Neal Seegmiller

About

Neal Seegmiller is a robotics researcher whose work has significantly advanced the modeling, control, and navigation of wheeled mobile robots (WMRs). His research spans vehicle kinematics and dynamics, inertial navigation, motion planning, and machine learning applied to robotic systems — areas in which he has built a cohesive and highly regarded body of work. Seegmiller's most influential contribution is his development of high-fidelity yet computationally efficient dynamic models for WMRs, a challenge central to enabling robots to operate reliably on uneven or low-traction terrain. His 2016 paper on this topic has garnered 49 citations, reflecting its broad adoption by the robotics community. Complementing this, his work on skid-steered robot modeling (34 citations) introduced learning-based approaches to capture complex kinematic and dynamic behaviors. His 2012 research on wheel slip modeling for inertial navigation (24 citations) demonstrated practical solutions for position-denied environments where GPS is unavailable. Early in his career, Seegmiller contributed to aerospace manufacturing, developing precision robotic coating systems for the F-35 Joint Strike Fighter — work cited 25 times and notable for bridging robotics with defense applications. Across more than a decade of research, his publications collectively illuminate a rigorous, mathematically principled approach to making mobile robots faster, smarter, and more reliable in real-world conditions.

Research Focus

Key Achievements

Top Papers

1. 1
   High-Fidelity Yet Fast Dynamic Models of Wheeled Mobile Robots

   49 citations · 2016
2. 2
   Learning of skid-steered kinematic and dynamic models for motion planning

   34 citations · 2017
3. 3
   Precision Robotic Coating Application and Thickness Control Optimization for F-35 Final Finishes

   25 citations · 2009
4. 4
   Aiding off-road inertial navigation with high performance models of wheel slip

   24 citations · 2012
5. 5
   Enhanced 3D Kinematic Modeling of Wheeled Mobile Robots

   20 citations · 2014
6. 6
   A Vector Algebra Formulation of Mobile Robot Velocity Kinematics

   19 citations · 2013
7. 7
   Optical flow odometry with robustness to self-shadowing

   18 citations · 2011
8. 8
   A Unified Perturbative Dynamics Approach to Online Vehicle Model Identification

   17 citations · 2016
9. 9
   Recursive kinematic propagation for wheeled mobile robots

   13 citations · 2015
10. 10
    Dynamic Model Formulation and Calibration for Wheeled Mobile Robots

    10 citations · 2014

Key Collaborators

Contact & Links