Name
Abstract | ||
|---|---|---|
This paper considers prediction of slip from a distance for wheeled ground robots using visual information as input. Large amounts of slippage which can occur on certain surfaces, such as sandy slopes, will negatively affect rover mobil- ity. Therefore, obtaining information about slip before entering a particular terrain can be very useful for better planning and avoiding terrains with large slip. The proposed method is based on learning from experience and consists of terrain type recognition and nonlinear regression modeling. After learning, slip prediction is done remotely using only the visual information as input. The method has been implemented and tested offline on several off-road terrains including: soil, sand, gravel, and woodchips. The slip prediction error is about 20 of the step size. |
Year | Venue | Keywords |
|---|---|---|
2006 | Robotics: Science and Systems | negative affect,nonlinear regression,prediction error |
Field | DocType | Citations |
Computer vision,Mean squared prediction error,Simulation,Computer science,Terrain,Nonlinear regression,Slip (materials science),Slippage,Artificial intelligence,Robot,Machine learning | Conference | 20 |
PageRank | References | Authors |
1.34 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anelia Angelova | 1 | 410 | 30.70 |
| Larry Matthies | 2 | 111 | 7.19 |
| Daniel M. Helmick | 3 | 208 | 15.78 |
| pietro perona | 4 | 16433 | 1969.06 |