The DARPA LAGR Program: Goals, Challenges, Methodology, and Phase I Results

摘要

The DARPA Learning Applied to Ground Vehicles (LAGR) program is accelerating progress in autonomous, perception-based, off-road navigation in unmanned ground vehicles (UGVs) by incorporating learned behaviors. In addition, the program is using passive optical systems to accomplish long-range scene analysis. By combining long-range perception with learned behavior, LAGR expects to make a qualitative break with the myopic, brittle behavior that characterizes most UGV autonomous navigation in unstructured environments. The very nature of testing navigation in unstructured, off-road environments makes accurate, objective measurement of progress a challenging task. While no absolute measure of performance has been defined by LAGR, the Government Team managing the program has created a relative measure: the Government Team tests navigation software by comparing its effectiveness to that of fixed, but state-of-the-art, navigation software running on a standardized vehicle on a series of varied test courses. Starting in March 2005, eight performers have been submitting navigation code for Government testing on such a standardized Government vehicle. As this text is being written, several teams have already demonstrated leaps in performance. In this paper we report observations on the state of the art in autonomous, off-road UGV navigation, we explain how LAGR intends to change current methods, we discuss the challenges we face in implementing technical aspects of the program, we describe early results, and we suggest where major opportunities for breakthroughs exist as LAGR progresses.