Hybrid collision detection perceptron of the robot in the fusion application

摘要

In a complicated fusion device, the robot is at risk of colliding with its surroundings when it moves. In this article, a hybrid collision detection perceptron is studied to ensure the safety for both the fusion device and the robot. Primary component analysis is adopted as a preprocessor for extracting the features of the data. The stochastic gradient descent, Adam and Adagrad are used as classifiers with different learning rate methods. From the simulation results, it proves that this hybrid perceptron is more valid than the traditional detection method based on the dynamic model, and it reduces the complexity of robot modeling.