Xiaohong Peng
Papers
2
Total Citations
16
H-Index
2
About
Dr. Xiaohong Peng is a researcher whose work bridges artificial intelligence and engineering design, with a focus on enhancing autonomous systems and decision-making processes. Her most cited paper, "Enhanced Autonomous Navigation of Robots by Deep Reinforcement Learning Algorithm with Multistep Method" (2021, 12 citations), introduces a novel MS-DDQN algorithm that combines multistep updates with deep reinforcement learning, significantly improving mobile robot navigation in complex environments. This contribution addresses critical challenges in robotics, offering more efficient and adaptive path-planning solutions. Additionally, her work "Design Concept Evaluation Based on Rough Number and Information Entropy Theory" (2015, 4 citations) tackles early-stage product development by integrating rough set theory and information entropy to reduce subjectivity in expert evaluations, providing a more robust framework for design concept selection. Dr. Peng’s research demonstrates a unique ability to apply computational intelligence to both autonomous robotics and engineering design, showcasing her versatility. Her contributions are particularly valuable for advancing real-world applications in robotics and manufacturing, where precise navigation and reliable design evaluation are paramount.
Research Focus
Key Achievements
Top Papers
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