Neural Networks
Practical neural network recipes in C++
Practical neural network recipes in C++
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Online terrain parameter estimation for wheeled mobile robots with application to planetary rovers
IEEE Transactions on Robotics
Adaptive Bayesian filtering for vibration-based terrain classification
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Surface identification using simple contact dynamics for mobile robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Vibration-based terrain classification for electric powered wheelchairs
Telehealth/AT '08 Proceedings of the IASTED International Conference on Telehealth/Assistive Technologies
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Robotics and Autonomous Systems
Velocity selection for high-speed UGVs in rough unknown terrains using force prediction
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part II
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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Many autonomous ground vehicle (AGV) missions, such as those related to agricultural applications, search and rescue, or reconnaissance and surveillance, require the vehicle to operate in difficult outdoor terrains such as sand, mud, or snow. To ensure the safety and performance of AGVs on these terrains, a terrain-dependent driving and control system can be implemented. A key first step in implementing this system is autonomous terrain classification. It has recently been shown that the magnitude of the spatial frequency response of the terrain is an effective terrain signature. Furthermore, since the spatial frequency response is mapped by an AGV's vibration transfer function to the frequency response of the vibration measurements, the magnitude of the latter frequency responses also serve as a terrain signature. Hence, this paper focuses on terrain classification using vibration measurements. Classification is performed using a probabilistic neural network, which can be implemented online at relatively high computational speeds. The algorithm is applied experimentally to both an ATRV-Jr and an eXperimental Unmanned Vehicle (XUV) at multiple speeds. The experimental results show the efficacy of the proposed approach.