Ian Buckley
Papers
9
Total Citations
168
H-Index
5
About
Ian Buckley’s research lies at the intersection of multi-robot systems, formation control, and safety-critical autonomy. His major contributions center on developing novel theoretical frameworks for provably safe and composable robot coordination, particularly through hybrid nonsmooth barrier functions—a breakthrough enabling rigorous collision avoidance guarantees while allowing robots to execute complex tasks. Buckley’s work on infinitesimal shape-similarity has fundamentally advanced the understanding of how sensing modalities, such as bearing-only measurements, constrain the geometric formations a robot team can achieve, providing both characterization and control strategies for these systems. His most cited paper, “Hybrid Nonsmooth Barrier Functions With Applications to Provably Safe and Composable Collision Avoidance for Robotic Systems” (91 citations), exemplifies his impact in making autonomous systems safer and more reliable. Beyond theory, Buckley has developed accessible platforms for undergraduate research in cooperative control and swarm robotics, fostering the next generation of roboticists. His work on autonomous under-ice profilers also demonstrates a commitment to real-world applications, pushing the boundaries of multi-vehicle teams in extreme environments. With a growing citation record and a clear trajectory of innovation, Buckley is shaping the future of safe, scalable multi-robot autonomy.
Research Focus
Key Achievements
Top Papers
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- 3Infinitesimally shape-similar motions using relative angle measurements21 citations · 2017
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- 6Self-Assembly of a Class of Infinitesimally Shape-Similar Frameworks4 citations · 2018
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- 8Controller Synthesis for Infinitesimally Shape-Similar Formations2 citations · 2020
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