Ms. Danielle A. Dumond Profile

Danielle A. Dumond

at Aptima Inc

SPIE Involvement:

Author

Publications (4)

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 | 17 May 2013 Paper

Multimodal interaction for human-robot teams

Dustin Burke, Nathan Schurr, Jeanine Ayers, Jeff Rousseau, John Fertitta, Alan Carlin, Danielle Dumond

Proceedings Volume 8741, 87410E (2013) https://doi.org/10.1117/12.2016334

KEYWORDS: Robots, Data modeling, Buildings, Tablets, Gesture recognition, Interfaces, Sensors, Robotics, Surveillance, Visualization

Read Abstract +

Unmanned ground vehicles have the potential for supporting small dismounted teams in mapping facilities, maintaining security in cleared buildings, and extending the team’s reconnaissance and persistent surveillance capability. In order for such autonomous systems to integrate with the team, we must move beyond current interaction methods using heads-down teleoperation which require intensive human attention and affect the human operator’s ability to maintain local situational awareness and ensure their own safety. This paper focuses on the design, development and demonstration of a multimodal interaction system that incorporates naturalistic human gestures, voice commands, and a tablet interface. By providing multiple, partially redundant interaction modes, our system degrades gracefully in complex environments and enables the human operator to robustly select the most suitable interaction method given the situational demands. For instance, the human can silently use arm and hand gestures for commanding a team of robots when it is important to maintain stealth. The tablet interface provides an overhead situational map allowing waypoint-based navigation for multiple ground robots in beyond-line-of-sight conditions. Using lightweight, wearable motion sensing hardware either worn comfortably beneath the operator’s clothing or integrated within their uniform, our non-vision-based approach enables an accurate, continuous gesture recognition capability without line-of-sight constraints. To reduce the training necessary to operate the system, we designed the interactions around familiar arm and hand gestures.

Proceedings Article | 17 May 2013 Paper

Cognitive patterns: giving autonomy some context

Danielle Dumond, Webb Stacy, Alexandra Geyer, Jeffrey Rousseau, Mike Therrien

Proceedings Volume 8741, 874109 (2013) https://doi.org/10.1117/12.2016383

KEYWORDS: Robots, Sensors, Telecommunications, Interfaces, Video, Navigation systems, Visualization, Cognitive modeling, Prototyping, Multilayers

Read Abstract +

Today’s robots require a great deal of control and supervision, and are unable to intelligently respond to unanticipated and novel situations. Interactions between an operator and even a single robot take place exclusively at a very low, detailed level, in part because no contextual information about a situation is conveyed or utilized to make the interaction more effective and less time consuming. Moreover, the robot control and sensing systems do not learn from experience and, therefore, do not become better with time or apply previous knowledge to new situations. With multi-robot teams, human operators, in addition to managing the low-level details of navigation and sensor management while operating single robots, are also required to manage inter-robot interactions. To make the most use of robots in combat environments, it will be necessary to have the capability to assign them new missions (including providing them context information), and to have them report information about the environment they encounter as they proceed with their mission. The Cognitive Patterns Knowledge Generation system (CPKG) has the ability to connect to various knowledge-based models, multiple sensors, and to a human operator. The CPKG system comprises three major internal components: Pattern Generation, Perception/Action, and Adaptation, enabling it to create situationally-relevant abstract patterns, match sensory input to a suitable abstract pattern in a multilayered top-down/bottom-up fashion similar to the mechanisms used for visual perception in the brain, and generate new abstract patterns. The CPKG allows the operator to focus on things other than the operation of the robot(s).

Proceedings Article | 15 May 2012 Paper

Coordinating with Humans by Adjustable-Autonomy for Multirobot Pursuit (CHAMP)

Danielle Dumond, Jeanine Ayers, Nathan Schurr, Alan Carlin, Dustin Burke, Jeffrey Rousseau

Proceedings Volume 8387, 838703 (2012) https://doi.org/10.1117/12.920451

KEYWORDS: Robots, Algorithm development, Buildings, Robotics, Sensors, Computer simulations, Detection and tracking algorithms, Control systems, Motion models, Software development

Read Abstract +

One of the primary challenges facing the modern small-unit tactical team is the ability of the unit to safely and effectively search, explore, clear and hold urbanized terrain that includes buildings, streets, and subterranean dwellings. Buildings provide cover and concealment to an enemy and restrict the movement of forces while diminishing their ability to engage the adversary. The use of robots has significant potential to reduce the risk to tactical teams and dramatically force multiply the small unit's footprint. Despite advances in robotic mobility, sensing capabilities, and human-robot interaction, the use of robots in room clearing operations remains nascent. CHAMP is a software system in development that integrates with a team of robotic platforms to enable them to coordinate with a human operator performing a search and pursuit task. In this way, the human operator can either give control to the robots to search autonomously, or can retain control and direct the robots where needed. CHAMP's autonomy is built upon a combination of adversarial pursuit algorithms and dynamic function allocation strategies that maximize the team's resources. Multi-modal interaction with CHAMP is achieved using novel gesture-recognition based capabilities to reduce the need for heads-down tele-operation. The Champ Coordination Algorithm addresses dynamic and limited team sizes, generates a novel map of the area, and takes into account mission goals, user preferences and team roles. In this paper we show results from preliminary simulated experiments and find that the CHAMP system performs faster than traditional search and pursuit algorithms.

Proceedings Article | 1 May 2009 Paper

Evaluation of terrain parameter estimation using a stochastic terrain model

Danielle Dumond, Laura Ray, Eric Trautmann

Proceedings Volume 7332, 73321F (2009) https://doi.org/10.1117/12.817737

KEYWORDS: Stochastic processes, Robots, Soil science, Sensors, Statistical analysis, Filtering (signal processing), Statistical modeling, Resistance, Electronic filtering, Digital filtering

Read Abstract +

Autonomous vehicles driving on off-road terrain exhibit substantial variation in mobility characteristics even when the terrain is horizontal and qualitatively homogeneous. This paper presents a simple stochastic model for characterizing observed variability in vehicle response to terrain and for representing transitions between homogeneous terrain with local variability or between heterogeneous terrain types. Such a model provides a means for more realistic evaluation of terrain parameter estimation methods through simulation. A stochastic terrain model in which friction angle and soil cohesion are represented by Gaussian random variables qualitatively represents observed variability in traction vs. slip characteristics measured experimentally. The stochastic terrain model is used to evaluate a terrain parameter estimation method in which terrain forces are first estimated independent of a terrain model, and subsequently, parameters of a terrain model, such as soil cohesion, friction angle, and stress distribution parameters are determined from estimated vehicle-terrain forces. Simulation results show drawbar pull vs. slip characteristics resulting from terrain parameter estimation are within statistical bounds established by the stochastic terrain model.

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