In this paper, an innovative fuzzy logic approach for the robotic laying of fabrics on a work table and based on fuzzy sets is presented. Through handling experiments the solution domain for the path of the robotic gripper is determined, the handling parameters are identified and implicit knowledge is accumulated. Then a proper scheme for the data acquisition is formed and a path-planning algorithm based on fuzzy logic is developed. Due to conflicts and inaccuracies of the acquired data, a subtractive clustering algorithm is used, to identify the proper clusters for the two developed fuzzy systems, with the first employing the clusters as rules and the second a neuro-fuzzy system initialised by the implicit knowledge and trained via back-propagation. Finally, the effectiveness of the two path-planning systems is investigated in an experimental stage where the robot successfully places on a table fabrics of a variety of materials and sizes.
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