Cheng‐Peng Li

Guangdong Ocean University, Chinese Academy of Sciences

Papers

Total Citations

H-Index

About

Cheng-Peng Li is a rising researcher at the intersection of robotics, autonomous systems, and intelligent perception. His primary research areas include autonomous robot exploration, robotic assembly, soft robotics, and 3D point cloud processing. Li’s most influential work, “Sample-based Frontier-Block Detection for Autonomous Robot Exploration” (2021, 8 citations), tackles the inefficiency of classical RRT-based frontier detection by introducing a novel, sample-driven approach that significantly accelerates exploration in unknown environments—a critical advance for search-and-rescue and planetary rovers. In robotic assembly, his 2021 study on “Irregular Shaped Peg-in-Hole with Partial Constraints” (4 citations) addresses the longstanding challenge of tight-tolerance assembly by developing force-control strategies that handle shape and clearance variances without requiring perfect alignment. More recently, Li has ventured into soft robotics with a 2025 paper on “Humidity-responsive hydrogel actuator and soft robot with dual-mode control” (3 citations), demonstrating a novel dual-mode actuation mechanism that expands the design space for compliant, environment-responsive robots. His 2023 work “PointStack” (1 citation) further showcases his versatility, proposing a deep learning architecture for enhanced global feature aggregation in point cloud processing. With a growing citation footprint and contributions spanning hardware, algorithms, and perception, Li is establishing himself as a multifaceted innovator in modern robotics.