Loy McGuire
Papers
2
Total Citations
31
H-Index
2
About
Loy McGuire’s research lies at the intersection of swarm robotics, multi-agent systems, and bio-inspired algorithms, with a focus on solving complex coordination and path-planning problems. In his most-cited work, “Swarm and Multi-agent Time-based A* Path Planning for Lighter-Than-Air Systems” (2020, 26 citations), McGuire introduced a novel time-based A* approach that enables multiple autonomous agents—specifically lighter-than-air vehicles—to synchronize their movements and complete tasks simultaneously. This work addresses a critical challenge in multi-agent coordination by integrating dynamic constraints directly into the planning process, offering a scalable solution for real-world aerial swarms. McGuire further pushes the boundaries of swarm intelligence with his “Viscoelastic Fluid-Inspired Swarm Behavior to Reduce Susceptibility to Local Minima: The Chain Siphon Algorithm” (2021, 5 citations). Drawing inspiration from the self-siphoning behavior of viscoelastic fluids, this algorithm helps robotic swarms escape local minima—a common pitfall in distributed optimization—by mimicking fluid dynamics. Though newer, this work showcases McGuire’s talent for translating physical phenomena into robust, decentralized control strategies. His contributions are particularly impactful for researchers exploring autonomous aerial systems, cooperative robotics, and nature-inspired computation, offering both theoretical depth and practical pathways for multi-agent coordination in complex environments.
Research Focus
Key Achievements
Top Papers
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