Fuzzy tracking methods for mobile robots
Applications of fuzzy logic
Pure-pursuit reactive path tracking for nonholonomic mobile robots with a 2D laser scanner
EURASIP Journal on Advances in Signal Processing - Special issue on signal processing advances in robots and autonomy
Robotics and Autonomous Systems
Modeling and motion stability analysis of skid-steered mobile robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Power consumption modeling of skid-steer tracked mobile robots on rigid terrain
IEEE Transactions on Robotics
Dynamic modeling of a skid-steered wheeled vehicle with experimental verification
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Analysis and experimental verification for dynamic modeling of a skid-steered wheeled vehicle
IEEE Transactions on Robotics
Vector-Field-Orientation Tracking Control for a Mobile Vehicle Disturbed by the Skid-Slip Phenomena
Journal of Intelligent and Robotic Systems
Trajectory estimation of a skid-steering mobile robot incorporating free wheels
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part II
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In this paper we propose a kinematic approach for tracked mobile robots in order to improve motion control and pose estimation. Complex dynamics due to slippage and track-soil interactions make it difficult to predict the exact motion of the vehicle on the basis of track velocities. Nevertheless, real-time computations for autonomous navigation require an effective kinematics approximation without introducing dynamics in the loop. The proposed solution is based on the fact that the instantaneous centers of rotation (ICRs) of treads on the motion plane with respect to the vehicle are dynamics-dependent, but they lie within a bounded area. Thus, optimizing constant ICR positions for a particular terrain results in an approximate kinematic model for tracked mobile robots. Two different approaches are presented for off-line estimation of kinematic parameters: (i) simulation of the stationary response of the dynamic model for the whole velocity range of the vehicle; (ii) introduction of an experimental setup so that a genetic algorithm can produce the model from actual sensor readings. These methods have been evaluated for on-line odometric computations and low-level motion control with the Auriga-α mobile robot on a hard-surface flat soil at moderate speeds.