Papers
8
Total Citations
51
H-Index
5
About
Shyba Zaheer has made significant contributions to autonomous mobile robotics, specializing in path planning, obstacle avoidance, and trajectory learning. Her pioneering work introduced the innovative "Free-configuration Eigenspace" (FCE) framework, a novel approach that transforms raw sensor data into low-dimensional eigenspaces for real-time navigation. This methodology, detailed in her most cited paper (12 citations), enables robots to detect unknown obstacles and avoid collisions while simultaneously steering toward targets, outperforming traditional techniques like A* and potential fields. Her research on autonomous trajectory learning using non-point-based maps from laser data (10 citations) and integrated path planning and control systems (7 citations) has advanced the field's understanding of smooth, efficient navigation in cluttered environments. Zaheer has also explored dynamic obstacle avoidance using Bézier curves and trajectory outlier detection through beta-eigenspaces. Her recent work incorporates YOLO-based deep learning for online path planning, demonstrating her continued innovation. With over 40 total citations, Zaheer's FCE paradigm represents a notable achievement in bridging sensor-space to eigenspace, offering practical solutions for search and rescue missions and autonomous driving. Her research remains influential for students and engineers developing robust, real-time navigation systems for mobile robots.
Research Focus
Key Achievements
Top Papers
- 1
- 2Autonomous trajectory learning using free configuration-eigenspaces10 citations · 2009
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- 6A trajectory learner for sonar based LEGO NXT differential drive robot4 citations · 2014
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