We are developing a complete, self-contained autonomous navigation system for mobile robots that learns quickly, uses commodity components, and has the added benefit of emitting no radiation signature. It builds on the
autonomous navigation technology developed by Net-Scale and New York University during the Defense Advanced Research Projects Agency (DARPA) Learning Applied to Ground Robots (LAGR) program and takes advantage of recent scientific advancements achieved during the DARPA Deep Learning program. In this paper we will present our approach and algorithms, show results from our vision system, discuss lessons learned from the past, and present our plans for further advancing vehicle autonomy.
A long-held dream for robotics researchers is the creation of vehicles that can move to a goal without human supervision, adapting as required to changing circumstances. While today’s ground robots are still far from achieving such complete autonomy, substantial progress has been attained. In this paper we describe the state-of-the-art in autonomous ground vehicle navigation as observed in the recently completed DARPA PerceptOR program, and we suggest new research directions where we see opportunities for leaps in performance.
Two of our analog neural net chips have been integrated into board systems and are being used now in a variety of image recognition applications. One of the two circuits, the NET32K chip, has connections with a low resolution of between one and four bits. With this chip one can scan up to 32 kernels of a size of 16 X 16 pixels over an image. It is used mainly for extracting geometrical features from images, for such applications as image segmentation. The second of the chips, named ANNA, operates with a higher resolution of 6 bits in the weights and 3 bits in the states. It has been designed for implementing nets to recognize characters. The computation rates obtained with these circuits are 10 to 100 times faster than those of standard processors. With the NET32K chip we achieve between two and ten billion connections per second. With the ANNA chip we read over 150 characters per second, a tenfold increase compared with a digital signal processor.
Conference Committee Involvement (5)
Unmanned Systems Technology X
17 March 2008 | Orlando, Florida, United States
Unmanned Systems Technology IX
9 April 2007 | Orlando, Florida, United States
Unmanned Systems Technology VIII
17 April 2006 | Orlando (Kissimmee), Florida, United States
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