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Qirong Tang is a prominent robotics researcher whose work spans swarm intelligence, space robotics, underwater systems, and multi-robot cooperation. Over more than a decade, Tang has made foundational contributions to the field of autonomous robotic systems, addressing some of the most complex challenges in robot coordination, navigation, and manipulation. Tang's early work established influential frameworks for mobile robot behavior, including a widely adopted behavior-based fuzzy control method for navigation in unknown environments (2013, 43 citations) and pioneering applications of Particle Swarm Optimization (PSO) to swarm robotics (2011). These foundations evolved into sophisticated multi-target search strategies and stigmergy-based dynamic tracking systems, collectively demonstrating Tang's sustained leadership in swarm intelligence. His research has expanded boldly into frontier domains: a comprehensive survey on space robotic technologies for on-orbit assembly (2022, 98 citations) and a thorough review of underwater autonomous perception and grasping (2019, 96 citations) rank among his most impactful works, reflecting remarkable breadth. More recently, Tang has pioneered deep reinforcement learning frameworks for dual-arm manipulation (2024, 46 citations) and distributed cooperative transportation schemes (2020, 48 citations). With hundreds of cumulative citations, Tang's contributions have meaningfully shaped modern robotics research across terrestrial, aquatic, and space environments.