Collective Perception and Decision Making in a Robot Swarm
Advisor: Prof. Radhika Nagpal, Harvard University
This research aims to improve the ability of a large group of robots to perceive and classify their environment by employing robots with different perceptual skills. The ability to make collective decisions is a critical component to developing complex collective behavior and intelligence and can contribute to the broader challenge of translating global goals to local rules.
In a first paper, we demonstrated that a bio-inspired algorithm that allowed a collective of Kilobots to discriminate between multiple binary-state features simultaneously. We also explored strategies for allocating robots between features, finding approaches that proved successful even when the initial distribution of robots across features was poor.
Currently, I am developing a more general framework for distributed Bayesian decision-making in robots.
2018. Multi-feature collective decision making in robot swarms. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 1711–1719. Stockholm, Sweden.
2019. Bayes Bots: Bayesian Decision-Making for Robot Swarms. Poster at DOE CSGF Program Review (14–18 July 2019). Washington, DC.