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DARPA LAGR Program : ウィキペディア英語版 | DARPA LAGR Program The Learning Applied to Ground Vehicles (LAGR) program, which ran from 2004 until 2008, had the goal of accelerating progress in autonomous, perception-based, off-road navigation in robotic unmanned ground vehicles (UGVs). LAGR was funded by DARPA, a research agency of the United States Department of Defense. ==History and Background==
While mobile robots had been in existence since the 1960s, (e.g.Shakey), progress in creating robots that could navigate on their own, outdoors, off road, on irregular, obstacle-rich terrain had been slow. In fact no clear metrics were in place to measure progress.〔See especially appendix C, National Research Council of the National Academies, “Technology Development for Army Unmanned Ground Vehicles,” National Academies Press, Washington DC , 2002.〕 A baseline understanding of off-road capabilities began to emerge with the DARPA PerceptOR program 〔E. Krotkov, S. Fish, L. Jackel, M. Perschbacher, and J. Pippine, “The DARPA PerceptOR evaluation experiments." Autonomous Robots, 22(1):pages 19-35, 2007.〕 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. These tests exposed the extreme challenges of off-road navigation. 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. Furthermore their reliance on pre-scripted behaviors did not allow them to adapt to unexpected circumstances. 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. The LAGR program was designed to build on the methodology started in PerceptOR while seeking to overcome the technical challenges exposed by the PerceptOR tests.
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