Xiao‐Jun Wu

Nanjing Forestry University, Nanyang Technological University, Shenyang Institute of Automation

Papers

Total Citations

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About

Xiao‐Jun Wu’s research lies at the intersection of agricultural robotics and intelligent motion planning, with a focus on enabling manipulators to operate efficiently in complex, unstructured environments. His most cited work, a 2025 study on 3D obstacle avoidance for apple-picking robotic arms, tackles the critical challenge of low efficiency in automated harvesting. By integrating an improved informed-RRT* algorithm with the artificial potential field method, Wu’s approach significantly enhances path planning performance, allowing robotic arms to navigate around obstacles in real-time—a breakthrough for precision agriculture. This paper has already garnered 10 citations, reflecting its timely relevance. Earlier contributions include a 2005 study on a general manipulator path planner using fuzzy reasoning (5 citations), which introduced a multi-agent, hierarchical framework for high-DOF robots in dynamic environments, and a 2004 method for converting polygonal meshes to volumetric datasets (3 citations), a foundational technique for collision detection in robotics. Wu’s work demonstrates a sustained commitment to bridging theoretical algorithms with practical robotic applications, making him a notable figure in the advancement of autonomous systems for agricultural and industrial settings.