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ABSTRACT: Abstract?The Defense Applied Research Projects Agency (DARPA) Learning Applied to Ground Vehicles (LAGR) program aims to develop algorithms for autonomous vehicle navigation that learn how to operate in complex terrain. For the LAGR program, The National Institute of Standards and Technology (NIST) has embedded learning into a control system architecture called 4D/RCS to enable the small robot used in the program to learn to navigate through a range of terrain types. This paper describes performance evaluation experiments on one of the algorithms developed under the program to learn terrain traversability. The algorithm uses color and texture to build models describing regions of terrain seen by the vehicle?s stereo cameras. Range measurements from stereo are used to assign traversability measures to the regions. The assumption is made that regions that look alike have similar traversability. Thus, regions that match one of the models inherit the traversability stored in the model. This allows all areas of images seen by the vehicle to be classified, and enables a path planner to determine a traversable path to the goal. The algorithm is evaluated by comparison with ground truth generated by a human observer. A graphical user interface (GUI) was developed that displays an image and randomly generates a point to be classified. The human assigns a traversability label to the point, and the learning algorithm associates its own label with the point. When a large number of such points have been labeled across a sequence of images, the performance of the learning algorithm is determined in terms of error rates. The learning algorithm is outlined in the paper, and results of performance evaluation are described.
11/2012;
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ABSTRACT: Industrial assembly of manufactured products is often performed by first bringing parts together in a kit and then moving the kit to the assembly area where the parts are used to assemble products. Kitting, the process of building kits, has not yet been automated in many industries where automation may be feasible. Consequently, the cost of building kits is higher than it could be. We are addressing this problem by building models of the knowledge that will be required to operate an automated kitting workstation. A first pass has been made at modeling non-executable information about a kitting workstation that will be needed, such as information about a robot, parts, kit designs, grippers, etc. A model (or models) of executable plans for building kits is also needed. The plans will be used by execution systems that control robots and other mechanical devices to build kits. The first steps in building a kitting plan model are to determine what the functional requirements are and what model constructs are needed to enable meeting those requirements. This paper discusses those issues.
Proceedings of the Performance Metrics for Intelligent Systems (PerMIS’12) Workshop, College Park, MD; 01/2012
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James Albus,
Roger Bostelman,
Raj Madhavan,
Harry Scott,
Tony Barbera,
Sandor Szabo,
Tsai Hong,
Tommy Chang,
Will Shackleford, Michael Shneier,
Stephen Balakirsky,
Craig Schlenoff,
Hui-Min Huang,
Fred Proctor
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ABSTRACT: The National Institute of Standards and Technology (NIST) Intelligent Control of Mobility Systems (ICMS) Program provides
architectures and interface standards, performance test methods and data, and infrastructure technology needed by the U.S.
manufacturing industry and government agencies in developing and applying intelligent control technology to mobility systems
to reduce cost, improve safety, and save lives. The ICMS Program is made up of several areas including: defense, transportation,
and industry projects, among others. Each of these projects provides unique capabilities that foster technology transfer
across mobility projects and to outside government, industry and academia for use on a variety of applications. A common theme
among these projects is autonomy and the Four Dimensional (3D+time)/Real-time Control System (4D/RCS) standard control architecture
for intelligent systems that has been applied to these projects.
05/2008: pages 315-362;
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Auton. Robots. 01/2008; 24:69-86.
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ABSTRACT: The Defense Applied Research Projects Agency (DARPA) Learning Applied to Ground Vehicles (LAGR) program aims to develop algorithms for autonomous vehicle navigation that learn how to operate in complex terrain. Over many years, the National Institute of Standards and Technology (NIST) has developed a reference model control system architecture called 4D/RCS that has been applied to many kinds of robot control, including autonomous vehicle control. For the LAGR program, NIST has embedded learning into a 4D/RCS controller to enable the small robot used in the program to learn to navigate through a range of terrain types. The vehicle learns in several ways. These include learning by example, learning by experience, and learning how to optimize traversal. Learning takes place in the sensory processing, world modeling, and behavior generation parts of the control system. The 4D/RCS architecture is explained in the paper, its application to LAGR is described, and the learning algorithms are discussed. Results are shown of the performance of the NIST control system on independently-conducted tests. Further work on the system and its learning capabilities is discussed. © 2007 Wiley Periodicals, Inc.
