Publication | Closed Access
Online Terrain Parameter Estimation for Wheeled Mobile Robots With Application to Planetary Rovers
308
Citations
20
References
2004
Year
EngineeringField RoboticsIntelligent RoboticsPlanetary RoversAutonomous SystemsGeotechnical EngineeringTrajectory PlanningOnline Estimation MethodMobile RoboticsKinematicsRobot LearningPath PlanningMechatronicsGeographyVehicle LocalizationTerrain ParametersAutonomous NavigationOdometryCivil EngineeringDifferential Wheeled RobotRoboticsWheeled Mobile RobotsKey Terrain Parameters
Planetary exploration will require rovers to traverse rough terrain with limited supervision, and wheel‑terrain interaction and terrain parameters are key for mobility and soil composition assessment. The paper presents an online method that estimates key terrain parameters from on‑board sensors for use in traversability prediction, traction control, and autonomous planning. The algorithm uses simplified terramechanics equations and a linear‑least‑squares approach to compute terrain parameters in real time. Simulations and experiments demonstrate that the algorithm accurately and efficiently estimates terrain parameters across various soil types.
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.
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