Robot swarms, large groups of robots working together as a distributed system to reliably and efficiently accomplish tasks, have the potential to be uniquely useful in many areas such as natural disaster response, land mine detection, self-assembly, and inspection of spacecraft. These applications and others can be abstracted to the general problem of task allocation, in which tasks with possibly different demands are located across an environment, and robots must efficiently discover and assign themselves to these tasks. While several algorithms for task allocation have been proposed, most of them assume either prior knowledge of task locations or a fixed set of tasks at unknown locations. In this project, we wish to investigate dynamic task allocation strategies for robot swarms.

Through this SuperUROP, I hope to gain experience in theoretical computer science research. I am interested in applying my knowledge from courses I have taken in algorithms and probability to develop and analyze distributed algorithms with several real-world applications.