Papers
2
Total Citations
4
H-Index
1
About
Peiqi Li is a researcher whose work bridges intelligent robotics and power system automation, with a particular focus on enhancing operational safety and efficiency in high-risk environments. A key contribution is the development of an image recognition system for substation inspection robots, integrating edge computing and incremental learning to enable real-time, adaptive fault detection. This work, published in 2021 with 3 citations, addresses the critical challenge of automating inspections in large-scale, high-voltage power grids, reducing both workload and human risk. Li’s research also extends to bio-inspired robotics, as demonstrated in a 2025 study on flapping-wing robots. By employing a pseudo-rigid-body model and multi-objective optimization, Li designed an optimized actuator that closely mimics natural flapping dynamics, advancing the field of biomimetic aerial vehicles. This work, with 1 citation, showcases a rigorous approach to mechanical design and dynamic modeling. Together, these contributions highlight Li’s ability to apply cutting-edge computational and mechanical techniques to solve real-world problems in both industrial automation and robotics, making a meaningful impact on the safety and autonomy of critical infrastructure systems.
Research Focus
Key Achievements
Top Papers
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