Kabirat Olayemi
Papers
3
Total Citations
16
H-Index
2
About
Kabirat Olayemi is a rising researcher at the forefront of autonomous mobile robotics, specializing in the intersection of safety-critical control, deep reinforcement learning (DRL), and perception-aware navigation. Her work addresses fundamental challenges in enabling robots to operate reliably in dynamic, uncertain environments. Her most-cited paper, "Model-Free Safety Critical Model Predictive Control for Mobile Robot in Dynamic Environments" (2024, 8 citations), introduces a novel framework that integrates safety constraints directly into Nonlinear Model Predictive Control (NMPC), overcoming parametric uncertainty and measurement inaccuracies—a critical step for real-world deployment. Complementing this, her study on "The Impact of LiDAR Configuration on Goal-Based Navigation within a Deep Reinforcement Learning Framework" (2023, 6 citations) systematically analyzes how sensor design, specifically LiDAR field of view, influences DRL-based mapless navigation performance, providing actionable insights for sensor selection. Most recently, in "A Twin Delayed Deep Deterministic Policy Gradient Algorithm for Autonomous Ground Vehicle Navigation via Digital Twin Perception Awareness" (2024, 2 citations), she pioneers the use of digital twins to enhance training and evaluation of autonomous ground vehicles, aiming to improve safety and accessibility. Collectively, her work has garnered over 16 citations, establishing her as a promising voice in safe, learning-driven robot autonomy.
Research Focus
Key Achievements
Top Papers
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