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About

Ashleigh Swingler’s research lies at the intersection of robotics, motion planning, and optimization, with a particular focus on developing novel computational frameworks for multi-agent systems. Her most influential contributions center on the use of disjunctive programming and cell decomposition to solve complex path planning and collision avoidance problems. In her highly cited 2010 paper, she introduced a cell decomposition approach that transforms obstacle-filled workspaces into disjunctive programs, enabling efficient minimum-distance path planning for multiple robotic vehicles. This foundational work was extended in 2011 to coordinate teams of mobile routers, ensuring network connectivity through motion planning. Swingler also made notable strides in pursuit-evasion problems, applying model-based cell decomposition to optimize paths for agents avoiding adversaries—a methodology she demonstrated through the classic video game Ms. Pac-Man. Her work on the duality of robot and sensor path planning further advanced the field by bridging sensing objectives with platform geometry. With multiple papers each garnering 16 citations, Swingler’s research has provided elegant, mathematically rigorous solutions to some of robotics’ most challenging coordination problems, establishing her as a key contributor to the theory and practice of autonomous multi-vehicle systems.