A novel method for estimating the track-soil parameters based on Kalman and improved strong tracking filters.

A tracked vehicle has been widely used in exploring unknown environments and military fields. In current methods for suiting soil conditions, soil parameters need to be given and the traction performance cannot always be satisfied on soft soil. To solve the problem, it is essential to estimate track-soil parameters in real-time. Therefore, a detailed mathematical model is proposed for the first time. Furthermore, a novel algorithm which is composed of Kalman filter (KF) and improved strong tracking filter (STF) is developed for online track-soil estimation and named as KF-ISTF. By this method, the KF is used to estimate slip parameters, and the ISTF is used to estimate motion states. Then the key soil parameters can be estimated by using a suitable soil model. The experimental results show that equipped with the estimation algorithm, the proposed model can be used to estimate the track-soil parameters, and make the traction performance satisfied with soil conditions.

[1]  Zhihong Man,et al.  Sliding Mode Control for Steer-by-Wire Systems With AC Motors in Road Vehicles , 2013, IEEE Transactions on Industrial Electronics.

[2]  Jewon Lee,et al.  Fuzzy Vector Field Orientation Feedback Control-Based Slip Compensation for Trajectory Tracking Control of a Four Track Wheel Skid-steered Mobile Robot , 2013 .

[3]  K. Xiong,et al.  Adaptive robust extended Kalman filter for nonlinear stochastic systems , 2008 .

[4]  P. Frank,et al.  Strong tracking filtering of nonlinear time-varying stochastic systems with coloured noise: application to parameter estimation and empirical robustness analysis , 1996 .

[5]  D. J. Purdy,et al.  Road vehicle state estimation using low-cost GPS/INS , 2011 .

[6]  Laura E. Ray.  Estimation of Terrain Forces and Parameters for Rigid-Wheeled Vehicles , 2009, IEEE Transactions on Robotics.

[7]  Karl Reichard,et al.  Model‐based Prediction of Skid‐steer Robot Kinematics Using Online Estimation of Track Instantaneous Centers of Rotation , 2014, J. Field Robotics.

[8]  M. Michaek,et al.  Vector-Field-Orientation Feedback Control Method for a Differentially Driven Vehicle , 2010, IEEE Transactions on Control Systems Technology.

[9]  Kaspar Althoefer,et al.  Track–terrain modelling and traversability prediction for tracked vehicles on soft terrain , 2010 .

[10]  Chang-Soo Han,et al.  Development of a navigation algorithm with dead reckoning for unmanned ground vehicles , 2011 .

[11]  Chandramouli Padmanabhan,et al.  Longitudinal dynamics of a tracked vehicle: Simulation and experiment , 2012 .

[12]  Dongpu Cao,et al.  Modeling and validation of off-road vehicle ride dynamics , 2012 .

[13]  Abdulrahman H. Bajodah,et al.  Generalised dynamic inversion spacecraft control design methodologies , 2009 .

[14]  M. Kitano,et al.  A Mathematical Model for Spatial Motion of Tracked Vehicles on Soft Ground , 1992 .

[15]  Amir Khajepour,et al.  Robust state feedback stabilization of articulated steer vehicles , 2007 .

[16]  Huei Peng,et al.  Fast analytical models of wheeled locomotion in deformable terrain for mobile robots , 2013, Robotica.

[17]  Jo Yung Wong,et al.  Theory of ground vehicles , 1978 .

[18]  Guangjun Liu,et al.  Error-Tolerant Switched Robust Extended Kalman Filter With Application to Parameter Estimation of Wheel-Soil Interaction , 2014, IEEE Transactions on Control Systems Technology.

[19]  M. G. Bekker.  Introduction to Terrain-Vehicle Systems , 1969 .

[20]  Magnus Gäfvert,et al.  A 9-DOF Tractor-Semitrailer Dynamic Handling Model for Advanced Chassis Control Studies , 2004 .

[21]  Sauro Longhi,et al.  Development and experimental validation of an adaptive extended Kalman filter for the localization of mobile robots , 1999, IEEE Trans. Robotics Autom..