Rui Gu

Papers

Total Citations

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About

Rui Gu is a researcher specializing in autonomous vehicle motion planning and robotics, with a particular focus on real-time, safe navigation for car-like robots. Their most cited work, "Online on-Road Motion Planning Based on Hybrid Potential Field Model for Car-Like Robot" (2022), has garnered 14 citations, establishing a foundation for integrating hybrid potential field models into dynamic on-road environments. This contribution addresses critical challenges in collision avoidance and path optimization under real-time constraints, offering a computationally efficient solution that balances safety and maneuverability. Gu’s research bridges theoretical control methods and practical deployment, making strides toward more reliable autonomous driving systems. Their work is notable for its emphasis on hybrid models that combine attractive and repulsive potential fields, enabling robots to navigate complex traffic scenarios with human-like decision-making. As a rising voice in intelligent transportation, Gu’s contributions are shaping the next generation of motion planning algorithms, with potential applications in logistics, urban mobility, and autonomous delivery systems.