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
Citations
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Journal ArticleDOI
Planning to see: A hierarchical approach to planning visual actions on a robot using POMDPs
TL;DR: This work defines a novel hierarchical POMDP-based approach for visual processing management and shows empirically that HiPPo and CP outperform the naive application of all visual operators on all ROIs and produces more robust plans than CP or the naive visual processing.
Journal ArticleDOI
Towards Autonomous Planetary Exploration
Martin J. Schuster,Sebastian G. Brunner,Kristin Bussmann,Stefan Büttner,Andreas Dömel,Matthias Hellerer,Hannah Lehner,Peter Lehner,Oliver Porges,Josef Reill,Sebastian Riedel,Mallikarjuna Vayugundla,Bernhard Vodermayer,Tim Bodenmüller,Christoph Brand,Werner Friedl,Iris Grixa,Heiko Hirschmüller,Michael Kaßecker,Zoltan-Csaba Marton,Christian Nissler,Felix Ruess,Michael Suppa,Armin Wedler +23 more
TL;DR: The Lightweight Rover Unit (LRU) as discussed by the authors is a small and agile rover prototype designed for the challenges of planetary exploration, which has a locomotion system with individually steered wheels for high maneuverability in rough terrain and stereo cameras as its main sensors.
Proceedings ArticleDOI
The Autonomous City Explorer (ACE) project — mobile robot navigation in highly populated urban environments
TL;DR: The algorithms used for Simultaneous Localization and Mapping (SLAM), path planning in dynamic environments and behavior selection are presented, as well as the system architecture that integrates them to a complete working system.
Proceedings ArticleDOI
Needle steering system using duty-cycled rotation for percutaneous kidney access
TL;DR: The authors present ongoing work on the use of a variable curvature flexible needle steering system to gain percutaneous access to the kidney for medical interventions using a nonlinear control law which drives the needle to track a predetermined planar path using a steering approach based on duty-cycled rotation during insertion.
Journal ArticleDOI
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System
TL;DR: In this paper, the authors use reinforcement learning to train an end-to-end learning-based mobile robot navigation system that can be trained with autonomously-labeled off-policy data gathered in real-world environments, without any simulation or human supervision.
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