Papers
4
Total Citations
49
H-Index
3
About
Bing Qiao is a robotics researcher whose work centers on autonomous navigation and control for mobile robots and manipulators. Her most significant contribution is in path planning, where she developed a predictive algorithm using Rapidly Exploring Random Trees (RRT) for dynamic environments—a paper that has garnered 29 citations. She further advanced the field by integrating deep reinforcement learning, specifically a Proximal Policy Optimization (PPO) algorithm, to enable robots to navigate unknown environments without prior maps, a study cited 13 times. Qiao also addressed foundational challenges in robot vision, proposing a novel joint calibration method that combines camera and hand-eye calibration for monocular vision systems, improving efficiency in robotic manipulation tasks. Her work on decentralized robust control for robotic manipulators, using torque feedbacks, adds to her portfolio of practical, real-world solutions. With a focus on bridging theoretical algorithms and applied robotics, Qiao’s research has direct implications for autonomous systems in manufacturing, exploration, and service robotics, making her a notable contributor to the field.
Research Focus
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