Knowledge acquisition and learning in unstructured robotic assembly environments

Author(s): LopezJuarez, I. | Howarth, M. |

Year: 2002

Citation: INFORMATION SCIENCES Volume: 145 Issue: 1-2 Pages: 89-111

Abstract: Mechanical assembly by robots has traditionally depended on simple sensing systems and the robot manufacturers programming language. However, this restricts the use of robots in complex manufacturing operations. An alternative to robot programming is the creation of self-adaptive robots based on the adaptive resonance theory (ART) artificial neural network (ANN). The research presented in this paper shows how robots can operate autonomously in unstructured environments. This is achieved by providing the robot with a primitive knowledge base (PKB) of the environment. This knowledge is gradually enhanced on-line based on the contact force information acquired during operations. The robot resembles a blindfold person performing the same task since no information is provided about the localisation of the fixed assembly component. The design of a novel neural network controller (NNC) based on the Fuzzy ARTMAP network and its implementation results on an industrial robot are presented, which validate the approach.

Topics: Robotics, Models: Fuzzy ARTMAP,

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