Husna Mutahira
Papers
5
Total Citations
81
H-Index
4
About
Husna Mutahira’s research lies at the intersection of autonomous robot navigation, deep reinforcement learning (DRL), and intelligent path planning, with a strong emphasis on enabling socially compliant behavior in crowded environments. Her most impactful work introduces a “dynamic warning zone” and short-distance goal framework for DRL-based navigation, achieving 31 citations by addressing the critical challenge of collision avoidance and human motion prediction. Complementing this, her memory-based crowd-aware navigation model (22 citations) leverages past observations to improve robot decision-making in dynamic settings, pushing the boundaries of service robot autonomy. Beyond navigation, Mutahira has advanced aerial robotics with the ABA* (Adaptive Bidirectional A*) algorithm (18 citations), which optimizes collision-free paths under kinematic constraints. Her contributions extend to smart agriculture, where she developed a stacking ensemble model for predicting sensor data in paprika greenhouses (7 citations), and to 3D shape reconstruction using deep neural networks for robotic perception. Through these works, Mutahira demonstrates a rare ability to bridge theoretical DRL advances with practical robotic systems, earning recognition for enhancing both safety and efficiency in real-world applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Memory-based crowd-aware robot navigation using deep reinforcement learning22 citations · 2022
- 3ABA*–Adaptive Bidirectional A* Algorithm for Aerial Robot Path Planning18 citations · 2023
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- 5