Yeheng Chen
Papers
1
Total Citations
7
H-Index
1
About
Yeheng Chen is a robotics researcher whose work bridges the gap between simulation and real-world autonomous systems. His primary research areas include robotic manipulation, simulation environments, and deep learning-based perception for robotic control. Chen’s most notable contribution is his 2020 paper, "Implementation of a unified simulation for robot arm control with object detection based on ROS and Gazebo," which has garnered 7 citations. In this work, he developed a method integrating deep learning-based object detection with robotic arm control within a Gazebo simulation environment running on the Robot Operating System (ROS). This unified framework allows researchers to test perception and control algorithms in a safe, reproducible setting before deploying them on physical hardware. By simplifying the integration of ROS with advanced object detection models, Chen’s work provides a practical template for developing and validating robotic systems. His research is particularly valuable for students and engineers entering the field of autonomous robotics, offering a clear pathway from simulation to real-world application.
Research Focus
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Top Papers
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