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About

Jianqi Gao is a robotics and artificial intelligence researcher whose work spans multi-agent pathfinding, autonomous robot navigation, and intelligent warehouse automation. His most influential contribution is a comprehensive review of graph-based multi-agent pathfinding (MAPF) solvers, which has garnered 34 citations and has become a key reference for researchers navigating the classical and emerging landscape of multi-robot coordination. Complementing this, his development of a two-objective integer linear programming model for multi-robot task assignment in intelligent warehouses — cited 18 times — demonstrates a strong ability to bridge theoretical optimization with real-world logistics applications. Gao's research increasingly incorporates deep reinforcement learning to tackle complex navigation challenges. His work on priority-aware communication for distributed MAPF, safe mapless navigation using constrained reinforcement learning, and sensor-fusion-based mobile robot navigation collectively reflect a commitment to making autonomous systems both scalable and safety-conscious. His more recent hierarchical reinforcement learning framework addresses the persistent challenge of local minima in indoor environments, pushing the boundaries of practical deployment. With a publication record spanning classical combinatorial optimization to cutting-edge learning-based methods, Gao has established himself as a versatile contributor to the multi-robot systems community, making his work particularly valuable for students and researchers working at the intersection of AI and robotics.