Publication Date


Document Type


First Advisor

Butail, Sachit

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Mechanical Engineering


Robotic swarms provide a scalable and robust solution for monitoring large environments. In this context, the inclusion of a human has the potential advantage of incorporating prior knowledge about the target location or dynamics, thus increasing the overall efficiency of the task at hand. This is especially critical when dealing with time-intensive missions such as search and rescue. In this thesis we develop a general information-theoretic framework to control multiple autonomous robots in search and rescue missions that include a human teleoperator. Human prior knowledge is modeled to capture target location and dynamics, and a mutual information based control is formulated to let autonomous robots weight between two strategies: independent search or staying in proximity of a reference robot representing human input. The control actions optimize a weighted sum of normalized mutual information calculated using particle filtered estimates of the target and the reference robot. We implement the framework to simulate two widely different scenarios designed after search and rescue missions from literature and incorporate varying levels of accuracy in human prior knowledge. Our results indicate that within the simulated environments, mission performance depends on how robots weight between the two strategies, with the amount of the optimal control effort shared between strategies affected by prior knowledge and number of robots. The proposed information-theoretic abstraction of human robot interaction can be implemented on a wide variety of scenarios and the results highlight the role of human prior knowledge towards eliciting effective robotic assistance in time-intensive missions.

To help better understand how humans solve the search and rescue problem and the effect of prior knowledge about environment and target location, we next conduct a human-subjects study where participants teleoperate a ground robot as they search for a missing target with varying levels of prior knowledge about target location and environmental map. Our preliminary experimental results indicate that prior knowledge affects not only the time to find the missing target but also the teleoperation behavior with significant differences in robot speed and control actions made by a participant. The experimental data set and the preliminary results set the stage for a data-driven dynamic model of a human teleoperator in search operations.


97 pages




Northern Illinois University

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In Copyright

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Media Type


Included in

Robotics Commons