For many years, the manufacturing industry has pursued a commercially viable, vision-guided, robotic bin-picking system. The goal of such a system is to select a target part and a corresponding grasp from a pile of jumbled parts. Strategic planning of this selection to reduce the risk of a failed grasp attempt would increase the system’s reliability, and, thus, its commercial viability, and is the focus of this thesis. Specifically, this work aims to find the best pick;​ namely, the best combination of a target part and corresponding grasp.

The primary contribution of this work is a novel method for generating many high-quality, rated, pick options for a given vision-guided robotic binpicking cycle, enabling the selection of the best pick. The method is tailored for a two-fingered (antipodal) gripper, typically used in industry;​ however, it may be extended to other gripper types (i.e., three-fingered). The method is broken down into two stages:​ (1) offline generation of many high-quality two-fingered grasps for a given part, and (2) online evaluation of these grasps in the context of the pile to determine a collision-free set of rated picks, and, ultimately, the most desirable pick. In evaluating grasps online, the effect of gripper finger clearance is considered to further minimize the risk of collision when executing the selected pick.

Subsidiary contributions of this work include:​ (1) an automatic graspgeneration method to sample the space of all two-fingered grasps for the target part, (2) a metric function for evaluating grasps, and (3) a measure of the robustness of a grasp.

The proposed method for pick selection is validated using stereo data of a real pile of parts. We compare the use of a small set of nominal grasps for pick selection (an approach typical in industry) to the use of an extensive evaluated grasp set generated using the proposed method. Our experimental results show that, in the majority of cases, the use of our method results in more valid and higher quality picks.