Papers
9
Total Citations
151
H-Index
7
About
Greg Droge’s research lies at the intersection of robotics, control theory, and human-robot interaction, with a focus on enabling intelligent, cooperative autonomy. His most influential work, “Less Is More: Mixed-Initiative Model-Predictive Control With Human Inputs” (69 citations), pioneers a method for seamlessly blending human commands with robot decision-making using model-predictive control—a framework that has become foundational for shared autonomy systems. Droge has also made significant contributions to multi-robot coordination, introducing a modified Kuramoto model for balanced deployment on a circle (19 citations) and developing behavior-based MPC for networked multi-agent systems. His work on adaptive look-ahead horizons for navigation in unknown environments (13 citations) and road-following formation control (12 citations) addresses practical challenges in field robotics, while his optimal decentralized gait transitions for snake robots (12 citations) showcases his versatility across platforms. Notably, Droge led the development of M2PEM, a graphical mission planning framework for unmanned vehicle teams (10 citations), and introduced a dual-mode dynamic window approach with convergence guarantees (2020). With over 150 total citations, Droge’s research continues to shape how robots navigate, coordinate, and collaborate with humans in complex, unstructured environments.
Research Focus
Key Achievements
Top Papers
- 1Less Is More: Mixed-Initiative Model-Predictive Control With Human Inputs69 citations · 2013
- 2Balanced deployment of multiple robots using a modified kuramoto model19 citations · 2013
- 3Adaptive look-ahead for robotic navigation in unknown environments13 citations · 2011
- 4Road-following formation control of autonomous ground vehicles12 citations · 2015
- 5Optimal decentralized gait transitions for snake robots12 citations · 2012
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- 7Behavior-based switch-time MPC for mobile robots9 citations · 2012
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