History and background
WhileLAGR goals
The principal goal of LAGR was to accelerate progress in off navigation of UGVs. 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.Structure and rationale of the LAGR program
The LAGR program was designed 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. The teams were each given two robots of the standard design. They developed newThe LAGR teams
Eight teams were selected as performers in Phase I, the first 18 months, of LAGR. The teams were from Applied Perception (Principal Investigator IMark Ollis),The LAGR vehicle
Scientific results
A cornerstone of the program was incorporation of learned behaviors in the robots. In addition, the program used passive optical systems to accomplish long-range scene analysis. The difficulty of testing UGV navigation in unstructured, off-road environments made accurate, objective measurement of progress a challenging task. While no absolute measure of performance had been defined in LAGR, the relative comparison of a team's code to that of the Baseline code on a given course demonstrated whether progress was being made in that environment. By the conclusion of the program, testing showed that many of the performers had attained leaps in performance. In particular, average autonomous speeds where increased by factor of 3 and useful visual perception was extended to ranges as far as 100 meters.For detailed discussion of LAGR results see the Special Issues of Journal of Field Robotics, Vol 23 issues 11/12 2006 and Vol 26 issue 1/2 2009. 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. Object recognition was not directly addressed. Even though the LAGR vehicle had a WAAS GPS, its position was never determined down to the width of the vehicle, so it was hard for the systems to re-use obstacle maps of areas the robots had previously traversed since the GPS continually drifted. The drift was especially severe if there was a forest canopy. A few teams developedProgram management
LAGR was administered under the DARPA Information Processing Technology Office. Larry Jackel conceived the program and was the program manager from 2004 to 2007. Eric Krotkov, Michael Perschbacher, and James Pippine contributed to LAGR conception and management. Charles Sullivan played a major role in LAGR testing. Tom Wagner was the program manager from mid-2007 to the program conclusion in early 2008.References