-based fuzzy path planner is established to deal with the behavior fusion problem by means of different interpretation of the environment from sensing system. Typically, the uncertainty of measurements together with the incremental error of the WMR self-localization is classified as the SLAM problem. In this research, we further consider the modeling uncertainty comparing with the SLAM problem so that the reduced-order SLAM is theoretically obtained via the variation approach in cope with the slipping and sliding effects. Therefore, the uncertainties are able to be effectively reduced at any motion time instead the time that the WMR revisits the well-known landmark in the SLAM algorithm. The effectiveness and the performance of the proposed bSLAM are verified via experiment. Finally, the results compared with SLAM and bSLAM approach show the error covariance is averagely diminished 26.60% in the complex environment.

[1]  Gianluca Antonelli,et al.  A calibration method for odometry of mobile robots based on the least-squares technique: theory and experimental validation , 2005, IEEE Transactions on Robotics.

[2]  Urbano Nunes,et al.  Path-following control of mobile robots in presence of uncertainties , 2005, IEEE Transactions on Robotics.

[3]  Jeffrey K. Uhlmann,et al.  Using covariance intersection for SLAM , 2007, Robotics Auton. Syst..

[4]  Dan Simon,et al.  Reduced Order Kalman Filtering without Model Reduction , 2007, Control. Intell. Syst..

[5]  Sergei Petrovich Novikov,et al.  The geometry of surfaces, transformation groups, and fields , 1984 .

[6]  Wan Kyun Chung,et al.  Unscented FastSLAM: A Robust and Efficient Solution to the SLAM Problem , 2008, IEEE Transactions on Robotics.

[7]  Marilena Vendittelli,et al.  WMR control via dynamic feedback linearization: design, implementation, and experimental validation , 2002, IEEE Trans. Control. Syst. Technol..

[8]  Li-Chen Fu,et al.  Navigation of a wheeled mobile robot in indoor environment by potential field based-fuzzy logic method , 2008, 2008 IEEE Workshop on Advanced robotics and Its Social Impacts.

[9]  Bernard Friedland,et al.  Separate-bias estimation with reduced-order Kalman filters , 1998, IEEE Trans. Autom. Control..

[10]  Danwei Wang,et al.  Modeling and Analysis of Skidding and Slipping in Wheeled Mobile Robots: Control Design Perspective , 2008, IEEE Transactions on Robotics.

[11]  K. Arras Feature-based robot navigation in known and unknown environments , 2003 .

[12]  José A. Castellanos,et al.  Robocentric map joining: Improving the consistency of EKF-SLAM , 2007, Robotics Auton. Syst..

[13]  José Luis Gordillo,et al.  A closed-form expression for the uncertainty in odometry position estimate of an autonomous vehicle , 2005, IEEE Transactions on Robotics.