Autonomous Exploration Inc. Awarded a 2009 NASA SBIR

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Lunar Electric Rover

Autonomous Exploration Inc. has been awarded a 2009 NASA Phase I SBIR grant
for the proposal titled Advanced Bayesian Methods for Lunar Surface Navigation.  We will be working toward developing navigation systems that target the new Lunar Electric Rover and the Mark III space suits.

The key innovation of this project will be the application of advanced Bayesian methods to integrate real-time dense stereo vision and high-speed optical flow with an Inertial Measurement Unit (IMU) to produce a highly accurate planetary rover navigation system. The software developed in this project will leverage current computing technology to implement advanced Visual Odometry (VO) methods that will accurately track much faster rover movements. Our fully Bayesian approach to VO will utilize more information from the images than previous methods are capable of using. Our Bayesian VO does not explicitly select features to track. Instead it implicitly determines what can be learned from each image pixel and weights the information accordingly. This means that our approach can work with images that have no distinct corners, which can be a significant advantage with low contrast images from permanently shadowed areas. We expect that the error characteristics of the visual processing with be complementary to the error characteristics of a low-cost IMU. Therefore, the combination of the two should provide highly accurate navigation.

Visual Odometry (VO) has played a key role in Mars exploration with the Spirit and Opportunity Mars Exploration Rovers (MERs). However, limitations in onboard computing power severely limit the speed of movement that can be tracked by MERS VO, requiring an order of magnitude reduction in forward progress in area where VO was required.
The software developed in this project will leverage current computing technology to implement advanced VO methods that will accurately track much faster rover movements. This will greatly increase exploration productivity. This improvement will become even more significant when exploring the more distant planetary bodies.

This project will also investigate whether combining vision with a low-cost, lightweight, low-power Micro-ElectroMechanical System (MEMS) Inertial Measurement Unit (IMU) can produce acceptable accuracy for lunar and planetary exploration. If so, this will facilitate the design of lower-cost, light-weight rovers, which will make it feasible to launch a team of rovers for wide area exploration.

There will be many potential terrestrial applications for a Bayesian VO system. Although GPS-IMU systems can work well in open outdoor settings, GPS is degraded or unavailable in indoor settings or in outdoor areas with significant tree cover. A navigation system combining a GPS and an IMU with Bayesian VO could provide continuous operation in all environments. The success of this project should lay the groundwork for low-cost, low-power, light-weight integrated navigation systems for robots and autonomous vehicles operating in a wide range of environments.
One potential market for this technology is the Department of Defense (DoD). Congress has given DoD a mandate that by 2020 30% of ground vehicles should be robotic. An accurate, low-cost VO system should allow many of these vehicles to be semi-autonomous, enabling only supervisory control for many missions.

NASA’s technology taxonomy has been developed by the SBIR-STTR program to disseminate awareness of proposed and awarded R/R&D in the agency. It is a listing of over 100 technologies, sorted into broad categories, of interest to NASA.

Guidance, Navigation, and Control

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.

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

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.