Papers
8
Total Citations
71
H-Index
5
About
J. Willard Curtis is a researcher whose work lies at the intersection of multi-agent robotics, human-robot collaboration, and control theory. His key contributions span map merging for cooperative robots, where he developed algorithms to integrate data from multiple ground robots despite differences in orientation, corruption, and scale—a problem critical for expanding individual robotic capabilities. Curtis has also advanced information fusion in human-robot teams, creating neural network-based methods to combine hard sensor data with soft human observations for tracking moving objects, addressing the challenge of modeling human input. His work on satisficing control for multi-agent formations, with 10 citations, introduced robust control Lyapunov functions to guarantee bounded formation errors, while his model-predictive satisficing approach tackled nonlinear tracking for nonholonomic robots. With over 70 total citations across his most-cited papers, Curtis has demonstrated impact in areas like adaptive path planning with step-length RRT algorithms and distributed guidance for urban target search. His research on passive switched system analysis for semi-autonomous systems further underscores his focus on safety in intermittently-teleoperated platforms, making his work valuable for students and researchers exploring the frontiers of autonomous and collaborative robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Satisficing control for multi-agent formation maneuvers10 citations · 2003
- 4A model-predictive satisficing approach to a nonlinear tracking problem6 citations · 2003
- 5Adaptive Step-length RRT Algorithm for Improved Coverage5 citations · 2016
- 6
- 7A hybrid estimation algorithm for tracking an adversarial team2 citations · 2017
- 8Passive switched system analysis of semi-autonomous systems2 citations · 2016