Cheng Gong
Papers
2
Total Citations
14
H-Index
2
About
Cheng Gong is a researcher in robotics and autonomous systems, with a focus on multi-agent coordination and mobile robot task planning. His work addresses critical challenges in enabling effective decision-making for unmanned systems operating in complex, dynamic environments. In his highly cited 2020 paper, Gong proposed a collaborative decision-making approach for multi-unmanned combat vehicles using behaviour trees, a framework that has garnered 11 citations for its practical utility in coordinating multiple agents under multi-task conditions. He further advanced the field with his 2021 work on orientation-aware planning for omni-directional mobile robots (OMRs), demonstrating how the extra degree of freedom in OMRs can be exploited for parallel task execution—a contribution that, while newer, signals a promising direction for enhancing robot efficiency in industrial and academic settings. Gong’s research bridges theoretical planning algorithms with real-world robotic applications, offering scalable solutions for defense, logistics, and automation. His work is particularly notable for integrating behaviour tree structures into multi-vehicle systems, a relatively underexplored area that has significant implications for autonomous swarms. As a rising voice in robotics, Gong continues to push the boundaries of how unmanned systems perceive, decide, and act collaboratively.
Research Focus
Key Achievements
Top Papers
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- 2