Cheng Shao

Papers

Total Citations

H-Index

About

Cheng Shao’s research lies at the intersection of robotics, computer vision, and intelligent control, with a focus on enabling autonomous systems to perceive and interact with their environments. His most cited work, “Target Position and Posture Recognition Based on RGB-D Images for Autonomous Grasping Robot Arm Manipulation” (2020, 7 citations), introduces a novel method that fuses RGB and depth images to accurately identify a target’s location and orientation—a critical step for robotic grasping. This contribution addresses a fundamental challenge in autonomous manipulation, offering a practical solution for robots to handle objects in unstructured settings. Shao also explores cooperative and decentralized systems in “Biologically Inspired Algorithms for Optimal Control” (2004, 6 citations), which examines how nature-inspired strategies can improve coordination among multiple agents, such as in mobile exploration tasks. Additionally, his work on “A Robust Iterative Learning Control with Neural Networks for Robot” (2004, 2 citations) demonstrates an early integration of neural networks with iterative learning to enhance robot control precision. Together, these studies highlight Shao’s commitment to advancing robotic autonomy through vision-based recognition and adaptive control, laying groundwork for more capable and intelligent robotic systems.