Terrain Sensing Project Page
For mobile robots in rough terrain, the ability to safely traverse terrain is highly dependent
on mechanical properties of that terrain. For example, a robot may be able to climb up a rocky
slope with ease, but slide down a sandy slope the same grade. With mobile robots being employed
for planetary exploration and UGVs being developed for missions on Earth, the ability to predict
these mechanical terrain properties from a distance is becoming increasingly important.
This project focuses on:
The goal of this work is to be able to set a robot down in a previously unexplored environment, and
after driving around for a short period of time have it be able to look out in the distance and
predict mechanical properties of the terrain it sees.
- Classifying natural terrain based on visual features, such as color, visual texture, and range data,
- Learning visual classification on-line, so that a robot can improve its terrain recognition based on its experiences,
- Autonomously identifying mechanically-distinct terrain classes to eliminate the need for human supervision in establishing the list of terrain classes, and
- Estimating the mechanical terrain properties associated with each of the terrain classes.
Experiments for this project have been performed using a four-wheeled mobile robot in natural
outdoor terrain. The robot appeared briefly in a segment of NOVA scienceNOW.
This work has been funded by NASA/JPL through the Mars Technology Program.