1998
|
Gutierrez-Osuna, R; Janet, J A; Luo, R C Modeling of ultrasonic range sensors for localization of autonomous mobile robots Journal Article In: Industrial Electronics, IEEE Transactions on, vol. 45, no. 4, pp. 654–662, 1998. @article{gutierrez1998modeling,
title = {Modeling of ultrasonic range sensors for localization of autonomous mobile robots},
author = {R Gutierrez-Osuna and J A Janet and R C Luo},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez1998modeling.pdf},
year = {1998},
date = {1998-01-01},
journal = {Industrial Electronics, IEEE Transactions on},
volume = {45},
number = {4},
pages = {654--662},
publisher = {IEEE},
abstract = {This paper presents a probabilistic model of ultrasonic range sensors using backpropagation neural networks trained on experimental data. The sensor model provides the probability of detecting mapped obstacles in the environment, given their position and orientation relative to the transducer. The detection probability can be used to compute the location of an autonomous vehicle from those obstacles that are more likely to be detected. The neural network model is more accurate than other existing approaches, since it captures the typical multilobal detection pattern of ultrasonic transducers. Since the network size is kept small, implementation of the model on a mobile robot can be efficient for real-time navigation. An example that demonstrates how the credence could be incorporated into the extended Kalman filter (EKF) and the numerical values of the final neural network weights are provided in the appendices.},
keywords = {Robotics},
pubstate = {published},
tppubtype = {article}
}
This paper presents a probabilistic model of ultrasonic range sensors using backpropagation neural networks trained on experimental data. The sensor model provides the probability of detecting mapped obstacles in the environment, given their position and orientation relative to the transducer. The detection probability can be used to compute the location of an autonomous vehicle from those obstacles that are more likely to be detected. The neural network model is more accurate than other existing approaches, since it captures the typical multilobal detection pattern of ultrasonic transducers. Since the network size is kept small, implementation of the model on a mobile robot can be efficient for real-time navigation. An example that demonstrates how the credence could be incorporated into the extended Kalman filter (EKF) and the numerical values of the final neural network weights are provided in the appendices. |
1997
|
Janét, Jason A; Gutierrez-Osuna, R; Chase, Troy A; White, M; Sutton, John C Autonomous mobile robot global self-localization using Kohonen and region-feature neural networks Journal Article In: Journal of Robotic Systems, vol. 14, no. 4, pp. 263–282, 1997. @article{ROBROB4,
title = {Autonomous mobile robot global self-localization using Kohonen and region-feature neural networks},
author = {Jason A Janét and R Gutierrez-Osuna and Troy A Chase and M White and John C Sutton},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/ROBROB4.pdf},
year = {1997},
date = {1997-01-01},
journal = {Journal of Robotic Systems},
volume = {14},
number = {4},
pages = {263--282},
publisher = {Wiley Subscription Services, Inc., A Wiley Company},
abstract = {This article presents and compares two neural network-based approaches to global self-localization (GSL) for autonomous mobile robots using: (1) a Kohonen neural network, and (2) a region-feature neural network (RFNN). Both approaches categorize discrete regions of space (topographical nodes) in a manner similar to optical character recognition (OCR). That is, the mapped sonar data assumes the form of a character unique to that region. Hence, it is believed that an autonomous vehicle can determine which room it is in from sensory data gathered from exploration. With a robust exploration routine, the GSL solution can be time-, translation-, and rotation-invariant. The GSL solution can also become independent of the mobile robot used to collect the sensor data. This suggests that a single robot can transfer its knowledge of various learned regions to other mobile robots. The classification rate of both approaches are comparable and, thus, worthy of presentation. The observed pros and cons of both approaches are also discussed.},
keywords = {Robotics},
pubstate = {published},
tppubtype = {article}
}
This article presents and compares two neural network-based approaches to global self-localization (GSL) for autonomous mobile robots using: (1) a Kohonen neural network, and (2) a region-feature neural network (RFNN). Both approaches categorize discrete regions of space (topographical nodes) in a manner similar to optical character recognition (OCR). That is, the mapped sonar data assumes the form of a character unique to that region. Hence, it is believed that an autonomous vehicle can determine which room it is in from sensory data gathered from exploration. With a robust exploration routine, the GSL solution can be time-, translation-, and rotation-invariant. The GSL solution can also become independent of the mobile robot used to collect the sensor data. This suggests that a single robot can transfer its knowledge of various learned regions to other mobile robots. The classification rate of both approaches are comparable and, thus, worthy of presentation. The observed pros and cons of both approaches are also discussed. |
