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Publications

Search Publications by William P. Shackleford

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Displaying 1 - 25 of 48

The Canonical Robot Command Language (CRCL)

August 1, 2016
Author(s)
Frederick M. Proctor, Stephen B. Balakirsky, Zeid Kootbally, Thomas R. Kramer, Craig I. Schlenoff, William P. Shackleford
Industrial robots can perform motion with sub-millimeter repeatability when programmed using the teach-and-playback method. While effective, this method requires significant up-front time, tying up the robot and a person during the teaching phase. Off-line

Performance Evaluation of Human Detection Systems for Robot Safety

January 22, 2016
Author(s)
William P. Shackleford, Geraldine S. Cheok, Tsai H. Hong, Kamel S. Saidi, Michael O. Shneier
Detecting and tracking people is becoming more important in robotic applications because of the increasing demand for collaborative work in which people interact closely with and in the same workspace as robots. New safety standards allow people to work

Obstacle Detection and Avoidance from an Automated Guided Vehicle

September 18, 2014
Author(s)
Roger V. Bostelman, William P. Shackleford, Geraldine S. Cheok
Current automated guided vehicle (AGV) technology typically provides material handling flow along single or dual opposing-flow lanes in manufacturing and distribution facilities. An AGV stops for most any obstacle that may be in its path which then stops

New AGV Capabilities are Safety Driven

September 3, 2014
Author(s)
Roger V. Bostelman, William P. Shackleford
Current automated guided vehicle (AGV) technology typically provides material handling flow along single or dual opposing-flow lanes in manufacturing and distribution facilities. An AGV stops for most any obstacle that may be in its path which then stops

Performance Metrics for Evaluating Object and Human Detection and Tracking Systems

July 31, 2014
Author(s)
Afzal A. Godil, Roger V. Bostelman, William P. Shackleford, Tsai H. Hong, Michael O. Shneier
In this report, we provide an overview of various performance evaluation metrics for object detection and tracking for robot safety applications in smart manufacturing. We present four different types of performance evaluation metrics based on detection

3D Ground-Truth Systems for Object/Human Recognition and Tracking

June 28, 2013
Author(s)
Afzal A. Godil, Roger V. Bostelman, Kamel S. Saidi, William P. Shackleford, Geraldine S. Cheok, Michael O. Shneier, Tsai Hong Hong
We have been researching 3D ground-truth systems for performance evaluation of vision and perception systems in the fields of smart manufacturing and robotics safety. In this paper we first present an overview of different systems that have been used to

Safe Control of Manufacturing Vehicles Research Towards Standard Test Methods

June 28, 2012
Author(s)
Roger V. Bostelman, William P. Shackleford, Geraldine S. Cheok, Kamel S. Saidi
The National Institute of Standards and Technology's Intelligent Systems Division has been researching several areas leading to safe control of manufacturing vehicles to improve AGV safety standards. The research areas include: - Automated guided vehicle

Advanced Sensing Towards Improved Forklift Safety

February 4, 2011
Author(s)
Roger V. Bostelman, William P. Shackleford
The National Institute of Standards and Technology's Intelligent Systems Division has been researching advanced three-dimensional (3D) imaging sensors and their use in manufacturing towards improving forklift safety. Experiments are presented in this paper

Performance Measurements Towards Improved Manufacturing Vehicle Safety

November 23, 2009
Author(s)
Roger V. Bostelman, William P. Shackleford
In this paper, we describe the current 2D (dimensional) sensor and ideal sensor configurations if 3D imagers are mounted on manufacturing vehicles in an attempt to make them more safe. Towards the ideal sensor configuration, three experiments were

Integrating Learning Into a Hierarchical Vehicle Control System

December 31, 2007
Author(s)
James S. Albus, Roger V. Bostelman, Tsai Hong Hong, Tommy Chang, William P. Shackleford, Michael O. Shneier
The National Institute of Standards and Technology s (NIST) Intelligent Systems Division (ISD) is a participant in the Defense Advanced Research Projects Agency (DARPA) LAGR (Learning Applied to Ground Robots) Program. The NIST team s objective for the

A Common Operator Control Unit Color Scheme for Mobile Robots

December 28, 2007
Author(s)
Michael O. Shneier, Roger V. Bostelman, James S. Albus, William P. Shackleford, Tommy Chang, Tsai Hong Hong
The Intelligent Systems Division at the National Institute of Standards and Technology (NIST) has partici-pated in the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) project for the past 2 ? years. In Phase 2 of

Learning Traversability Models for Autonomous Mobile Vehicles

November 21, 2007
Author(s)
Michael O. Shneier, Tommy Chang, Tsai Hong Hong, William P. Shackleford
Autonomous mobile robots need to adapt their behavior to the terrain over which they drive, and to predict the traversability of the terrain so that they can effectively plan their paths. Such robots usually make use of a set of sensors to investigate the

Learning in a Hierarchical Control System: 4D/RCS in the DARPA LAGR Program

December 29, 2006
Author(s)
James S. Albus, Roger V. Bostelman, Tommy Chang, Tsai Hong Hong, William P. Shackleford, Michael O. Shneier
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 com-plex terrain. Over many years, the National Institute of

Unstructured Facility Navigation by Applying the NIST 4D/RCS Architecture

September 5, 2006
Author(s)
Roger V. Bostelman, Tsai Hong Hong, Tommy Chang, William P. Shackleford, Michael O. Shneier
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

THE LAGR PROJECT - Integrating Learning Into the 4D/RCS Control Hierarchy

August 31, 2006
Author(s)
James S. Albus, Roger V. Bostelman, Tsai Hong Hong, William P. Shackleford, Michael O. Shneier
The National Institute of Standards and Technology s (NIST) Intelligent Systems Division (ISD) has been a part of the Defense Advanced Research Project Agency (DARPA) LAGR (Learning Applied to Ground Robots) Project. The NIST team s objective for the LAGR

The LAGR Project. Integrating learning into the 4D/RCS Control Hierarchy

August 1, 2006
Author(s)
James S. Albus, Roger V. Bostelman, Tsai Hong Hong, Tommy Chang, William P. Shackleford, Michael O. Shneier
The National Institute of Standards and Technology?s (NIST) Intelligent Systems Division (ISD) is a par-ticipant in the Defense Advanced Research Project Agency (DARPA) LAGR (Learning Applied to Ground Robots) Project. The NIST team?s objective for the