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About

Qingdang Li is a robotics researcher whose work centers on intelligent motion planning for industrial and agricultural automation. His primary contributions lie in advancing rapidly-exploring random tree (RRT) algorithms, particularly for robotic manipulators and mobile robots operating in complex, constrained environments. Li’s most cited work, a 2023 survey on RRT-based path planning for industrial robots (33 citations), established a foundational review of probabilistic completeness in motion planning. He then developed practical enhancements, including an improved Bi-RRT algorithm for robotic manipulators (14 citations, 2024) and a DGABI-RRT variant for mobile robots (6 citations, 2021), both designed to boost efficiency and adaptability. Notably, his 2025 paper on efficient motion planning for chili flower pollination mechanisms (12 citations) extends his expertise into precision agriculture, demonstrating real-world impact beyond traditional manufacturing. With a growing citation record and a focus on bridging theoretical RRT advances with deployable solutions, Li’s work is shaping the next generation of autonomous systems in both factory and field settings.