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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GeRoNa: Generic Robot Navigation
TL;DR: This paper presents the whole framework, detail every controller and provide an extensive experimental evaluation of the most important components of the GeRoNa (Generic Robot Navigation), a modular navigation framework for wheeled mobile robots.
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Characterizing a Heterogeneous System for Person Detection in Video Using Histograms of Oriented Gradients: Power Versus Speed Versus Accuracy
C. Blair,Neil Robertson,D. Hume +2 more
TL;DR: This paper presents a new implementation of the processing operations required in a widely-used pedestrian detection algorithm (the histogram of oriented gradients) when run in various configurations on a heterogeneous platform suitable for use as an embedded system and demonstrates that prioritization of each of these factors can be made by selecting a specific configuration.
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Method and apparatus for routing ocean going vessels to avoid treacherous environments
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Learning to assess terrain from human demonstration using an introspective Gaussian-process classifier
TL;DR: This paper presents an approach to learning robot terrain assessment from human demonstration that improves on current methods and uses a Gaussian-process classifier for terrain assessment due to its superior introspective abilities when compared to other classifier methods in the literature.
Proceedings ArticleDOI
Combining local trajectory planning and tracking control for autonomous ground vehicles navigating along a reference path
TL;DR: An integrated local trajectory planning and control scheme for the navigation of autonomous ground vehicles (AGVs) along a reference path with avoidance of static obstacles using an internal model controller to track the desired trajectory, while rejecting the negative effects resulting from model uncertainties and external disturbances.
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