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ECE Master of Science Thesis Defense By: Abigail Rachel Keith

When: Friday, October 29, 2021
9:00 AM - 11:00 AM
Where: > See description for location
Cost: Free
Description: Topic: Analysis and Implementation of INFOTAXIS as a Practical Strategy to Maximize Target-Search Efficiency

Location: Dion 114

Zoom Teleconference:
Please contact Dr. John R. Buck via email at for Meeting ID and Passcode.

Traditionally, search strategies are categorized into one of two types: explorative and exploitative. Explorative search strategies search by exploring an area without using gathered information to guide their search; this strategy type functions best when there is high clutter, which makes detections untrustworthy. Exploitative search strategies search by trusting available information and using it to guide their search; this strategy type functions best when there is low clutter, which makes detections, and thus, available information, more trustworthy. Infotaxis melds the two types of search strategies together by using explorative tactics in high clutter density and exploitative tactics in low clutter density to detect targets faster than established search strategies. It achieves this by maximizing information gain through maximizing entropy reduction. Infotaxis selects which grid cells it measures each iteration by calculating which cell will reduce the amount of entropy or uncertainty the most. Previously, infotaxis has been used to search for a target based on passive odor sensing. This thesis tests a version of infotaxis that instead uses active sensing to capitalize on available information and in turn, speed up the search process. This thesis demonstrates that infotaxis is faster than traditional search strategies and can be implemented into a searching robot using ultrasonic sensors. Through constructing a MATLAB search strategy simulation, the speed of infotaxis is compared to three other search strategies: Maximum A Posteriori (MAP), cycling in order and random searching. Infotaxis is found to be faster than cycling in order and random searching under all tested conditions, and it is slightly faster than MAP when the probability of detection, PD, decreases. Infotaxis is implemented into a real search for a single target by programming an iRobot Create 2 to search a linear row of ten cells with the infotaxis method using an HC-SR04 ultrasonic sensor to make detections. With PD = 0.7 and a probability of false alarm, PD = 0.1, the robot finds the target with infotaxis 100% faster than MAP. By implementing infotaxis using a robot and ultrasonic sensor, this thesis demonstrates that infotaxis can be used as a search strategy in the real world, outside of simulations.

Note: All ECE Graduate Students are ENCOURAGED to attend.
All interested parties are invited to attend. Open to the public.

Advisor: Dr. John R. Buck

Committee Members: Dr. Paul J. Gendron, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Karen Payton, Professor Emerita, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Amir Habboosh, Engineering Fellow, Raytheon Technologies; Dr. Wu-Jung Lee, Senior Oceanographer, Applied Physics Lab, University of Washington

*For further information, please contact Dr. John R. Buck via email at
Topical Areas: General Public, University Community, College of Engineering, Electrical and Computer Engineering