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About

Quancheng Pu is a leading researcher in autonomous robotics and intelligent navigation systems, with a focus on solving critical challenges in path planning, obstacle avoidance, and simultaneous localization and mapping (SLAM). His most impactful work centers on enhancing the A* algorithm for mobile robot navigation, where he has proposed novel hybrid approaches that combine improved A* with CSA-APF algorithms to overcome issues like dynamic obstacle avoidance and local optima entrapment. His 2024 paper on this topic has already garnered 20 citations, reflecting its significance in the field. Pu has also made notable contributions to dual-arm robot coordination, developing algorithms for multiple obstacle avoidance tasks that integrate zero-space and terminal obstacle avoidance with self-collision prevention. His work on time-jerk optimal trajectory planning using improved dingo optimization further demonstrates his versatility in robotic arm control. Additionally, Pu has advanced SLAM technology for unmanned vehicles in dynamic environments by integrating YOLOv7 object detection, addressing a key limitation of traditional SLAM systems that assume static surroundings. With over 50 total citations across his most-cited papers, Pu's research is driving practical improvements in autonomous navigation, making robots more adaptable and efficient in complex, real-world environments.