An Xu

Tsinghua University, Northwestern Polytechnical University

Papers

Total Citations

H-Index

About

An Xu is a leading researcher in robotics and autonomous systems, with a primary focus on motion planning, multi-robot coordination, and imitation learning. His work addresses fundamental challenges in enabling robots to operate efficiently in complex, obstacle-filled environments. Xu’s major contributions include developing a deep imitation learning algorithm for obstacle avoidance, which allows mobile robots with limited computational resources to make optimal, real-time navigational decisions. This work, published in 2018, has garnered 41 citations and is recognized for its practical impact on resource-constrained robotics. In parallel, Xu has advanced the field of multi-robot coverage path planning. His 2018 paper introduces an ideal-shaped spanning tree approach, combined with an improved ant colony optimization algorithm, to achieve optimal coverage paths for multiple robots in areas containing obstacles. This highly cited work (37 citations) provides a scalable solution for applications such as search-and-rescue, environmental monitoring, and industrial inspection. Through these contributions, An Xu has established himself as a key innovator in creating computationally efficient, learning-based strategies for autonomous navigation and coordinated multi-robot tasks.