Daniel Boley
Papers
7
Total Citations
95
H-Index
5
About
Daniel Boley is a leading figure in robotics and control theory, whose work bridges the gap between efficient algorithms and real-world robotic systems. His primary research areas include robot motion planning, localization, and robust stability analysis. Boley’s most significant contributions lie in developing novel computational methods that dramatically improve robot performance under uncertainty. For instance, his work on a parallel formulation of informed randomized search (40 citations) demonstrated how to generate paths for complex, high-degree-of-freedom robots in seconds, a breakthrough for articulated manipulators operating in realistic 3D environments. He also pioneered the use of recursive total least squares (RTLS) as a powerful alternative to the Kalman filter for robot navigation (26 citations), offering superior handling of noisy sensor data. This RTLS approach, further refined in subsequent papers, provides a rapidly converging method for mobile robot localization with limited sensor readings. Beyond robotics, Boley has explored robust stability in parameter spaces using matrix pencil methods. His recent work extends to multi-robot systems, modeling correlated random walks to predict hitting time distributions. With a career spanning decades, Boley’s algorithms remain foundational for researchers tackling motion planning and sensor fusion challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robot localization from landmarks using recursive total least squares26 citations · 2002
- 3
- 4A Rapidly Converging Recursive Method for Mobile Robot Localization7 citations · 1998
- 5
- 6Building and navigating a network of local minima4 citations · 2001
- 7