The Development of a Full-Field Image Ranger System for Mobile Robotic Platforms

2011 ◽  
Author(s):  
Johnny McClymont ◽  
Dale A. Carnegie ◽  
Adrian Jongenelen ◽  
Benjamin Drayton
Keyword(s):  
Full Field ◽  
Robotic Platforms

scholarly journals Development of Extrospective  Systems for Mobile Robotic  Vehicles.

2021 ◽  
Author(s):  
◽  
Johnny Robert Keogh McClymont
Keyword(s):  
Sensor Network ◽  
Network Architecture ◽  
Power Efficiency ◽  
Field Of View ◽  
Prototype System ◽  
Full Field ◽  
Intelligent Sensor ◽  
Hardware Configuration ◽  
Robotic Platforms ◽  
Position Sensitive Detectors

<p>Extrospection is the process of receiving knowledge of the outside world through the senses. On robotic platforms this is primarily focussed on determining distances to objects of interest and is achieved through the use of ranging sensors. Any hardware implemented on mobile robotic platforms, including sensors, must ideally be small in size and weight, have good power efficiency, be self-contained and interface easily with the existing platform hardware. The development of stable, expandable and interchangeable mobile robot based sensing systems is crucial to the establishment of platforms on which complex robotic research can be conducted and evaluated in real world situations. This thesis details the design and development of two extrospective systems for incorporation in the Victoria University of Wellington's fleet of mobile robotic platforms. The first system is a generic intelligent sensor network. Fundamental to this system has been the development of network architecture and protocols that provide a stable scheme for connecting a large number of sensors to a mobile robotic platform with little or no dependence on the existing hardware configuration of the platform. A prototype sensor network comprising fourteen infrared position sensitive detectors providing a short to medium distance ranging system (0.2 - 3 m) with a 360' field of view has been successfully developed and tested. The second system is a redesign of an existing prototype full-field image ranger system. The redesign has yielded a smaller, mobile version of the prototype system capable of ranging medium to long distances (0 - 15 m) with a 22.2' - 16.5' field-of-view. This ranger system can now be incorporated onto mobile robotic platforms for further research into the capabilities of full-field image ranging as a form of extrospection on a mobile platform.</p>

scholarly journals Development of Extrospective  Systems for Mobile Robotic  Vehicles.

2021 ◽  
Author(s):  
◽  
Johnny Robert Keogh McClymont
Keyword(s):  
Sensor Network ◽  
Network Architecture ◽  
Power Efficiency ◽  
Field Of View ◽  
Prototype System ◽  
Full Field ◽  
Intelligent Sensor ◽  
Hardware Configuration ◽  
Robotic Platforms ◽  
Position Sensitive Detectors

<p>Extrospection is the process of receiving knowledge of the outside world through the senses. On robotic platforms this is primarily focussed on determining distances to objects of interest and is achieved through the use of ranging sensors. Any hardware implemented on mobile robotic platforms, including sensors, must ideally be small in size and weight, have good power efficiency, be self-contained and interface easily with the existing platform hardware. The development of stable, expandable and interchangeable mobile robot based sensing systems is crucial to the establishment of platforms on which complex robotic research can be conducted and evaluated in real world situations. This thesis details the design and development of two extrospective systems for incorporation in the Victoria University of Wellington's fleet of mobile robotic platforms. The first system is a generic intelligent sensor network. Fundamental to this system has been the development of network architecture and protocols that provide a stable scheme for connecting a large number of sensors to a mobile robotic platform with little or no dependence on the existing hardware configuration of the platform. A prototype sensor network comprising fourteen infrared position sensitive detectors providing a short to medium distance ranging system (0.2 - 3 m) with a 360' field of view has been successfully developed and tested. The second system is a redesign of an existing prototype full-field image ranger system. The redesign has yielded a smaller, mobile version of the prototype system capable of ranging medium to long distances (0 - 15 m) with a 22.2' - 16.5' field-of-view. This ranger system can now be incorporated onto mobile robotic platforms for further research into the capabilities of full-field image ranging as a form of extrospection on a mobile platform.</p>

Full-Field Approach for Modeling of Microstructural Evolutions During Forming Processes

2019 ◽  
Author(s):  
S. Andrietti ◽  
M. Bernacki ◽  
N. Bozzolo ◽  
L. Maire ◽  
P. De Micheli ◽  
...  
Keyword(s):  
Full Field ◽  
Field Approach

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

2020 ◽  
Author(s):  
A. Syahputra
Keyword(s):  
Neural Network ◽  
Water Saturation ◽  
Prediction Method ◽  
Neural Model ◽  
Training Model ◽  
Acquisition Time ◽  
Oil Saturation ◽  
Full Field ◽  
Remaining Oil ◽  
Observation Wells

Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.

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