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Open AccessJournal ArticleDOI

Stanley: The Robot that Won the DARPA Grand Challenge

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.

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Posted Content

MROS: Runtime Adaptation For Robot Control Architectures.

TL;DR: A structured model-based framework for the adaptation of robot control architectures at run-time to satisfy set quality requirements is presented, using a formal meta-model to represent the configuration space of control architectures and the corresponding mission requirements.
Proceedings ArticleDOI

Visual Navigation Using a Webcam Based on Semantic Segmentation for Indoor Robots

TL;DR: This paper proposes a vision-based navigation scheme that enables autonomous movement in indoor scenes; only a webcam is used as an external sensor and demonstrates that a robot can move around on a floor.
Proceedings ArticleDOI

An adaptive detection approach for autonomous forest path following using stereo vision

TL;DR: An image-based segmentation method to improve autonomous robot navigation in the forest is presented and it is summarized how the detection results are transformed to the 3D space, using a plane which is extracted from the stereo data, to be stored and maintained in a probabilistic grid map.
Journal ArticleDOI

Self-supervised learning as an enabling technology for future space exploration robots: ISS experiments on monocular distance learning

TL;DR: This article investigates a learning mechanism, Self-Supervised Learning (SSL), which is very reliable and hence an important candidate for real-world deployment even on safety-critical systems such as space robots, and introduces a novel SSL setup that allows a stereo vision equipped robot to cope with the failure of one of its cameras.
Journal ArticleDOI

Control System Design of an Automated Bus in Revenue Service

TL;DR: Three key elements in the controller design that address the safety and performance challenges are described, i.e., high precision, fault tolerance, and control transitions, which help advance the field of automated vehicle.
References
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Book

Pattern classification and scene analysis

TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Proceedings ArticleDOI

New extension of the Kalman filter to nonlinear systems

TL;DR: It is argued that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.
Book

Fundamentals of Vehicle Dynamics

TL;DR: In this article, the authors attempt to find a middle ground by balancing engineering principles and equations of use to every automotive engineer with practical explanations of the mechanics involved, so that those without a formal engineering degree can still comprehend and use most of the principles discussed.
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