Slip-based tire-road friction estimation
Automatica (Journal of IFAC)
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Computational Intelligence: Concepts to Implementations
Computational Intelligence: Concepts to Implementations
Current-Based Slippage Detection and Odometry Correction for Mobile Robots and Planetary Rovers
IEEE Transactions on Robotics
A Dynamic-Model-Based Wheel Slip Detector for Mobile Robots on Outdoor Terrain
IEEE Transactions on Robotics
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Improvement of dead reckoning accuracy is essential for robotic localization systems and has been intensively studied. However, existing solutions cannot provide accurate positioning when a robot suffers from changing dynamics such as wheel slip. In this paper, we propose a fuzzy-logic-assisted interacting multiple model (FLAIMM) framework to detect and compensate for wheel slip. Firstly, two different types of extended Kalman filter (EKF) are designed to consider both no-slip and slip dynamics of mobile robots. Then a fuzzy inference system (FIS) model for slip estimation is constructed using an adaptive neuro-fuzzy inference system (ANFIS). The trained model is utilized along with the two EKFs in the FLAIMM framework. The approach is evaluated using real data sets acquired with a robot driving in an indoor environment. The experimental results show that our approach improves position accuracy and works better in slip detection and compensation compared to the conventional multiple model approach.