Conventional methods for robot system integration mainly include using demonstration boards, force traction, or virtual space simulation. However, these methods have some limitations. They cannot be applied to different robotic arm hardware devices and are not suitable for production environments with varying variables. If the robotic arm hardware is changed or the object of production operation is modified, the previous methods cannot be used anymore. To accommodate different hardware devices, production conditions, and objects, flexibility is required in the robot system integration method.
Wei-Wei Wan, associate professor of Graduate School of Engineering Science, Osaka University , has arranged 16 NOKOV motion capture cameras in the Robotics Laboratory to capture data of the position of the experimenter and the robotic arm. By utilizing the motion capture system, human movement data can be transmitted to the collaborative robots for flexible collaboration.
Through this method, collaborative robots can acquire real-time information on human movement and posture, allowing them to instantly imitate and collaborate with human experimenters. The teaching-learning method using the motion capture system is applicable to different robotic arm hardware devices and can be used in production environments with variable factors, enabling robots to operate flexibly.
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