Clutter Reduction for Geocollaborative Interfaces
As robotic capabilities become more autonomous and reliable, there is a desire to integrate robots into first response and military deployments. A benefit, but also a curse of robotic technology is the immense amount of information that is collected and relayed to the human operator via the interface. During a large, mass casualty event the overall response system incorporates information from multiple sources, including: first response personnel responding to the event, a priori information about the incident location, incident command, social networks, etc. Often, it is critical to provide access to all the information, even if it is not currently relevant to a particular human operator. Clutter emerges as a significant problem when all information is available and the human must attend to particular aspects. Most clutter reduction approaches require a priori classification of information, human classification in real time, or are random. None of these approaches are appropriate for high stress, unknown, and dynamic situations.
We have developed two methods for addressing different aspects of this problem. The General Visualization and Abstraction algorithm provides a means of automatically reducing clutter based on information type, novelty, relevance, spatial aspects and human operator role. Feature Sets is an approach that focuses on reducing clutter by considering the environmental context, geospatial relationships, temporal aspects, and semantic relationships. This presentation will focus on the problem challenges, the algorithms, associated results and future directions.
Dr. Julie A. Adams is an Associate Professor of Computer Science and Computer Engineering in the Electrical Engineering and Computer Science Department at Vanderbilt University. She is also the founder of the Human-Machine Teaming Laboratory. Prior to joining Vanderbilt, Dr. Adams was an Assistant Professor of Computer Science at Rochester Institute of Technology (RIT). Before returning to academia, she worked in Human Factors for Honeywell, Inc. and the Eastman Kodak Company from 1995 to 2000.
Dr. Adams received her Ph.D. degree in Computer and Information Sciences in 1995 from the University of Pennsylvania (Penn), performing her research on human-robotic interaction for multi-robot systems in Penn's General Robotics, Automation, Sensing and Perception (GRASP) Laboratory. She received her M.S.E. degree in Computer and Information Sciences from the University of Pennsylvania, and her B.S. in Computer Science and B.B.E. in Accounting from Siena College. She was the recipient of the NSF CAREER award.