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This article describes the robot Stanley, which won the 2005 DARPA Grand Challenge. Stanley was developed for high-speed desert driving without manual intervention. The robot's software system relied predominately on state-of-the-art artificial intelligence technologies, such as machine learning and probabilistic reasoning. This article describes the major(More)
As an alternative to cumbersome aerial vehicles with considerable maintenance requirements and flight envelope restrictions, the X4 flyer is chosen as the basis for the Stanford testbed of autonomous rotorcraft for multi-agent control (STARMAC). This paper outlines the design and development of a miniature autonomous waypoint tracker flight control system,(More)
The mobile robotics community has traditionally addressed motion planning and navigation in terms of steering decisions. However, selecting the best speed is also important – beyond its relationship to stopping distance and lateral maneuverability. Consider a high-speed (35 mph) autonomous vehicle driving off-road through challenging desert terrain. The(More)
—Mapping and localization for indoor robotic navigation is a well-studied field. However, existing work largely relies on long range perceptive sensors in addition to the robot's odometry, and little has been done with short range sensors. In this paper, we propose a method for real-time indoor mapping using only short range sensors such as bumpers and/or(More)
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