Kalman filtering: theory and practice
Kalman filtering: theory and practice
Slip-based tire-road friction estimation
Automatica (Journal of IFAC)
A Smoothly Constrained Kalman Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient failure detection on mobile robots using particle filters with Gaussian process proposals
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Applications of support vector machines to speech recognition
IEEE Transactions on Signal Processing
Vibration-based terrain classification for planetary exploration rovers
IEEE Transactions on Robotics
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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This paper introduces a signal-recognition based approach for detecting autonomous mobile robot immobilization on outdoor terrain. The technique utilizes a support vector machine classifier to form class boundaries in a feature space composed of statistics related to inertial and (optional) wheel speed measurements. The proposed algorithm is validated using experimental data collected with an autonomous robot operating in an outdoor environment. Additionally, two detector fusion techniques are proposed to combine the outputs of multiple immobilization detectors. One technique is proposed to minimize false immobilization detections. A second technique is proposed to increase overall detection accuracy while maintaining rapid detector response. The two fusion techniques are demonstrated experimentally using the detection algorithm proposed in this work and a dynamic model-based algorithm. It is shown that the proposed techniques can be used to rapidly and robustly detect mobile robot immobilization in outdoor environments, even in the absence of absolute position information.