Arielle Charles
Papers
1
Total Citations
4
H-Index
1
About
Arielle Charles is a rising researcher in autonomous mobile robotics, with a primary focus on sensor fusion, object recognition, and quadrupedal locomotion. Her most-cited work, "Optimizing Data Capture Through Object Recognition for Efficient Sensor and Camera Management with a Quadruped Robot" (2024, 4 citations), introduces a novel integration of the YOLOv8 machine learning module with an RGB camera system aboard the Unitree GO1 robotic dog. This research advances real-time data capture by enabling quadrupedal swarm units to intelligently manage sensor and camera resources through object recognition, significantly improving efficiency in dynamic environments. Charles’s contributions lie at the intersection of computer vision and robotics, demonstrating how lightweight AI models can enhance autonomous decision-making in legged robots. Her work has implications for search-and-rescue, environmental monitoring, and industrial inspection, where adaptive sensor management is critical. Though early in her career, Charles’s innovative approach to combining quadrupedal mobility with efficient data processing marks her as a promising voice in the field of embodied AI and autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1