Handong Cheng
Papers
2
Total Citations
6
H-Index
2
About
Handong Cheng is a robotics researcher whose work focuses on advancing autonomous systems for marine and amphibious applications. His primary research areas include target tracking, target recognition, and edge computing for robotic platforms. Cheng’s major contributions lie in developing novel algorithms that address critical challenges in real-world marine operations, such as poor tracking accuracy and low data transmission efficiency. His 2021 paper, "A Novel Target Tracking System for the Amphibious Robot based on Improved Camshift Algorithm" (4 citations), proposes a robust tracking system designed for marine rescue, garbage search, and aquaculture monitoring. In 2020, he introduced "A Novel Target Recognition System for the Amphibious Robot based on Edge Computing and Neural Network" (2 citations), which leverages edge computing to enhance recognition accuracy and transmission speed for sea rescue and garbage search missions. Though his citation counts are modest, Cheng’s work demonstrates practical innovation in integrating edge computing with neural networks for flexible, real-time robotic decision-making. His research holds significant potential for improving autonomous operations in challenging marine environments, making him a promising contributor to the field of amphibious robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2