Journal of Field Robotics 10/2006; 23(11‐12):975 - 1003. · 2.24 Impact Factor
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ABSTRACT: The National Institute of Standards and Technology's (NIST) Intelligent Systems Division (ISD) is working with the material handling industry, specifically on automated guided vehicles, to develop next generation vehicles. ISD is also a participant in the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) Project embedding learning algorithms into the modules that make up the Four Dimensional/Real- Time Control System (4D/RCS). 4D/RCS is the standard reference model architecture which ISD has applied to control many intelligent systems. Technology from LAGR is being transferred to the material handling industry through the NIST Industrial Autonomous Vehicles Project. This paper describes the 4D/RCS structure and control applied to LAGR and the transfer of this technology through a demonstration to the automated guided vehicles industry.
06/2006;
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J. Field Robotics. 01/2006; 23:975-1003.
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Ninth International Conference on Control, Automation, Robotics and Vision, ICARCV 2006, Singapore, 5-8 December 2006, Proceedings; 01/2006
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ICINCO 2006, Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics, Robotics and Automation, Setúbal, Portugal, August 1-5, 2006; 01/2006
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ABSTRACT: We describe a project to collect and disseminate sensor data for autonomous mobility research. Our goals are to provide data of known accuracy and precision to researchers and developers to enable algorithms to be developed using realistically difficult sensory data. This enables quantitative comparisons of algorithms by running them on the same data, allows groups that lack equipment to participate in mobility research, and speeds technology transfer by providing industry with metrics for comparing algorithm performance. Data are collected using the NIST High Mobility Multi-purpose Wheeled Vehicle (HMMWV), an instrumented vehicle that can be driven manually or autonomously both on roads and off. The vehicle can mount multiple sensors and provides highly accurate position and orientation information as data are collected. The sensors on the HMMWV include an imaging ladar, a color camera, color stereo, and inertial navigation (INS) and Global Positioning System (GPS). Also available are a highresolution scanning ladar, a line-scan ladar, and a multicamera panoramic sensor. The sensors are characterized by collecting data from calibrated courses containing known objects. For some of the data, ground truth will be collected from site surveys. Access to the data is through a web-based query interface. Additional information stored with the sensor data includes navigation and timing data, sensor to vehicle coordinate transformations for each sensor, and sensor calibration information. Several sets of data have already been collected and the web query interface has been developed. Data collection is an ongoing process, and where appropriate, NIST will work with other groups to collect data for specific applications using third-party sensors.
06/2003;
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ABSTRACT: A robotic vehicle needs to understand the terrain and features around it if it is to be able to navigate complex environments such as road systems. By taking advantage of the fact that such vehicles also need accurate knowledge of their own location and orientation, we have developed a sensing and object recognition system based on information about the area where the vehicle is expected to operate. The information is collected through aerial surveys, from maps, and by previous traverses of the terrain by the vehicle. It takes the form of terrain elevation information, feature information (roads, road signs, trees, ponds, fences, etc.) and constraint information (e.g., one-way streets). We have implemented such an a priori database using One Semi-Automated Forces (OneSAF), a military simulation environment. Using the Inertial Navigation System and Global Positioning System (GPS) on the NIST High Mobility Multipurpose Wheeled Vehicle (HMMWV) to provide indexing into the database, we extract all the elevation and feature information for a region surrounding the vehicle as it moves about the NIST campus. This information has also been mapped into the sensor coordinate systems. For example, processing the information from an imaging Laser Detection And Ranging (LADAR) that scans a region in front of the vehicle has been greatly simplified by generating a prediction image by scanning the corresponding region in the a priori model. This allows the system to focus the search for a particular feature in a small region around where the a priori information predicts it will appear. It also permits immediate identification of features that match the expectations. Results indicate that this processing can be performed in real time.
04/2003;
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ABSTRACT: This paper describes a world model that combines a variety of sensed inputs and a priori information and is used to generate on-road and off-road autonomous driving behaviors. The system is designed in accordance with the principles of the 4D/RCS architecture. The world model is hierarchical, with the resolution and scope at each level designed to minimize computational resource requirements and to support planning functions for that level of the control hierarchy. The sensory processing system that populates the world model fuses inputs from multiple sensors and extracts feature information, such as terrain elevation, cover, road edges, and obstacles. Feature information from digital maps, such as road networks, elevation, and hydrology, is also incorporated into this rich world model. The various features are maintained in different layers that are registered together to provide maximum flexibility in generation of vehicle plans depending on mission requirements. The paper includes discussion of how the maps are built and how the objects and features of the world are represented. Functions for maintaining the world model are discussed. The world model described herein is being developed for the Army Research Laboratory's Demo III Autonomous Scout Vehicle experiment.
