robot scientists

Robot scientists to explore treacherous ice environments

Alex Soojung-Kim Pang — Wed, 28 May 2008 05:33:49 -0700

Description

EurekAlert reports:

Scientists are diligently working to understand how and why the world’s ice shelves are melting. While most of the data they need (temperatures, wind speed, humidity, radiation) can be obtained by satellite, it isn’t as accurate as good old-fashioned, on-site measurement and static ground-based weather stations don’t allow scientists to collect info from as many locations as they’d like.

And unfortunately, the locations in question are volatile ice sheets, possibly cracking, shifting and filling with water — not exactly a safe environment for scientists.

To help scientists collect the more detailed data they need without risking scientists’ safety, researchers at the Georgia Institute of Technology, working with Pennsylvania State University, have created specially designed robots called SnoMotes to traverse these potentially dangerous ice environments. The SnoMotes work as a team, autonomously collaborating among themselves to cover all the necessary ground to gather assigned scientific measurements. Data gathered by the Snomotes could give scientists a better understanding of the important dynamics that influence the stability of ice sheets.

:In order to say with certainty how climate change affects the world’s ice, scientists need accurate data points to validate their climate models," said Ayanna Howard, lead on the project and an associate professor in the School of Electrical and Computer Engineering at Georgia Tech. "Our goal was to create rovers that could gather more accurate data to help scientists create better climate models. It’s definitely science-driven robotics."

Howard was previously a scientist at NASA, and so you can argue that there's a family resemblance-- and a design one-- between planetary explorers and SnoMotes.

The SnoMote represents two key innovations in rovers: a new method of location and work allocation communication between robots and maneuvering in ice conditions.

Once placed on site, the robots place themselves at strategic locations to make sure all the assigned ground is covered. Howard and her team are testing two different methods that allow the robots to decide amongst themselves which positions they will take to get all the necessary measurements.

The first is an “auction” system that lets the robots “bid” on a desired location, based on their proximity to the location (as they move) and how well their instruments are working or whether they have the necessary instrument (one may have a damaged wind sensor or another may have low battery power).

The second method is more mathematical, fixing the robots to certain positions in a net of sorts that is then stretched to fit the targeted location. Magnus Egerstedt is working with Howard on this work allocation method.

In addition to location assignments, another key innovation of the SnoMote is its ability to find its way in snow conditions. While most rovers can use rocks or other landmarks to guide their movement, snow conditions present an added challenge by restricting topography and color (everything is white) from its guidance systems.

For snow conditions, one of Howard's students discovered that the lines formed by snow banks could serve as markers to help the

SnoMote track distance traveled, speed and direction. The SnoMote could also navigate via GPS if snow bank visuals aren’t available.

Computer & Information Science

Source

http://www.eurekalert.org/pub_releases/2008-05/giot-rgw052708.php

Robot Scientist Creates and Evaluates Microbiology Hypotheses

Jean-Claude Bradley — Sun, 04 May 2008 12:09:59 -0700

Description

From Ross King's Robot Scientist web site (1):

The Robot Scientist is perhaps the first physical implementation of the task of Scientific Discovery in a microbiology laboratory. It represents the merging of increasingly automated and remotely controllable laboratory equipment and knowledge discovery techniques from Artificial Intelligence.

Automation of laboratory equipment (the "Robot" of Robot Scientist) has revolutionised laboratory practice by removing the "drudgery" of constructing many wet lab experiments by hand, allowing an increase in both the scope and scale of potential experiments. Most lab robots only require a simple description of the various chemical/ biological entities to be used in the experiments, along with their required volumes and where these entities are stored. Automation has also given rise to significantly increased productivity and a concomitant increase in the production of results and data requiring interpretation, giving rise to an "interpretation bottleneck" where the process of understanding the results is lagging behind the production of results.

The research fields of Computational Scientific Discovery and Bioinformatics have emerged in part as a response to this bottleneck. Both disciplines use computational approaches from Statistics and Machine Learning to provide an "automated understanding" of the experimental results.

This is a strong signal foreshadowing the near automation of the entire scientific process. This robot is able to function within the framework of molecular biology. However, each field has its own set of opportunities and challenges. The difficulty in extending this concept to other fields, such as organic (2) or inorganic chemistry will depend upon the conceptual models used. Providing the system with fewer human-based rules about how chemistry works would make it more difficult but ultimately could be more interesting.

Coupled with the practice of Open Data (3) and Crowdsourcing (4), a new form of distributed scientific intelligence could emerge that would "understand" reality in the sense that it is predictive and able to control phenomena but not in a way that is necessarily intuitive to humans.

This may be a pathway to the technological singularity.(5)

Future of chemistry

Source

1) http://www.aber.ac.uk/compsci/Research/bio/robotsci/intro/
2) http://sciencex2.org/en/node/16263
3) http://sciencex2.org/en/node/15726
4) http://precedings.nature.com/documents/1505/version/1
5) http://en.wikipedia.org/wiki/Technological_singularity

Robotic laboratories are becoming commonplace, so computational data analysis has to as well

Andrew Walkingshaw — Fri, 02 May 2008 12:29:03 -0700

Description

It's not news that robots are better at routine experiments than postdocs are: Wikipedia points out, on the page on high-throughput screening, that

Automation is an important element in HTS's usefulness. Typically, an integrated robot system consisting of from one or more robots transports assay microplates from station to station for sample and reagent addition, mixing, incubation, and finally readout or detection. An HTS system can usually prepare, incubate, and analyze many plates simultaneously, further speeding the data-collection process. HTS robots currently exist which can test up to 100,000 compounds per day (Hann 2004). The term uHTS or ultra high throughput screening refers (circa 2008) to screening in excess of 100,000 compounds per day.

No scientist can review this many results by hand, but these methods are medically and technologically very important:

High-throughput screening methods have become essential for sifting through large chemical libraries in search of drug candidates, and several sensitive and reliable analytical techniques have been specifically adapted to high-throughput measurements of biocatalytic activity. High-throughput biocatalytic assay platforms thus enable rapid screening.

So to take advantage of the advances in experimental technique, we need advances in informatics to let us deal with this torrent of data.

Future of chemistry

Source

http://nihroadmap.nih.gov/
http://en.wikipedia.org/wiki/High-throughput_screening
http://dx.doi.org/10.1016/j.cbpa.2006.02.033