The Intelligent Ground Vehicle Competition (IGVC) is one of four, unmanned systems, student competitions that were
founded by the Association for Unmanned Vehicle Systems International (AUVSI). The IGVC is a multidisciplinary
exercise in product realization that challenges college engineering student teams to integrate advanced control theory,
machine vision, vehicular electronics and mobile platform fundamentals to design and build an unmanned system.
Teams from around the world focus on developing a suite of dual-use technologies to equip ground vehicles of the future
with intelligent driving capabilities. Over the past 18 years, the competition has challenged undergraduate, graduate and
Ph.D. students with real world applications in intelligent transportation systems, the military and manufacturing
automation. To date, teams from over 75 universities and colleges have participated. This paper describes some of the
applications of the technologies required by this competition and discusses the educational benefits. The primary goal of
the IGVC is to advance engineering education in intelligent vehicles and related technologies. The employment and
professional networking opportunities created for students and industrial sponsors through a series of technical events
over the four-day competition are highlighted. Finally, an assessment of the competition based on participation is
presented.
Over the last two decades, research in Unmanned Vehicles (UV) has rapidly progressed and become more influenced by
the field of biological sciences. Researchers have been investigating mechanical aspects of varying species to improve
UV air and ground intrinsic mobility, they have been exploring the computational aspects of the brain for the
development of pattern recognition and decision algorithms and they have been exploring perception capabilities of
numerous animals and insects. This paper describes a 3 month exploratory applied research effort performed at the US
ARMY Research, Development and Engineering Command's (RDECOM) Tank Automotive Research, Development
and Engineering Center (TARDEC) in the area of biologically inspired spectrally augmented feature selection for robotic
visual odometry. The motivation for this applied research was to develop a feasibility analysis on multi-spectrally
queued feature selection, with improved temporal stability, for the purposes of visual odometry. The intended
application is future semi-autonomous Unmanned Ground Vehicle (UGV) control as the richness of data sets required to
enable human like behavior in these systems has yet to be defined.
Remote Imagery for Unmanned Ground Vehicles (RIUGV) uses a combination of high-resolution multi-spectral satellite imagery and advanced commercial off-the-self (COTS) object-oriented image processing software to provide automated terrain feature extraction and classification. This information, along with elevation data, infrared imagery, a vehicle mobility model and various meta-data (local weather reports, Zobler Soil map, etc...), is fed into automated path planning software to provide a stand-alone ability to generate rapidly updateable dynamic mobility maps for Manned or Unmanned Ground Vehicles (MGVs or UGVs). These polygon based mobility maps can reside on an individual platform or a tactical network. When new information is available, change files are generated and ingested into existing mobility maps based on user selected criteria. Bandwidth concerns are mitigated by the use of shape files for the representation of the data (e.g. each object in the scene is represented by a shape file and thus can be transmitted individually). User input (desired level of stealth, required time of arrival, etc...) determines the priority in which objects are tagged for updates. This paper will also discuss the planned July 2006 field experiment.
KEYWORDS: LIDAR, Sensors, Roads, Robotics, Global Positioning System, Control systems, Cameras, Safety, Unmanned ground vehicles, Commercial off the shelf technology
The 2005 DARPA Grand Challenge (DCG) was a 'Huge Leap Forward for Robotics R&D' according to the DARPA
Grand Challenge tracking website. Similar to the transatlantic flight competition that spurred commercial flights all
over the world, the Grand Challenge was a step forward in the area of navigation for unmanned ground vehicles.
However, questions like 'What are the important technologies brought forth by the Grand Challenge?' and 'How can
these technologies assist our soldiers in the field?' need to be addressed. This paper will look at the 2005 DARPA
Grand Challenge from the perspective of individuals involved in some of the Army's unmanned ground vehicle
programs. Information will be presented contrasting this year's competition to the one held in 2004. Details of the
enabling technologies from many of the competitors will be discussed along with problems they encountered at the
National Qualification Event (NQE) and on Race Day. Finally, thoughts will be presented on how these technologies
may be harvested in commercial and DOD research and development for current and future systems.
The combination of high-resolution multi-spectral satellite imagery and advanced COTS object-oriented image processing software provides for an automated terrain feature extraction and classification capability. This information, along with elevation data, infrared imagery, a vehicle mobility model and various meta-data (local weather reports, Zobler Soil map, etc...), is fed into automated path planning software to provide a stand-alone ability to generate rapidly updateable dynamic mobility maps for Manned or Unmanned Ground Vehicles (MGVs or UGVs). These polygon based mobility maps can reside on an individual platform or a tactical network. When new information is available, change files are generated and ingested into existing mobility maps based on user selected criteria. Bandwidth concerns are mitigated by the use of shape files for the representation of the data (e.g. each object in the scene is represented by a shape file and thus can be transmitted individually). User input (desired level of stealth, required time of arrival, etc...) determines the priority in which objects are tagged for updates. This technology was tested at Fort Knox, Kentucky October 11th-15th 2004. Satellite imagery was acquired in a near-real-time fashion for the selected test site. Portions of the resulting geo-rectified image were compared with surveyed range locations to assess the accuracy of the approach. The derived UGV Path Plans were ingested into a Stryker UGV and the routes were autonomously traversed. This paper will detail the feasibility of this approach based of the results of this testing.
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