Wantana Sukmanee
Papers
1
Total Citations
6
H-Index
1
About
Dr. Wantana Sukmanee is a robotics researcher whose work focuses on sensor-based manipulation and environmental perception for robotic systems. Her key research areas include depth sensing, obstacle modeling, and calibration techniques for manipulators. Her most notable contribution, the 2012 paper "Obstacle modeling for manipulator using iterative least square (ILS) and iterative closest point (ICP) base on Kinect," introduces a novel method to distinguish a robotic manipulator from its surroundings using a Kinect depth sensor. This work addresses critical challenges in robotic workspace safety and autonomy by solving coordinate calibration between the sensor and manipulator through an iterative least square algorithm. With 6 citations, this foundational paper has influenced subsequent research in sensor-guided manipulation and obstacle avoidance. Dr. Sukmanee's work demonstrates the practical application of combining computer vision and robotics to enhance manipulator awareness in dynamic environments, making her contributions valuable for students and researchers exploring affordable depth-sensing solutions for robotic systems.
Research Focus
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Top Papers
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