Xiangda Yan

National University of Defense Technology

Papers

Total Citations

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About

Xiangda Yan is a researcher advancing the frontier of autonomous multi-robot systems, with a primary focus on cooperative exploration, task allocation, and deep reinforcement learning for robotic mapping. In his highly cited 2023 work on multi-robot cooperative autonomous exploration via task allocation, Yan addresses the fundamental challenge of enabling robot teams to cover unknown terrestrial environments more efficiently than a single agent—demonstrating how strategic task distribution can dramatically reduce exploration time and path length. This work has already garnered 13 citations, signaling its growing influence in the field. Complementing this, Yan’s research on autonomous exploration through deep reinforcement learning tackles the computational bottlenecks of LiDAR-based mapping in large-scale, complex environments. By proposing a hybrid exploration model that integrates learning-based decision-making with traditional mapping algorithms, he offers a path toward scalable, real-time autonomous navigation. Yan’s contributions are particularly relevant for applications in search-and-rescue, planetary exploration, and industrial automation, where robust, efficient multi-robot coordination is critical. His work stands at the intersection of robotics, artificial intelligence, and systems engineering, making him a promising voice in the next generation of autonomous systems research.