Papers
21
Total Citations
130
H-Index
6
About
Xinjun Mao is a researcher specializing in autonomous robotics, swarm intelligence, and robot software engineering, with a particular focus on bridging software architecture principles with the practical demands of real-world robotic systems. His work addresses some of the most pressing challenges in developing autonomous robot software, including adaptive planning, dynamic task allocation, and robust execution in unpredictable environments. Among his most influential contributions is his hybrid software architecture and multi-agent framework for autonomous robots, which tackles the critical need for reactive and adaptive capabilities in dynamic environments, accumulating 21 citations. His research on swarm robotics has advanced spatial formation strategies and task allocation methods, including an innovative application of optimal mass transport theory to improve efficiency in large-scale swarm systems. His survey on robot programming languages has served as a valuable reference for both researchers and practitioners navigating the evolving landscape of robotic software development. More recently, Mao has extended behavior trees as a formalism for robotic decision-making in partially observable environments, reflecting his sustained commitment to principled software engineering for autonomous systems. Collectively, his body of work positions him as a meaningful contributor to the emerging field of software engineering for autonomous robots, a discipline he has helped define and advance.
Research Focus
Key Achievements
Top Papers
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- 2The Roadmap and Challenges of Robot Programming Languages15 citations · 2015
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