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Learning for Autonomous NavigationRobotic ground vehicles for outdoor applications have achieved some remarkable successes, notably in autonomous highway following (Dickmanns, 1987), planetary exploration (1), and off-road navigation on Earth (1). Nevertheless, major challenges remain to enable reliable, high-speed, autonomous navigation in a wide variety of complex, off-road terrain. 3-D perception of terrain geometry with imaging range sensors is the mainstay of off-road driving systems. However, the stopping distance at high speed exceeds the effective lookahead distance of existing range sensors. Prospects for extending the range of 3-D sensors is strongly limited by sensor physics, eye safety of lasers, and related issues. Range sensor limitations also allow vehicles to enter large cul-de-sacs even at low speed, leading to long detours. Moreover, sensing only terrain geometry fails to reveal mechanical properties of terrain that are critical to assessing its traversability, such as potential for slippage, sinkage, and the degree of compliance of potential obstacles. Rovers in the Mars Exploration Rover (MER) mission have got stuck in sand dunes and experienced significant downhill slippage in the vicinity of large rock hazards. Earth-based off-road robots today have very limited ability to discriminate traversable vegetation from non-traversable vegetation or rough ground. It is impossible today to preprogram a system with knowledge of these properties for all types of terrain and weather conditions that might be encountered.
Document ID
20090026380
Acquisition Source
Jet Propulsion Laboratory
Document Type
Preprint (Draft being sent to journal)
External Source(s)
Authors
Angelova, Anelia
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Howard, Andrew
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Matthies, Larry
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Tang, Benyang
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Turmon, Michael
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Mjolsness, Eric
(California Univ. Irvine, CA, United States)
Date Acquired
August 24, 2013
Publication Date
December 9, 2005
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Meeting Information
Meeting: Neural Information Processing Systems (NIPS) Workshop Machine Learning Based Robotics in Unstructured Environments
Location: British Columbia
Country: Canada
Start Date: December 10, 2005
Distribution Limits
Public
Copyright
Other
Keywords
mixture of Gaussians
robotic ground vehicles
autonomous navigation
learning

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