MODELLING OF MOBILE ROBOT DYNAMICS

This paper presents two approaches to modelling of mobile robot dynamics. First approach is based on physical modelling and second approach is based on experimental identification of mobile robot dynamics features. Model of mobile robot dynamics can then be used to improve the navigational system, especially path planing and localization modules. Localization module estimates mobile robot pose using its kinematic odometry model for pose prediction and additional sensor measurements for pose correction. Kinematic odometry models are simple, valid if mobile robot is travelling with low velocity, low acceleration and light load. Disadvantage is that they don’t take any dynamic constraints into account. This leads to errors in pose prediction, especially when significant control signal (translational and rotational velocity reference) changes occur. Problem lies in the fact that mobile robot can’t immediately change its current velocity to the desired value and mostly there exists a communication delay between the navigation computer and mobile robot micro-controller. Errors in predicted pose cause additional computations in path planning and localization modules. In order to reduce such pose prediction errors and considering that mobile robots are designed to travel at higher velocities and perform heavy duty work, mobile robot drive dynamics can be modelled and included as part of the navigational system. Proposed two modelling approaches are described and first results using a Pioneer 3DX mobile robot are presented. They are also compared regarding to complexity, accuracy and suitability of implementation as part of the mobile robot navigational system.

[1]  Ivan Petrovic,et al.  Dynamic window based approach to mobile robot motion control in the presence of moving obstacles , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

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

[3]  Anthony Stentz Optimal and Efficient Path Planning for Unknown and Dynamic Environments , 1993 .

[4]  Gaurav S. Sukhatme,et al.  Robust localization using relative and absolute position estimates , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[5]  Wahyudi,et al.  Dynamic Modelling and Adaptive Traction Control for Mobile Robots , 2004, ArXiv.

[6]  Mario Vasak,et al.  Sonar-based Pose Tracking of Indoor Mobile Robots , 2004 .

[7]  Rihard Karba,et al.  Modelling and simulation of a group of mobile robots , 2007, Simul. Model. Pract. Theory.

[8]  Edouard Ivanjko,et al.  Simple off-line Odometry Calibration of Differential Drive Mobile Robots , 2007 .

[9]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[10]  Bakir Lacevic,et al.  Nonlinear Motion Control of Mobile Robot Dynamic Model , 2008 .