Papers
7
Total Citations
43
H-Index
4
About
Sile Ma is a robotics researcher whose work spans multi-robot systems, autonomous navigation, and intelligent task allocation. With a career arc stretching from foundational contributions in stereo vision-based obstacle avoidance to cutting-edge deep reinforcement learning approaches, Ma has consistently pushed the boundaries of what autonomous robotic systems can achieve. Ma's early work, including two complementary 2010 papers on binocular stereo vision for mobile robot navigation (totaling 10 citations), established a practical framework for 3D terrain recognition and autonomous obstacle avoidance. These contributions laid important groundwork for real-world robot deployment. More recently, Ma's focus has shifted toward the complex challenge of multi-robot task allocation (MRTA), producing two highly regarded papers: a 2022 study employing Lin–Kernighan–Helsgott-guided evolutionary algorithms to simultaneously minimize energy consumption and task completion time (12 citations), and a 2024 investigation leveraging graph deep reinforcement learning with graph normalization to enable scalable MRTA solutions (5 citations). Additional contributions include wearable sensor network attitude configuration using beetle antennae search strategies and a cost-effective ArUco-based indoor positioning system. Collectively, Ma's portfolio reflects a researcher dedicated to bridging theoretical optimization with practical, deployable robotic intelligence.
Research Focus
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Top Papers
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- 7Design Method of Robot Welding Workstation Based on Adaptive Planing2 citations · 2020