Papers
4
Total Citations
72
H-Index
4
About
Tenghai Qiu is a leading researcher at the forefront of intelligent multiagent systems, specializing in deep reinforcement learning (DRL) for autonomous robot coordination. His work tackles fundamental challenges in multirobot navigation, formation control, and target coverage within highly dynamic and uncertain environments. Qiu’s major contributions lie in bridging the gap between model-based paradigms and data-driven DRL, creating hybrid frameworks that generate collision-free, connectivity-guaranteed policies for robot teams. His most cited paper (2023, 29 citations) introduces a novel DRL approach combined with model-based methods for multiagent formation control with collision avoidance, addressing a critical bottleneck in collaborative navigation. He further advanced the field with a DRL-based multitarget coverage strategy ensuring connectivity (2022, 20 citations) and a graph attention network method for robot navigation among external autonomous agents (2020, 12 citations). Qiu’s knowledge-incorporated policy framework (2021, 11 citations) provides a distributed, efficient solution for multi-target coverage while maintaining team connectivity. With a growing citation impact and a focus on practical, scalable solutions for real-world multirobot systems, Qiu’s work is shaping the next generation of autonomous swarm intelligence.
Research Focus
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