Haoyue Liu

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About

Haoyue Liu is a researcher in robotics and autonomous navigation, with a primary focus on motion planning and path optimization. Their most notable contribution is the development of a goal-biased bidirectional Rapidly-exploring Random Trees (RRT) algorithm enhanced by curve-smoothing, published in 2019. This work addresses a critical challenge in robotic path planning: generating smooth, feasible trajectories in complex environments while maintaining computational efficiency. By intelligently biasing tree growth toward the goal and seamlessly connecting two independently grown trees, Liu's method significantly improves both path quality and convergence speed over traditional RRT approaches. With 44 citations, this paper has become a reference point for researchers working on sampling-based planning algorithms. Liu's work bridges the gap between theoretical path planning and practical robotic applications, offering solutions that reduce jerk and energy consumption in real-world systems. Their contributions are particularly valuable for autonomous vehicles, mobile robots, and manipulators operating in cluttered spaces, where smooth motion is essential for safety and efficiency.