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Title: Design and Sensor-Based COntrol for Hyper-Redundant Mechanisms

Abstract

Toxic materials in DOE sites pose a significant threat to DOE personnel who must inspect these locations. Working in confined spaces further complicates the situation especially when the workers must wear heavy and cumbersome protective suits.

Authors:
Publication Date:
Research Org.:
Carnegie Mellon University
Sponsoring Org.:
USDOE
OSTI Identifier:
882476
Report Number(s):
DOE/ER/63265
TRN: US0702692
DOE Contract Number:
FG07-01ER63265
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; DESIGN; PROTECTIVE CLOTHING; PERSONNEL; TOXIC MATERIALS; INSPECTION; SENSORS; OCCUPATIONAL SAFETY

Citation Formats

Howie Choset. Design and Sensor-Based COntrol for Hyper-Redundant Mechanisms. United States: N. p., 2006. Web. doi:10.2172/882476.
Howie Choset. Design and Sensor-Based COntrol for Hyper-Redundant Mechanisms. United States. doi:10.2172/882476.
Howie Choset. Fri . "Design and Sensor-Based COntrol for Hyper-Redundant Mechanisms". United States. doi:10.2172/882476. https://www.osti.gov/servlets/purl/882476.
@article{osti_882476,
title = {Design and Sensor-Based COntrol for Hyper-Redundant Mechanisms},
author = {Howie Choset},
abstractNote = {Toxic materials in DOE sites pose a significant threat to DOE personnel who must inspect these locations. Working in confined spaces further complicates the situation especially when the workers must wear heavy and cumbersome protective suits.},
doi = {10.2172/882476},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri May 12 00:00:00 EDT 2006},
month = {Fri May 12 00:00:00 EDT 2006}
}

Technical Report:

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  • Toxic materials in DOE sites pose a significant threat to DOE personnel who must inspect these locations. Working in confined spaces further complicates the situation especially when the workers must wear heavy and cumbersome protective suits. A robot or conventional mechanism can clearly bypass the danger and perhaps expedite the characterization process because the person is removed from the site and neither the site nor the person require preparation. However, conventional robots are not suitable for these inspection tasks because they are not flexible enough to pass through and into target DOE inspection sites. This effort is developing an articulatedmore » probe, called a hyper redundant mechanism, which is a snake-like device that can exploit its many internal degrees of freedom to thread through tightly packed volumes transmitting images and data from remote locations inaccessible to conventional robots and people. This effort contains two parts: mechanism development and control of the device.« less
  • Toxic materials in DOE sites pose a significant threat to DOE personnel who must inspect these locations. Working in confined spaces further complicates the situation especially when the workers must wear heavy and cumbersome protective suits. A robot or conventional mechanism can clearly bypass the danger and perhaps expedite the characterization process because the person is removed from the site and neither the site nor the person require preparation. However, conventional robots are not suitable for these inspection tasks because they are not flexible enough to pass through and into target DOE inspection sites. This effort is developing an articulatedmore » probe, called a hyper redundant mechanism, which is a snake-like device that can exploit its many internal degrees of freedom to thread through tightly packed volumes transmitting images and data from remote locations inaccessible to conventional robots and people. This effort contains two parts: mechanism development and control of the device.« less
  • Knowing how many people occupy a building, and where they are located, is a key component of building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, however, current sensor technology and control algorithms limit the effectiveness of both energy management and security systems. This topical report describes results from the first phase of a project to design, implement, validate, and prototype new technologies to monitor occupancy, control indoor environment services, and promote security in buildings. Phase I of the project focused on instrumentation and data collection. In this project phase a newmore » occupancy detection system was developed, commissioned and installed in a sample of private offices and open-plan office workstations. Data acquisition systems were developed and deployed to collect data on space occupancy profiles. Analysis tools based on Bayesian probability theory were applied to the occupancy data generated by the sensor network. The inference of primary importance is a probability distribution over the number of occupants and their locations in a building, given past and present sensor measurements. Inferences were computed for occupancy and its temporal persistence in individual offices as well as the persistence of sensor status. The raw sensor data were also used to calibrate the sensor belief network, including the occupancy transition matrix used in the Markov model, sensor sensitivity, and sensor failure models. This study shows that the belief network framework can be applied to the analysis of data streams from sensor networks, offering significant benefits to building operation compared to current practice.« less
  • Knowing how many people occupy a building, and where they are located, is a key component of building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, however, current sensor technology and control algorithms limit the effectiveness of both energy management and security systems. This topical report describes results from the first phase of a project to design, implement, validate, and prototype new technologies to monitor occupancy, control indoor environment services, and promote security in buildings. Phase I of the project focused on instrumentation and data collection. In this project phase a newmore » occupancy detection system was developed, commissioned and installed in a sample of private offices and open-plan office workstations. Data acquisition systems were developed and deployed to collect data on space occupancy profiles. Analysis tools based on Bayesian probability theory were applied to the occupancy data generated by the sensor network. The inference of primary importance is a probability distribution over the number of occupants and their locations in a building, given past and present sensor measurements. Inferences were computed for occupancy and its temporal persistence in individual offices as well as the persistence of sensor status. The raw sensor data were also used to calibrate the sensor belief network, including the occupancy transition matrix used in the Markov model, sensor sensitivity, and sensor failure models. This study shows that the belief network framework can be applied to the analysis of data streams from sensor networks, offering significant benefits to building operation compared to current practice.« less
  • This project is investigating the development and application of sensor networks to enhance building energy management and security. Commercial, industrial and residential buildings often incorporate systems used to determine occupancy, but current sensor technology and control algorithms limit the effectiveness of these systems. For example, most of these systems rely on single monitoring points to detect occupancy, when more than one monitoring point would improve system performance. Phase I of the project focused on instrumentation and data collection. In Phase I, a new occupancy detection system was developed, commissioned and installed in a sample of private offices and open-plan officemore » workstations. Data acquisition systems were developed and deployed to collect data on space occupancy profiles. In phase II of the project, described in this report, we demonstrate that a network of several sensors provides a more accurate measure of occupancy than is possible using systems based on single monitoring points. We also establish that analysis algorithms can be applied to the sensor network data stream to improve the accuracy of system performance in energy management and security applications, and show that it may be possible to use sensor network pulse rate to distinguish the number of occupants in a space. Finally, in this phase of the project we also developed a prototype web-based display that portrays the current status of each detector in a sensor network monitoring building occupancy. This basic capability will be extended in the future by applying an algorithm-based inference to the sensor network data stream, so that the web page displays the likelihood that each monitored office or area is occupied, as a supplement to the actual status of each sensor.« less