Xueqiu Wang

Papers

Total Citations

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About

Xueqiu Wang is a robotics researcher specializing in motion planning and autonomous navigation, with a particular focus on improving the efficiency and safety of mobile robot pathfinding. Their most notable contribution is the development of an improved Rapidly-exploring Random Tree (RRT) algorithm, which addresses key limitations in traditional path planning methods—namely, slow convergence and suboptimal paths in complex environments. This work, published in 2024, has already garnered 4 citations, signaling early recognition in the field. Wang’s approach enhances the RRT algorithm by incorporating heuristic guidance and adaptive sampling, enabling robots to navigate cluttered or dynamic spaces more reliably. While their citation count is still growing, the research holds promise for applications in warehouse logistics, autonomous vehicles, and search-and-rescue operations. Wang’s work reflects a commitment to bridging theoretical algorithm design with practical robotic systems, making their contributions valuable for students and researchers exploring real-time path planning solutions. As the field of mobile robotics expands, Wang’s innovations are poised to influence future developments in autonomous navigation.