Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments
International Journal of Robotics Research
International Journal of Computer Vision
Online speed adaptation using supervised learning for high-speed, off-road autonomous driving
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Estimation of terrain forces and parameters for rigid-wheeled vehicles
IEEE Transactions on Robotics - Special issue on rehabilitation robotics
Sliding mode avoidance in passively articulated vehicles
International Journal of Intelligent Systems Technologies and Applications
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Non-contact terrain classification for autonomous mobile robot
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain
International Journal of Robotics Research
Intelligent Service Robotics
Autonomous navigation based on the velocity space method in dynamic environments
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Self-supervised terrain classification for planetary surface exploration rovers
Journal of Field Robotics
How the Location of the Range Sensor Affects EKF-based Localization
Journal of Intelligent and Robotic Systems
Dynamic Wheel-Soil Model for Lightweight Mobile Robots with Smooth Wheels
Journal of Intelligent and Robotic Systems
Comparison of different approaches to visual terrain classification for outdoor mobile robots
Pattern Recognition Letters
Hi-index | 0.00 |
Future planetary exploration missions will require wheeled mobile robots ("rovers") to traverse very rough terrain with limited human supervision. Wheel-terrain interaction plays a critical role in rough-terrain mobility. In this paper, an online estimation method that identifies key terrain parameters using on-board robot sensors is presented. These parameters can be used for traversability prediction or in a traction control algorithm to improve robot mobility and to plan safe action plans for autonomous systems. Terrain parameters are also valuable indicators of planetary surface soil composition. The algorithm relies on a simplified form of classical terramechanics equations and uses a linear-least squares method to compute terrain parameters in real time. Simulation and experimental results show that the terrain estimation algorithm can accurately and efficiently identify key terrain parameters for various soil types.