Raul G. Longoria
Papers
9
Total Citations
123
H-Index
5
About
Raul G. Longoria is a leading researcher in mobile robotics, specializing in assistive navigation, terrain interaction, and energy-aware autonomy. His work bridges mechanical systems and intelligent control, with a focus on making robots safer, more reliable, and more responsive to human needs. Longoria’s most cited paper, “A framework for planning comfortable and customizable motion of an assistive mobile robot” (2009, 40 citations), introduced user-centered motion planning for robots that transport people with mobility impairments—a key contribution to human-robot interaction. He has also made significant advances in modeling track–terrain interaction for small-scale robotic tracked vehicles, developing methods using extended Kalman filters to estimate slip, traction, and friction coefficients from sensor data (33 and 26 citations for his top two papers in this area). These contributions enable robots to adapt to unstructured environments and predict battery depletion under load uncertainty, improving mission feasibility in stochastic settings. More recently, Longoria has explored smart hand tools that use synthetic data and edge AI for work task recognition, reflecting a broadening interest in human-centered automation. With over 120 total citations, his work continues to inform the design of robust, energy-aware robotic systems for real-world deployment.
Research Focus
Key Achievements
Top Papers
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- 2Slip estimation for small-scale robotic tracked vehicles33 citations · 2010
- 3Modeling track–terrain interaction for transient robotic vehicle maneuvers26 citations · 2008
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