1996
|
Gutierrez-Osuna, R; Schudel, D S; Janet, J A; Luo, R C Lola, the mobile robot from NC State Conference Proceedings of the thirteenth national conference on Artificial intelligence, 1996. @conference{gutierrez1996lola,
title = {Lola, the mobile robot from NC State},
author = {R Gutierrez-Osuna and D S Schudel and J A Janet and R C Luo},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez1996lola1.pdf},
year = {1996},
date = {1996-01-01},
booktitle = {Proceedings of the thirteenth national conference on Artificial intelligence},
journal = {PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE},
pages = {1354--1354},
keywords = {Robotics},
pubstate = {published},
tppubtype = {conference}
}
|
Gutierrez-Osuna, R; Luo, R C LOLA Probabilistic Navigation for Topological Maps Journal Article In: AI Magazine, vol. 17, no. 1, pp. 55-62, 1996. @article{gutierrez1996lolab,
title = {LOLA Probabilistic Navigation for Topological Maps},
author = {R Gutierrez-Osuna and R C Luo},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/gutierrez1996lola.pdf},
year = {1996},
date = {1996-01-01},
journal = {AI Magazine},
volume = {17},
number = {1},
pages = {55-62},
abstract = {LOLA's entry in the Office Delivery event of the 1995 Robot Competition and Exhibition, held in conjunction with the Fourteenth International Joint Conference on Artificial Intelligence, was the culmination of a three-month design and implementation period for an indoor navigation system for topological maps. This article describes the major components of the robot's navigation architecture. It also summarizes the experiences and lessons learned from the competition.},
keywords = {Robotics},
pubstate = {published},
tppubtype = {article}
}
LOLA's entry in the Office Delivery event of the 1995 Robot Competition and Exhibition, held in conjunction with the Fourteenth International Joint Conference on Artificial Intelligence, was the culmination of a three-month design and implementation period for an indoor navigation system for topological maps. This article describes the major components of the robot's navigation architecture. It also summarizes the experiences and lessons learned from the competition. |
1995
|
Janet, J A; Gutierrez-Osuna, R; Kay, M G; Luo, RC Autonomous mobile robot self-referencing with sensor windows and neural networks Conference Proceedings of the IEEE 21st International Conference on Industrial Electronics, Control and Instrumentation, IEEE 1995. @conference{janet1995autonomous,
title = {Autonomous mobile robot self-referencing with sensor windows and neural networks},
author = {J A Janet and R Gutierrez-Osuna and M G Kay and RC Luo},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/janet1995autonomous.pdf},
year = {1995},
date = {1995-01-01},
booktitle = {Proceedings of the IEEE 21st International Conference on Industrial Electronics, Control and Instrumentation},
pages = {1124--1129},
organization = {IEEE},
abstract = {When navigating an environment a mobile robot can update its position and orientation by searching known landmarks and compare predictions with observations. This paper presents a method of mobile-robot self-referencing where every mapped object (obstacles to the global motion planner) in the environment can be used as potential sources of position and orientation information. This approach employs the efficiency of traversability vectors (t-vectors) for finding in-range geometric beacons and isolating surfaces visible to a sensor. Configuration-space (C-space) buffering (growing polygons to keep motion a safe distance from objects) will reduce the search time for finding in-range geometric beacons. Finally, a small multilayered neural network is used to provide a credence value for each predicted range that can be factored in to a filter or control strategy. This approach can be generalized to any ranging sensor that samples a region (e.g. IR sensors).},
keywords = {Robotics},
pubstate = {published},
tppubtype = {conference}
}
When navigating an environment a mobile robot can update its position and orientation by searching known landmarks and compare predictions with observations. This paper presents a method of mobile-robot self-referencing where every mapped object (obstacles to the global motion planner) in the environment can be used as potential sources of position and orientation information. This approach employs the efficiency of traversability vectors (t-vectors) for finding in-range geometric beacons and isolating surfaces visible to a sensor. Configuration-space (C-space) buffering (growing polygons to keep motion a safe distance from objects) will reduce the search time for finding in-range geometric beacons. Finally, a small multilayered neural network is used to provide a credence value for each predicted range that can be factored in to a filter or control strategy. This approach can be generalized to any ranging sensor that samples a region (e.g. IR sensors). |
