Tool to pick objects out of a bin using qualitative-vision and part-reflectance properties

Marcello Ricotti

doi:10.1117/12.141788

Abstract

A new approach to the quasi specular object picking task is described. Purposive and qualitative vision is used to detect the object to be picked. Surface reflectance properties and appropriate lighting directions are used to estimate the surface orientation. Extrinsic camera calibration parameters are used to compute the part attitude in a world reference system. A pneumatic gripper with proximity sensors on its extremities is used to effectively grasp the part. The acquisition sequence is driven by prior knowledge about the object to be grasped as well as by the reflectance properties of its surface. The purposive picking algorithm does not perform any 3-D reconstruction of the scene, but identifies the part to be picked as the part which is not occluded by any other part. Due to the purposive nature of the method no 3-D scene nor 3-D part modeling is required. Our method is characterized by having low sensitivity to image noise and being robust and applicable to a wide range of industrial plastic and metal objects whose bidirectional reflectance distribution function (BDRF) approximates an ideal specular reflector. Test results using a real framework with metallic hexagonal rods are described. Experiments with cylindrical, square, and generic polygon base rods are in process giving encouraging results.

Citation Download Citation

Marcello Ricotti "Tool to pick objects out of a bin using qualitative-vision and part-reflectance properties", Proc. SPIE 1964, Applications of Artificial Intelligence 1993: Machine Vision and Robotics, (11 March 1993); doi: 10.1117/12.141788; https://doi.org/10.1117/12.141788

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