Jingtao Qi

National University of Defense Technology

Papers

Total Citations

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About

Jingtao Qi is a rising researcher in swarm robotics and collective intelligence, with a focus on enabling adaptive, decentralized behaviors for unmanned systems in the Internet of Things (IoT). His work bridges visual perception, clustering algorithms, and anti-flocking frameworks to solve real-world coordination challenges. In his highly cited 2023 paper, Qi introduced a model for the emergence of adaptive collective behavior based on visual perception, addressing a critical gap in classical swarm models that rely on velocity and position data. This work has already garnered 19 citations, reflecting its timely impact on the field. His 2022 study on area coverage in swarm robotics proposed a novel anti-flocking framework integrated with K-means dynamical clustering, achieving higher coverage rates with reduced time costs—a contribution with direct applications in surveillance, search and rescue, and material transport. By tackling the tension between exploration and cohesion in robot swarms, Qi is shaping the next generation of autonomous, perception-driven collective systems. His research is essential reading for anyone interested in the intersection of robotics, IoT, and adaptive multi-agent systems.