Abstract
We propose a neural network model generating a robot arm trajectory. The developed neural network model is based on a recurrent-type neural network (RNN) model calculating the proper arm trajectory based on data acquired by evaluation functions of human operations as the training data. A self-learning function has been added to the RNN model. The proposed method is applied to a 2-DOF robot arm, and laboratory experiments were executed to show the effectiveness of the proposed method. Through experiments, it is verified that the proposed model can reproduce the arm trajectory generated by a human. Further, the trajectory of a robot arm is successfully modified to avoid collisions with obstacles by a self-learning function.
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This work was presented, in part, at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004
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Imajo, S., Konishi, M., Nishi, T. et al. Application of a neural network to the generation of a robot arm trajectory. Artif Life Robotics 9, 107–111 (2005). https://doi.org/10.1007/s10015-004-0334-4
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DOI: https://doi.org/10.1007/s10015-004-0334-4