DARPA LAGR Program
[1] A baseline understanding of off-road capabilities began to emerge with the DARPA PerceptOR program [2] in which independent research teams fielded robotic vehicles in unrehearsed Government tests that measured average speed and number of required operator interventions over a fixed course over widely spaced waypoints.
While the PerceptOR vehicles were equipped with sensors and algorithms that were state-of-the-art for the beginning of the 21st century, the limited range of their perception technology caused them to become trapped in natural cul-de-sacs.
The overall result was that except for essentially open terrain with minimal obstacles, or along dirt roads, the PerceptOR vehicles were unable navigate without numerous, repeated operator intervention.
Additional, synergistic goals included (1) establishing benchmarking methodology for measuring progress for autonomous robots operating in unstructured environments, (2) advancing machine vision and thus enabling long-range perception, and (3) increasing the number of institutions and individuals who were able to contribute to forefront UGV research.
The LAGR program was designed [3] to focus on developing new science for robot perception and control rather than on new hardware.
Thus, it was decided to create a fleet of identical, relatively simple robots that would be supplied to the LAGR researchers, who were members of competitive teams, freeing them to concentrate on algorithm development.
This rather modest “Go/ No Go” metric was chosen to allow teams to choose risky, but promising approaches that might not be fully developed in the first 18 months of the program.
All 8 teams achieved this metric, with some scoring more twice the speed of the Baseline on the later tests which was the objective for Phase II.
It was battery powered and had two independently driven wheelchair motors in the front, and two caster wheels in the rear.
The ~ $30,000 cost of the LAGR vehicle meant that a fleet could be built and distributed to a number of teams expanding on the field of researchers who had traditionally participated in DARPA robotics programs.
The difficulty of testing UGV navigation in unstructured, off-road environments made accurate, objective measurement of progress a challenging task.
[5] While LAGR did succeed in extending the useful range of visual perception, this was primarily done by either pixel or patch-based color or texture analysis.
LAGR also had the goal of expanding the number of performers and removing the need for large system integration so that valuable technology nuggets created by small teams could be recognized and then adopted by the larger community.
Eric Krotkov, Michael Perschbacher, and James Pippine contributed to LAGR conception and management.
