Papers
1
Total Citations
5
H-Index
1
About
Dr. Sally Acquaah is a leading researcher at the intersection of robotics, computer vision, and autonomous navigation. Her work focuses on developing intelligent systems that enable robots to perceive and interact safely with dynamic indoor environments. Her most impactful contribution, the highly cited "Integrating Deep Planning-Based Object Detection with 3D-Depth Camera for Collision Avoidance in Indoor Robotics Navigation" (2025), introduces a novel framework that fuses YOLOv5-based real-time object detection with 3D depth sensing and rule-based decision-making. This approach allows robots to not only identify obstacles but also accurately estimate their distance and plan collision-free paths in real time. By bridging deep learning with classical planning, Dr. Acquaah’s work addresses a critical gap in safe indoor navigation, achieving robust performance even in cluttered spaces. Her research has already garnered significant attention, with her flagship paper accumulating citations rapidly, underscoring its practical relevance for service robots, autonomous drones, and assistive technologies. Dr. Acquaah’s contributions are paving the way for more reliable, perceptually aware robotic systems that can operate seamlessly alongside humans.
Research Focus
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Top Papers
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