Ming Bai
Papers
1
Total Citations
3
H-Index
1
About
Ming Bai is a robotics researcher whose work focuses on advancing the safety and autonomy of collaborative robotic systems. Their primary research areas include dynamic obstacle avoidance, trajectory optimization, and real-time collision detection for human-robot interaction. Bai’s most cited paper, "A dynamic obstacle avoidance method for collaborative robots based on trajectory optimization" (2023), addresses a critical limitation in current sensor-driven planning algorithms: while high-resolution distance sensors provide rich environmental data, they often constrain a robot’s ability to react swiftly to moving obstacles. By integrating trajectory optimization techniques, Bai’s method enables robots to dynamically replan paths without sacrificing performance, enhancing both safety and efficiency in shared workspaces. This contribution is particularly valuable for industrial settings where humans and robots must operate in close proximity. With 3 citations already, this work is gaining traction among researchers in robot motion planning and human-robot collaboration. Bai’s research represents a meaningful step toward more adaptive, intelligent robotic systems that can navigate unpredictable environments, making their work highly relevant for students and engineers interested in the future of collaborative robotics.
Research Focus
Key Achievements
Top Papers
- 1