Ryan Skeele
Papers
3
Total Citations
144
H-Index
3
About
Ryan Skeele is a roboticist whose research lies at the intersection of multi-robot coordination, autonomous exploration, and risk-aware planning under uncertainty. His most impactful work, a 2015 paper on multi-UAV exploration with limited communication and battery (115 citations), introduced an adaptive coordination algorithm that enables heterogeneous teams of aerial robots to efficiently map unknown environments despite unreliable connectivity and energy constraints—a critical advance for real-world search-and-rescue and environmental monitoring. Skeele further pioneered risk-aware decision-making with his 2018 algorithm for dynamic edge cost discovery (26 citations), which allows robots to plan paths through graphs with uncertain costs by learning true values as they move, improving safety in unpredictable terrains. His earlier work on "tricking" cost-based planners into cooperative behavior, though less cited, creatively addressed UAV traffic management by manipulating cost spaces to induce safe, coordinated trajectories without explicit inter-robot communication. Skeele’s contributions bridge theoretical planning algorithms and practical deployment challenges, making him a notable figure in field robotics and multi-agent systems.
Research Focus
Key Achievements
Top Papers
- 1Multi-UAV exploration with limited communication and battery115 citations · 2015
- 2Risk-aware graph search with dynamic edge cost discovery26 citations · 2018
- 3Learning to trick cost-based planners into cooperative behavior3 citations · 2015