Chase M Greenhagen

Papers

Total Citations

H-Index

About

Chase M Greenhagen is a researcher at the intersection of swarm intelligence and real-world robotics, focusing on translating nature-inspired algorithms into practical, hardware-constrained applications. His work centers on the adaptation of evolutionary and bio-mimetic optimization methods—specifically Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)—for deployment on physical multi-robot systems. In his highly cited 2016 paper, Greenhagen demonstrated a real-life robotic application of PSO, modifying the classic algorithm to control a small robot swarm in a search and rescue scenario, addressing the critical gap between theoretical simulation and tangible deployment. His 2015 work further advanced the field by adapting the ACO algorithm for execution on multi-core robotic platforms, enabling more efficient pathfinding under strict computational limits. With each of his top papers garnering 9 citations, Greenhagen’s contributions are recognized for their practical engineering rigor, offering a blueprint for researchers aiming to implement complex swarm behaviors on resource-constrained hardware. His achievements highlight a commitment to bridging algorithmic theory with operational reality, making his work essential reading for students and engineers developing autonomous, cooperative robotic systems.