Jingguo Zhang

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About

Jingguo Zhang is a researcher specializing in underwater robotics and autonomous navigation systems, with a particular focus on real-time obstacle avoidance and path planning. His work addresses critical challenges in subsea environments, where traditional algorithms often falter due to dynamic obstacles and communication constraints. Zhang’s most-cited paper, “The underwater obstacle avoidance method based on ROS” (2023), introduces an artificial potential field approach tailored for underwater robots, mitigating issues like excessive attractive forces when far from targets. This contribution has garnered 2 citations, reflecting its emerging impact in the field. Beyond this, Zhang’s research integrates Robot Operating System (ROS) frameworks to enhance practical deployment, bridging theoretical algorithms with real-world applications. His work is notable for advancing the reliability of autonomous underwater vehicles (AUVs) in complex, unstructured environments—a key step toward safer marine exploration, inspection, and environmental monitoring. For students and researchers, Zhang’s contributions offer a foundation for developing more adaptive and robust underwater navigation systems, highlighting the ongoing evolution of robotics in challenging aquatic domains.