The vehicles would also learn from experience.

A program initiated by the Defense Advanced Research Projects Agency (DARPA) and now also pursued by other agencies called “Learning Applied to Ground Robots” (LAGR), is developing control algorithms that would enable a robotic land vehicle, robotic underwater crawler, or other similar autonomous mobile robot to traverse terrain safely. Among the algorithms needed are navigation algorithms for finding and then following a safe path across terrain from a starting or current position to a destination.

In one approach to solving the navigation problem, the navigation algorithms form a hierarchical fuzzy-logic system that includes (1) modules that perform a variety of low- and intermediate- level sensor-data-processing functions involved in planning a path, and (2) a supervisory module that uses information from the aforementioned modules. Another notable aspect of this approach is that the algorithms provide for improving performance by learning from path-planning choices made by a human driver and remembering data from previous traversals.

The hierarchical fuzzy-logic system is intended to mimic path-planning decisions that would be made by a human in the face of the currently available sensory data and of a traversability map based on a terrain-height-and-obstacle map that also incorporates what is known about the terrain from previously acquired sensor data. It is assumed that a robot utilizing these algorithms would be equipped with sensory systems that would include at least a stereoscopic machine vision system, a Global Positioning System receiver, and an inertial navigation system.