Lindsay Burke

Swarm robotics introduces the potential of simple agents to achieve complex emergent behaviors that extend far beyond the abilities of an individual. From the micro-scale of targeted intra-venous drug delivery robots, to the macro-scale of fleets of construction robots optimally transporting materials, swarm systems could enable decentralized, adaptable approaches to coordination in dynamic environments. However, modeling swarm coordination algorithms on a large scale tends to become computationally expensive as algorithmic complexity scales with swarm size, which makes it difficult to model hundreds or even thousands of agents that can potentially operate within a swarm system. Further, experimental modeling in real-world hardware becomes a challenge due to high cost and manufacturing time of agents, which makes simulation more feasible to test algorithmic limitations. Consequently, my research aims to use computational methods to explore emergent behaviors and optimized control algorithms in large-scale microrobotic swarm systems.