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About

Qingyong Jia is a researcher specializing in multi-robot systems and cooperative autonomy for underwater environments. His work focuses on developing intelligent coordination strategies for multiple underwater robots, addressing critical challenges in swarm operations, area search, and pursuit-evasion scenarios. Jia's most impactful contribution, "Research on cooperative area search of multiple underwater robots based on the prediction of initial target information" (2019), has garnered 26 citations, demonstrating its significance in advancing autonomous underwater search methodologies. He also proposed a modular miniature underwater robot design scheme for swarm operations (2016), introducing a flexible hardware and software architecture that enables scalable, reconfigurable robot teams. In his pursuit strategy research, Jia developed a novel cooperative pursuit approach using rapidly-exploring random tree (RRT) search algorithms, effectively modeling pursuit-evasion games in two stages to improve target interception efficiency. His work bridges theoretical game theory with practical robotic applications, offering valuable insights for underwater surveillance, environmental monitoring, and search-and-rescue missions. Jia's contributions are particularly notable for their focus on real-world deployability, emphasizing modularity and scalability in underwater swarm systems.