Papers
4
Total Citations
55
H-Index
4
About
Yingcang Ma is a leading researcher in multi-robot systems and modular manipulator control, whose work bridges theoretical coordination strategies with practical robotic applications. His primary research areas include multi-robot coordinated hunting, dynamic task allocation, and obstacle avoidance for super redundant manipulators. Ma’s most influential contribution is a multi-robot coordinated hunting strategy with dynamic alliance (2009, 27 citations), which addresses the challenge of tracking multiple evaders by enabling robots to form flexible, real-time alliances—a significant advance for military and security applications. He also developed a hierarchical task allocation method based on task case matching (2008, 7 citations), combining contract net and acquaintance net protocols to improve efficiency in complex multi-robot missions. In more recent work, Ma has focused on super redundant modular manipulators, proposing a Bezier curve and particle swarm optimization approach for obstacle avoidance and multitarget tracking (2020, 17 citations). This work enables slender, serpentine robots to navigate narrow spaces and perform precise manipulations, with applications in hazardous environments and industrial automation. With over 55 total citations, Ma’s research continues to shape intelligent robotics, offering scalable solutions for coordinated and adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1A multi-robot coordinated hunting strategy with dynamic alliance27 citations · 2009
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