Stanley: The Robot that Won the DARPA Grand Challenge
Sebastian Thrun,Michael Montemerlo,Hendrik Dahlkamp,David Stavens,Andrei Aron,James Diebel,Philip Fong,John Gale,Morgan Halpenny,Gabriel M. Hoffmann,Kenny Lau,Celia M. Oakley,Mark Palatucci,Vaughan R. Pratt,Pascal Stang,Sven Strohband,Cedric Dupont,Lars-Erik Jendrossek,Christian Koelen,Charles Markey,Carlo Rummel,Joe van Niekerk,Eric Jensen,Philippe Alessandrini,Gary Bradski,Bob Davies,Scott M. Ettinger,Adrian Kaehler,Ara V. Nefian,Pamela Mahoney +29 more
TLDR
The robot Stanley, which won the 2005 DARPA Grand Challenge, was developed for high‐speed desert driving without manual intervention and relied predominately on state‐of‐the‐art artificial intelligence technologies, such as machine learning and probabilistic reasoning.Abstract:
This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without human intervention. The robot’s software system relied predominately on state-of-the-art AI technologies, such as machine learning and probabilistic reasoning. This article describes the major components of this architecture, and discusses the results of the Grand Challenge race.read more
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Space-indexed dynamic programming: learning to follow trajectories
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Structure-based color learning on a mobile robot under changing illumination
Mohan Sridharan,Peter Stone +1 more
TL;DR: A self-contained vision system that works on-board a vision-based autonomous robot under varying illumination conditions, and introduces algorithms for autonomous planned color learning and illumination change detection and adaptation.
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Autonomous vehicle for surveillance missions in off-road environment
Jose E. Naranjo,Miguel Clavijo,Felipe Jiménez,Omar G. Alvarado Gomez,Jose L. Tapia Rivera,Manuel Anguita +5 more
TL;DR: This paper presents the application of the civil autonomous vehicle technology to develop a demonstrator of UGV for the Spanish Army, including the background, architecture and first field tests.
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Speed Daemon: Experience-Based Mobile Robot Speed Scheduling
TL;DR: This is the first speed scheduler to incorporate experience from previous path traversals in order to address system constraints and the approach to speed scheduling is shown to generate fast speed schedules while remaining within the limits of the robot's capability.
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