Abstract
Industrial manufacturing cells involving fixtureless environments require more efficient methods to achieve assembly tasks. This paper introduces an approach for Robotic Fixtureless Assembly (RFA). The approach is based on the Fuzzy ARTMAP neural network and learning strategies to acquire the skill from scratch without knowledge about the assembly system. The vision system provides the necessary information to accomplish the assembly task such as pose, orientation and type of component. Different ad-hoc input vectors were used as input to the assembly and the vision systems through several experiments which are described. The paper also describes the task knowledge acquisition and the followed strategies to solve the problem of automating the peg-in-hole assembly using 2D images. The approach is validated through experimental work using an industrial robot.
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Corona-Castuera, J., Rios-Cabrera, R., Lopez-Juarez, I., Peña-Cabrera, M. (2005). An Approach for Intelligent Fixtureless Assembly: Issues and Experiments. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_107
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DOI: https://doi.org/10.1007/11579427_107
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