Penggang Gao

Westlake University, Institute of Disaster Prevention

Papers

Total Citations

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About

Dr. Penggang Gao is a robotics researcher whose work bridges autonomous navigation and bio-inspired engineering. His primary research areas include robot path planning, reinforcement learning applications, and biomimetic robotic systems for industrial inspection. Gao's most impactful contribution comes from his 2019 paper on global path planning using reinforcement learning, which has garnered 44 citations—a strong indicator of its influence in the field. This work addresses critical limitations in traditional algorithms like BFS and RRT by producing shorter, smoother paths for autonomous mobile robots, directly advancing practical navigation in complex environments. In a more specialized vein, his 2018 study on a pipeline leak detection robot fish demonstrates innovative cross-disciplinary thinking. By simplifying fish motion models and integrating a single-joint driving mechanism with an OV7725 image sensor, Gao designed a compact system capable of navigating pipes to detect oil leaks. While this work has fewer citations, it showcases his versatility in applying biological principles to industrial challenges. Together, these contributions position Gao as a researcher tackling both fundamental algorithmic problems and applied robotic systems, with his path planning work particularly shaping how robots move through the world.