Haifeng Lu

Papers

Total Citations

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About

Haifeng Lu’s research centers on mobile robot path planning and obstacle avoidance, with a particular focus on overcoming the limitations of traditional potential field methods. His major contribution lies in developing bio-inspired navigation algorithms that mimic the natural flow of water to solve persistent problems in robotics, such as local minima traps and oscillatory behavior in cluttered or narrow environments. In his most cited work (21 citations), Lu proposed a novel approach that treats the robot’s starting position as an artificial headstream, allowing an artificial stream to flow from high to low potential fields, effectively guiding the robot through complex, unknown, and static environments. This method not only avoids inherent pitfalls of conventional techniques but also ensures smoother, more reliable trajectories. His earlier work (6 citations) further refined this concept into an improved potential grid method, demonstrating the scalability of his water-flow analogy for path planning. Lu’s research has practical implications for autonomous navigation in real-world settings, offering a computationally efficient and robust alternative to existing algorithms. His innovative use of fluid dynamics principles in robotics marks a notable achievement, bridging theoretical modeling with applied engineering.