The Application of Machine Learning Technique to the Detection and Management of Soft Robots
Keywords:
Soft robots, management, Boosted Central Forced Convolutional Neural Network (BCF-CNN)Abstract
A rapidly developing area of robotics called "soft robotics" provides special benefits including compliance, flexibility, and secure human connection. The detection and control of their complicated and nonlinear dynamics are difficult, nevertheless. Because inherent safety is built into soft robots at the material level, there is interest in using them in practical applications. These manipulative robots use flexible materials that may alter shape and behavior and allow for conformable physical touch. However, the addition of soft and flexible materials to robotic systems brings several obstacles to sensor integration, such as multimodal sensing capable of stretching, the embedding of high-resolution yet large-area sensor arrays, and sensor fusion with a growing amount of data. To address these issues, this research suggests a machine-learning strategy for the detection and management of soft robots. Sampling for force modeling and kinematics The kinematic model and the force model were both learned using data that were collected, and the data were then preprocessed using z-score normalization. Then, we proposed Boosted Central Forced Convolutional Neural Network (BCF-CNN) for data clustering and detection of soft robots. And the results of the experiment demonstrate that our recommended methodology works better than the other available approaches.
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