Autonomous Sensor Placement

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In November 2008, Kevin Knuth (President, Autonomous Exploration, Inc.) and Julian Center (CEO, Autonomous Exploration, Inc.) presented a talk titled “Autonomous Sensor Placement” at the 2008 IEEE International Conference on Technologies for Practical Robot Applications in Woburn MA.  In addition to the talk, a demonstration of a robot employing autonomous sensor placement was performed.

An abstract follows below:

Autonomous Sensor Placement

Kevin H. Knuth
Associate Professor of Physics and Informatics, University at Albany
President, Autonomous Exploration, Inc.

Julian Center
CEO, Autonomous Exploration, Inc.

With an increasing reliance on robotic platforms to perform scientific exploration in remote or hostile environments, it is becoming crucial that robotic systems be able to perform autonomous intelligent sensor placement as well as autonomous experimental design.  Such a system requires encoding of scientific knowledge, the ability to make inferences from data, and the ability to identify the most relevant question to ask given both the instrument’s prior knowledge and the issue it is designed to address.  This requires implementation of two computational engines: the inference engine and the inquiry engine. Here we demonstrate our first efforts to develop intelligent instruments that rely on autonomous sensor placement.

Knuth: Developing Robotic Scientists for Space Exploration

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The University at Albany (SUNY) has highlighted Knuth’s research in a recent news piece.

UAlbany Professor Kevin Knuth with a robot built from LEGOs. (Photo Mark Schmidt)

UAlbany Professor Kevin Knuth with a robot built from LEGOs. (Photo Mark Schmidt)

Kevin Knuth has a laboratory in the physics department of the University at Albany that is filled with LEGOs. The bricks are relatively cheap and can be used to rapidly prototype a robot’s body. Knuth’s robots are being programmed to solve such problems as mapping complex terrain.

At UAlbany Day on Saturday, Oct. 25, he will give a demonstration on Robotics and Robotic Exploration in Life Sciences Room 143 at 10:45 a.m.

More here:

http://www.albany.edu/news/update_4522.shtml

Building instructions for the robot shown in the UAlbany article can be found on Brickengineer.com

Visit Robots Everywhere for a general blog on robotics news.

CIDU Presentation at NASA HQ

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Dr. Kevin Knuth was recently invited to give a presentation titled “Intelligent Science Platforms” at the 2008 Conference on Intelligent Data Understanding (CIDU).  The meeting was held from September 9-10, 2008 at NASA Headquarters in Washington DC.  The presentation slides can be downloaded here, or on the CIDU web site.

The abstract follows:

Intelligent Science Platforms

Kevin H. Knuth
Departments of Physics and Informatics, University at Albany, Albany NY 12222
Autonomous Exploration Inc., Albany NY 12208

The exploration of space requires that we continue our dependence on remote scientific platforms. The continued success of the Mars Exploration Rovers highlights the great benefits of navigational autonomy. However, science operations continue to require a team of scientists to select the specific experiments to perform and to precisely guide sensor placement. While this works well on Mars, which has a communication delay ranging from 6.5 to 44 minutes, this model will be strained during future operations on Jupiter’s icy moons, and will most certainly break on future Saturnian missions to Titan and Enceladus. For robotic missions to operate in the outer solar system at a production level comparable to that of the Mars rovers, they will require greater autonomy, not only in mapping and navigation, but also in experimental design and sensor placement.

I will introduce our initial efforts to develop a software-based inquiry engine that relies on a generalized form of information theory called the inquiry calculus. This computational technology enables one to compute the optimal experimental question to ask in a given situation. This technology depends on predicting the probable answers to questions, and selecting the question based on the entropy of the probability distribution of potential answers. I will demonstrate these concepts on a robotic arm that has been programmed to identify and characterize shapes on a playing field using only a simple light sensor.

Presentation at ISBA in Australia

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Dr. Kevin Knuth recently presented his work on Intelligent Autonomous Machines at the 9th Annual International Society for Bayesian Analysis (ISBA 2008) on Hamilton Island, Queensland, Australia, from 21-25 July 2008.  The presentation included a demonstration of our prototype intelligent robotic arm.

The abstract can be downloaded here.

I, Rodney Brooks, Am a Robot

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This month’s issue of IEEE Spectrum Online has an interesting article by Rodney Brooks on the future of artificial intelligence titled “I, Rodney Brooks, Am a Robot“.

Kevin Knuth

Memory Stick Datalogger: Robotics Device of the Day

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Parallax has developed a “Memory Stick Datalogger“.  For $35, this little board takes 5V power and hosts a USB Type A (female) jack on one end and a row of pins providing both SPI and UART interfaces at the other end.  The idea is that you plug in a USB mass storage device, for instance a USB Flash drive, containing a FAT filesystem.  A microcontroller that speaks UART or SPI can then edit files on the USB drive using a DOS-like language.

This lets you write to cheap storage — $8 for a 1GB USB Flash stick these days — and access those same files from Windows or Linux from the filesystem level.  Cheaper devices, for instance Futurlec’s SD/MMC Mini Board ($7), let you read and write Flash memory in SD card form, but they require you to (a) speak the SD card language and (b) implement the FAT filesystem access yourself.  (Note: C libraries are available for both of these purposes.)

Since the USB mass storage language is standardized, you could probably connect a USB hard disk enclosure to the Memory Stick Datalogger to gain access to very large data storage areas.  The product manual is not clear on this point, however, and I haven’t had a chance to test compatibility with hard disks.

Automating the Scientific Method

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In February 2008, Kevin Knuth (President, Autonomous Exploration, Inc.) was invited to present a talk titled “Automating the Scientific Method” at the New York Workshop on Computer, Earth, and Space Sciences (CESS 2009) held at the NASA Goddard Institute for Space Studies in New York City NY.  This talk discussed the computational foundation of automated inference and inquiry.  An abstract follows below:

Automating the Scientific Method

Kevin H. Knuth
Associate Professor of Physics and Informatics, University at Albany
President, Autonomous Exploration, Inc.


In the last decade we have seen computer science, statistics, and the earth,
space, life and social sciences come together with a new synergy based on the
common goal of data analysis. These multi-disciplinary interactions have
become necessary as we pursue both high quality data analysis as well as
analysis of extremely large data sets. However, the ultimate goal is more
fundamental than mere data analysis. We aim to automate the scientific method
itself.

The scientific method relies on the cyclic application of three activities:
hypothesis generation, inquiry (experimental design) and inference
(data analysis). The majority of our efforts at this point have been focused
on the process of automating inference. However, little attention has been
paid to automating the processes of inquiry and hypothesis generation.

The most scientifically-useful approach to data analysis is model-based.
I will briefly review the methodology behind automating model-based inference
with a focus on Bayesian probability theory. I will then introduce a new
related methodology called the inquiry calculus, which enables the automation
of model-based inquiry. Automated hypothesis (model) generation will be left
for another day, as it is the least advanced of the three technologies. I will
demonstrate the application of automated inference and inquiry with a robotic
scientist that performs its own experiments and analyzes its own data.