Janet, J A; Gutierrez-Osuna, R; Chase, T A; White, M; Luo, R C Global self-localization for autonomous mobile robots using region and feature-based neural networks Conference Proceedings of the IEEE 21st International Conference on Industrial Electronics, Control and Instrumentation, IEEE 1995. @conference{janet1995global,
title = {Global self-localization for autonomous mobile robots using region and feature-based neural networks},
author = {J A Janet and R Gutierrez-Osuna and T A Chase and M White and R C Luo},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/janet1995global.pdf},
year = {1995},
date = {1995-01-01},
booktitle = {Proceedings of the IEEE 21st International Conference on Industrial Electronics, Control and Instrumentation},
pages = {1142--1147},
organization = {IEEE},
abstract = {This paper presents an approach to global self-localization for autonomous mobile robots using a region- and feature-based neural network. This approach categorizes discrete regions of space using mapped sonar data corrupted by noise of varied sources and ranges. The authors' approach is like optical character recognition (OCR) in that the mapped sonar data assumes the form of a character unique to that room. Hence, it is believed that an autonomous vehicle can determine which room it is in from sensory data gathered while exploring that room. With the help of receptive fields, some pre-processing, and a robust exploration routine, the solution becomes time-, translation- and rotation-invariant. The classification rate of this approach is comparable to the Kohonen based approach. Some pros and cons of both approaches are discussed.},
keywords = {Robotics},
pubstate = {published},
tppubtype = {conference}
}
This paper presents an approach to global self-localization for autonomous mobile robots using a region- and feature-based neural network. This approach categorizes discrete regions of space using mapped sonar data corrupted by noise of varied sources and ranges. The authors' approach is like optical character recognition (OCR) in that the mapped sonar data assumes the form of a character unique to that room. Hence, it is believed that an autonomous vehicle can determine which room it is in from sensory data gathered while exploring that room. With the help of receptive fields, some pre-processing, and a robust exploration routine, the solution becomes time-, translation- and rotation-invariant. The classification rate of this approach is comparable to the Kohonen based approach. Some pros and cons of both approaches are discussed. |
Janet, J A; Gutierrez-Osuna, R; Chase, T A; White, M; Luo, R C Global self-localization for autonomous mobile robots using self-organizing Kohonen neural networks Conference Proceedings of the IEEE International Conference on Intelligent Robots and Systems, IEEE 1995. @conference{janet1995globalb,
title = {Global self-localization for autonomous mobile robots using self-organizing Kohonen neural networks},
author = {J A Janet and R Gutierrez-Osuna and T A Chase and M White and R C Luo},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/janet1995globalb.pdf},
year = {1995},
date = {1995-01-01},
booktitle = {Proceedings of the IEEE International Conference on Intelligent Robots and Systems},
pages = {504--509},
organization = {IEEE},
abstract = {An approach to global self-localization for autonomous mobile robots has been developed using self-organizing Kohonen neural networks. This approach categorizes discrete regions of space using mapped sonar data corrupted by noise of varied sources and ranges. Our approach is similar to optical character recognition (OCR) in that the mapped sonar data can, over time, assume the form of a character unique to that room. Hence, it is believed that an autonomous vehicle can be capable of determining which room it is in based on mapped sensory data ascertained by wandering through and exploring that room. With some pre-processing and a robust explore routine, the solution becomes time-, translation- and rotation-invariant.},
keywords = {Robotics},
pubstate = {published},
tppubtype = {conference}
}
An approach to global self-localization for autonomous mobile robots has been developed using self-organizing Kohonen neural networks. This approach categorizes discrete regions of space using mapped sonar data corrupted by noise of varied sources and ranges. Our approach is similar to optical character recognition (OCR) in that the mapped sonar data can, over time, assume the form of a character unique to that room. Hence, it is believed that an autonomous vehicle can be capable of determining which room it is in based on mapped sensory data ascertained by wandering through and exploring that room. With some pre-processing and a robust explore routine, the solution becomes time-, translation- and rotation-invariant. |