01/2003;
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Elena Messina,
Karl Murphy,
Alberto Lacaze, Michael Shneier,
Harry Scott,
Proctor William Shackleford,
John Michaloski,
Albert Wavering,
Thomas Kramer Nicholas,
Steven Legowik,
Roger Bostelman,
Adam Jacoff,
Joseph Falco,
James Gilsinn,
C Anthony Barbera,
M. L. Fitzgerald,
D Mark Del Giorno
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ABSTRACT: The 4D/RCS architecture provides a reference model for military unmanned vehicles on how their software components should be identified and organized. It defines ways of interacting to ensure that missions, especially those involving unknown or hostile environments, can be analyzed, decomposed, distributed, planned, and executed intelligently, effectively, efficiently and in coordination. To achieve this, the 4D/RCS reference model provides well defined and highly coordinated sensory processing, world modeling, knowledge management, cost/benefit analysis, behavior generation, and messaging functions, as well as the associated interfaces. The 4D/RCS architecture is based on scientific principles and is consistent with military hierarchical command doctrine.
12/2002;
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ABSTRACT: As part of the Army's Demo III project, a sensor-based system has been developed to identify roads and to enable a mobile robot to drive along them. A ladar sensor, which produces range images, and a color camera are used in conjunction to locate the road surface and its boundaries. Sensing is used to constantly update an internal world model of the road surface. The world model is used to predict the future position of the road and to focus the attention of the sensors on the relevant regions in their respective images. The world model also determines the most suitable algorithm for locating and tracking road features in the images based on the current task and sensing information. The planner uses information from the world model to determine the best path for the vehicle along the road. Several different algorithms have been developed and tested on a diverse set of road sequences. The road types include some paved roads with lanes, but most of the sequences are of unpaved roads, including dirt and gravel roads. The algorithms compute various features of the road images including smoothness in the world model map and in the range domain, and color features and texture in the color domain. Performance in road detection and tracking are described and examples are shown of the system in action.
05/2002;
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ABSTRACT: In an outdoor, o#-road mobile robotics environment, it is important to identify objects that can a#ect the vehicle's ability to traverse its planned path, and to determine their three-dimensional characteristics. In this paper, a combination of three elements is used to accomplish this task. An imaging ladar collects range images of the scene. A color camera, whose position relative to the ladar is known, is used to gather color images. Information extracted from these sensors is used to build a world model, a representation of the current state of the world. The world model is used actively in the sensing to predict what should be visible in each of the sensors during the next imaging cycle. The paper explains how the combined use of these three types of information leads to a robust understanding of the local environment surrounding the robotic vehicle for two important tasks: puddle/pond avoidance and road sign detection. Applications of this approach to road detection are also discussed.
01/2002;
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ABSTRACT: This paper describes a world model designed to act as a bridge between multiple sensory inputs and a behavior generation (path planning) subsystem for off-road autonomous driving. It describes how the world model map is built and how the objects and features of the world are represented. The functions used to maintain the model are explained and the sensors and sensory processing used to provide data for this application are discussed. The paper includes examples of integrating and fusing sensory data from multiple sources into the world model map. The representation is being developed for the Army's Demo III autonomous driving experiment, which is an on-going research project. The paper concludes with a discussion of future research directions. 1
03/2001;
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ABSTRACT: In this paper, we apply two fundamental approaches to-ward evaluating a static, vision based, six-degree-of-freedom (6DoF) pose determination system that measures the po-sition and orientation of a part. The first approach uses groundtruth carefully obtained from a laser tracker and the second approach doesn't use any external groundtruth. The evaluation procedure focuses on characterizing both the sys-tem's accuracy and precision as well as the effect of object viewpoints. For the groundtruth method, we first use a laser tracker for system calibration and then compare the calibrated out-put with the surveyed pose. In the method without external groundtruth, we evaluate the effect of viewpoint factors on the system's performance.
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ABSTRACT: In this paper we demonstrate the use of a dynamic, six-degree-of-freedom (6DOF) laser tracker to empirically evaluate the performance of a real-time visual servoing implementation, with the objective of establishing a general method for evaluating real-time 6DOF dimensional measurements. The laser tracker provides highly accurate ground truth reference measurements of position and orientation of an object under motion, and can be used as an objective standard for calibration and evaluation of visual servoing and robot control algorithms. The real-time visual servoing implementation used in this study was developed at the Purdue Robot Vision Lab with a subsumptive, hierarchical, and distributed vision-based architecture. Data were taken simultaneously from the laser tracker and visual servoing implementation, enabling comparison of the data streams.
