The Demo III UGV program: a testbed for autonomous navigation research

Shoemaker, Chuck M.; Bornstein, J.

doi:10.1109/isic.1998.713784

Cited by 59 publications

(41 citation statements)

References 2 publications

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“…The US Department of Defense through its various agencies has been the major sponsor of research in this field. Notable examples include the DEMO I, II and III projects (Shoemaker and Bornstein, 1998) and the DARPA Grand Challenge (http://www.grandchallenge.org). Indeed, in the 2001 Defense Authorization Act, the U.S. Congress set a goal that by 2015 one third of the operational ground vehicle be unmanned (http://www.gao.gov/new.items/d01311.pdf).…”

Section: Introductionmentioning

confidence: 99%

“…An effective description of the outside world should consist of the combination of geometric and terrain type information, coupled with adaptive control strategies (Bellutta et al, 2000;Hebert et al, 2002). The DEMO III project (Shoemaker and Bornstein, 1998) pioneered this concept, resulting in possibly the most advanced autonomous vehicle for cross-country navigation. We present two approaches to terrain cover perception, one based on stereo and color analysis (from color cameras), and the other based on range data processing (from a ladar).…”

Section: Introductionmentioning

confidence: 99%

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Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation

et al. 2005

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Abstract. Autonomous navigation in cross-country environments presents many new challenges with respect to more traditional, urban environments. The lack of highly structured components in the scene complicates the design of even basic functionalities such as obstacle detection. In addition to the geometric description of the scene, terrain typing is also an important component of the perceptual system. Recognizing the different classes of terrain and obstacles enables the path planner to choose the most efficient route toward the desired goal.This paper presents new sensor processing algorithms that are suitable for cross-country autonomous navigation. We consider two sensor systems that complement each other in an ideal sensor suite: a color stereo camera, and a single axis ladar. We propose an obstacle detection technique, based on stereo range measurements, that does not rely on typical structural assumption on the scene (such as the presence of a visible ground plane); a color-based classification system to label the detected obstacles according to a set of terrain classes; and an algorithm for the analysis of ladar data that allows one to discriminate between grass and obstacles (such as tree trunks or rocks), even when such obstacles are partially hidden in the grass. These algorithms have been developed and implemented by the Jet Propulsion Laboratory (JPL) as part of its involvement in a number of projects sponsored by the US Department of Defense, and have enabled safe autonomous navigation in high-vegetated, off-road terrain.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation

et al. 2005

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“…The Demo III UGB Program [84] developed and demonstrated advances in control of unmanned systems, especially small UGVs under supervised control. That is where the 4D/RCS architecture and its characteristics arose.…”

Section: D/rcs (Real-time Control Systems)mentioning

confidence: 99%

Integration of Service Robots in the Smart Home

Alcalá

Maestre

Pinta

2011

Service Robotics Within the Digital Home

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“…The use of real urban cars in robotics has a major sponsor: the US Department of Defence through its various agencies. Notable examples include the DEMO I, II and III projects [6] and the DARPA Grand Challenge [7]. Two of the most important researchers in this area are Sebastian Thrun and M. Montemerlo, who won the DARPA Grand Challenge in 2005 [8], and developed another model to win the second place at the 2008 DARPA Urban-Challenge [9].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Guided Car Using a Fuzzy Controller

Olivares-Méndez

Campoy

Mellado-Bataller

et al. 2013

Recent Advances in Robotics and Automation

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The goal of the work described in this paper is to develop a visual line guided system for being used on-board an Autonomous Guided Vehicle (AGV) commercial car, controlling the steering and using just the visual information of a line painted below the car. In order to implement the control of the vehicle, a Fuzzy Logic controller has been implemented, that has to be robust against curvature changes and velocity changes. The only input information for the controller is the visual distance from the image center captured by a camera pointing downwards to the guiding line on the road, at a commercial frequency of 30Hz. The good performance of the controller has successfully been demonstrated in a real environment at urban velocities. The presented results demonstrate the capability of the Fuzzy controller to follow a circuit in urban environments without previous information about the path or any other information from additional sensors.

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The Demo III UGV program: a testbed for autonomous navigation research

Cited by 59 publications

References 2 publications

Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation

Obstacle Detection and Terrain Classification for Autonomous Off-Road Navigation

Integration of Service Robots in the Smart Home

Autonomous Guided Car Using a Fuzzy Controller

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