Dr. Eric B. Martinson Profile

Dr. Eric B. Martinson

Senior Scientist at Soar Technology Inc

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 12 April 2021 Presentation + Paper

Improving success and efficiency of underwater autonomous tasks through dynamic re-planning and episodic reasoning

Eric Martinson, Lilia Moshkina, Nicholas Paul

Proceedings Volume 11758, 117580N (2021) https://doi.org/10.1117/12.2585182

KEYWORDS: Space robots, Object recognition, Computer architecture

Read Abstract +

In this paper, we present a novel underwater autonomy architecture that combines reasoning about prior history with dynamic selection of the best set of action options for the current environment. Prior history is recorded as a set of indexed episodes, including features best describing the environment each episode occurred in, the set of actions and their parameters, and the system’s performance during the episode. Based on previous history most related to current experience, the robot dynamically selects actions and/or parameters most likely to succeed in the immediate environment; the action space is represented as a dynamic Hierarchical Task Network (HTN). We have implemented and tested the architecture in UWSim, on a simulated Blue ROV-2 with a 4 DOF manipulator in a 3D motion planning domain, where the task goal is to touch a designated underwater object with the arm’s end effector. We have shown that after just 20 episodes of learning, the robot converges on a stable global policy that maximizes success rates of object touch task. The architecture is designed to be relatively domain-independent, and is applicable to a variety of underwater tasks, such as survey/search, manipulation, active perception, etc. We are currently extending our implementation to a survey domain.

Proceedings Article | 29 December 2004 Paper

Noise maps for acoustically sensitive navigation

Eric Martinson, Ronald Arkin

Proceedings Volume 5609, (2004) https://doi.org/10.1117/12.581461

KEYWORDS: Robots, Scanning probe lithography, Robotics, Sensors, Acoustics, Electronic filtering, Mobile robots, Signal processing, Error analysis, Statistical modeling

Read Abstract +

More and more robotic applications are equipping robots with microphones to improve the sensory information available to them. However, in most applications the auditory task is very low-level, only processing data and providing auditory event information to higher-level navigation routines. If the robot, and therefore the microphone, ends up in a bad acoustic location, then the results from that sensor will remain noisy and potentially useless for accomplishing the required task. To solve this problem, there are at least two possible solutions. The first is to provide bigger and more complex filters, which is the traditional signal processing approach. An alternative solution is to move the robot in concert with providing better audition. In this work, the second approach is followed by introducing noise maps as a tool for acoustically sensitive navigation. A noise map is a guide to noise in the environment, pinpointing locations which would most likely interfere with auditory sensing. A traditional noise map, in an acoustic sense, is a graphical display of the average sound pressure level at any given location. An area with high sound pressure level corresponds to high ambient noise that could interfere with an auditory application. Such maps can be either created by hand, or by allowing the robot to first explore the environment. Converted into a potential field, a noise map then becomes a useful tool for reducing the interference from ambient noise. Preliminary results with a real robot on the creation and use of noise maps are presented.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years