Abstract
A non-redundant manipulator inverted kinematics can be easily solved by a multilayer perceptron neural network. For redundant manipulators, the inverted function cannot exist. Many advanced types of neural networks have been used at least for kinematic and dynamic control. This article describes a solution, when the redundancy is compensated by a simple quality function, which serves at the same time as a solution of the obstacle avoidance problem. This additional function is not combined with the functions describing the manipulator forward kinematics, but is applied to the data, prepared for the network training. This makes the whole process much simpler to realize, although the preparation of data for the training is computationally demanding.
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Hlaváč, V. (2022). MLP Neural Network for a Kinematic Control of a Redundant Planar Manipulator. In: Beran, J., Bílek, M., Václavík, M., Žabka, P. (eds) Advances in Mechanism Design III. TMM 2020. Mechanisms and Machine Science, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-030-83594-1_3
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DOI: https://doi.org/10.1007/978-3-030-83594-1_3
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