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About

Kangjie Zhou is a rising researcher at the forefront of swarm robotics and autonomous navigation, with a focus on developing scalable, safe, and intelligent motion planning algorithms for multi-agent systems. His work addresses fundamental challenges in target tracking, where he pioneered probabilistic visibility-aware trajectory planning to maintain target visibility in cluttered environments—a critical capability for both civilian and military applications. Zhou's contributions to swarm robotics include SwarmPRM, a probabilistic roadmap method that overcomes the traditional trade-off between scalability and solution quality in large-scale systems, and SwarmDiff, a novel diffusion transformer-based framework that achieves efficient, collision-free trajectory generation in obstacle-dense settings. His research also extends to bio-inspired underwater sensing, where he developed artificial lateral line systems for azimuth estimation in fish-like robots. With over 20 citations across his most-cited works, Zhou's innovations are already shaping the next generation of autonomous systems, offering practical solutions for complex, real-world environments. His work stands out for its blend of theoretical rigor and applied impact, making him a notable figure in modern robotics.