Empowered Optical Inspection by Using Robotic Manipulator in Industrial Applications Nowadays the inspection of products at the end of line represents a critical phase. At this stage, it is necessary to look for defects in order to prevent the quality check to fail, and to provide information for improving the production as well. This task can be performed by using several sensing technologies, and the contactless optical inspection plays a key role. In this regard, the use of advanced robotic manipulators offers the capability to change the viewpoint of a given object and to inspect its multiple faces. We propose an approach that combines the use of photometric stereo to derive a 3D model of objects, empowered by the superresolution that is applied on the original dataset (upstream) or on the normal images (downstream) in order to increase the quality of the final 3D model. The vision system is mounted on a robotic manipulator, able to grasp and change the viewpoint, thus offering a more complete view of the object to be inspected. The obtained results show that the developed solution increases the quality of the derived 3D models used for inspection tasks on different faces of the objects; this is achieved by using the manipulation ability offered by the adopted robotic platform.

Binpicking of Randomly Piled Shiny Industrial Objects Using Light Transport Matrix Estimation * The use of robots in automated factories requires accurate binpicking to ensure that objects are correctly identified and selected. In the case of objects with multiple reflections from their surfaces, this is a challenging task. We attempted to address this problem by developing a 3D measurement method based on a Light Transport Matrix (LTM), which can be applied to shiny objects or semitransparent objects. The study presented herein evaluates the accuracy of the proposed method as well as the method for 3D pose estimation we previously reported, by examining a binpicking task, which is a wellknown robot application in factory automation. There is considerable demand for automated binpicking systems for general objects. However, in the use of costeffective measurement systems, some objects such as shiny metallic objects continue to prove problematic in terms of binpicking because of the difficulty to measure their shapes accurately. Our 3D measurement method uses only a projectorcamera system; thus, it is costeffective, and it does not require any special optical system. It is based on fast LTM sparse estimation. We previously demonstrated that this approach can measure the 3D shape of metallic objects and showed that our pose estimation method is applicable to binpicking. However, we did not verify its accuracy with the application of 3D robot vision. In this study, we integrate these two methods, and demonstrate that our 3D measurement method, in combination with our pose estimation work, can successfully accomplish binpicking tasks involving shiny metallic industrial objects. Ultimately, we achieved 100 % picking success for 5 scenes including 15 pieces. We concluded that our proposed methods are sufficiently accurate to carry out binpicking tasks in automated factory environments.