Intelligent Task Level Planning for Robotic Assembly: Issues and Experiments

Today’s industrial robots use programming languages that do not allow learning and task knowledge acquisition and probably this is one of the reasons of its restricted used for complex task in unstructured environments. In this paper, results on the implementation of a novel task planner using a 6 DOF industrial robot as an alternative to overcome this limitation are presented. Different Artificial Neural Networks (ANN) models were assessed first to evaluate their learning capabilities, stability and feasibility of implementation in the planner. Simulations showed that the Adaptive Resonance Theory (ART) outperformed other connectionist models during tests and therefore this model was chosen. This work describes initial results on the implementation of the planner showing that the manipulator can acquire manipulative skills to assemble mechanical components using only few clues.