Perception for learned trafficability models

摘要

Unmanned ground vehicles (UGV), traversing open terrain, require the capability of identifying non-geometric barriers or impediments to navigation, such as soft soil, fine sand, mud, snow, compliant vegetation, washboard, and ruts. Given the ever changing nature of these terrain characteristics, for an UVG to be able to consistently navigate such barriers, it must have the ability to learn from and to adapt to changes in these environmental conditions. As part of ongoing research co-operation with the Defense Research Establishment Suffield (DRES), Scientific Instrumentation Ltd. (SIL) has developed a Terrain Simulator that allows for the investigation of terrain perception and of learning techniques.