Abstract
In the modern field of biomedical robotics, there is increasing attention on the use of soft robots as carriers for surgical devices. Compared to rigid robots, soft robots exhibit more complex postures and work environments, requiring new methods for recognizing and controlling their postures within biological organisms. This paper investigates the recognition and control of the posture of soft robots using a neural network learning approach, utilizing a soft robot equipped with resistive sensors. By establishing the relationship between changes in resistance values and posture variations, the study successfully achieves the identification and control of the soft robot's posture. The obtained posture data based on resistance values are validated for reliability. Thus, by combining resistive sensors with soft robots and employing neural network analysis, the recognition and control of soft robots postures within biological organisms can be achieved.
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