This work is focused on the use of a CZT detector for a radiation mapping with an industrial robotic arm. Measurements were carried out within the RadioRoSo experiment (Radioactive Waste Robotic Sorter), under the umbrella of EU FP7 project ECHORD++. In tests with a dual-arm robot and standard point sources of 137Cs and 60Co, a Magnox waste was mimicked. Thereafter, for relevant measurement geometries and selected shielding materials, full energy peak efficiencies were calculated using the MCNP transport code. Simulated FEP efficiency values were used for minimum detectable activity assessments for expected measurement conditions. Obtained results would be also applicable for cases of shielded lost/orphan point-like sources.

Sorting of old and mixed nuclear waste is an essential process in nuclear decommissioning operations. The main bottleneck is manual picking and separation of the materials using remotely operated arms, which is slow and error prone especially with small items. Automation of the process is therefore desirable. In the framework of the newly funded European project ECHORD++, experiment RadioRoSo, a pilot robotic cell is being developed and validated against industrial requirements on a range of sorting tasks. Industrial robots, custom gripper, vision feedback and new manipulation skills will be developed. This paper presents application context, cell layout and sorting approach.

This work presents a complete pipeline for folding a pile of clothes using a dual-armed robot. This is a challenging task both from the viewpoint of machine vision and robotic manipulation. The presented pipeline is comprised of the following parts: isolating and picking up a single garment from a pile of crumpled garments, recognizing its category, unfolding the garment using a series of manipulations performed in the air, placing the garment roughly flat on a work table, spreading it, and, finally, folding it in several steps. The pile is segmented into separate garments using color and texture information, and the ideal grasping point is selected based on the features computed from a depth map. The recognition and unfolding of the hanging garment are performed in an active manner, utilizing the framework of active random forests to detect grasp points, while optimizing the robot actions. The spreading procedure is based on the detection of deformations of the garment's contour. The perception for folding employs fitting of polygonal models to the contour of the observed garment, both spread and already partially folded. We have conducted several experiments on the full pipeline producing very promising results. To our knowledge, this is the first work addressing the complete unfolding and folding pipeline on a variety of garments, including T-shirts, towels, and shorts.

This deliverable describes the second demonstrator of the CloPeMa project. Its purpose is to present the state of the project and to make a preliminary evaluation of the system performance and function. The demonstrator is focused on a presentation of the individual modules which have been integrated into one system and work side-byside. The modules share the same hardware, data, computational results and services to perform their tasks. Moreover, several modules which are supposed to be integrated in the third year are presented separately.

The work addresses the problem of clothing perception and manipulation by a two armed industrial robot aiming at a real-time automated folding of a piece of garment spread out on a flat surface. A complete solution combining vision sensing, garment segmentation and understanding, planning of the manipulation and its real execution on a robot is proposed. A new polygonal model of a garment is introduced. Fitting the model into a segmented garment contour is used to detect garment landmark points. It is shown how folded variants of the unfolded model can be derived automatically. Universality and usefulness of the model is demonstrated by its favorable performance within the whole folding procedure which is applicable to a variety of garments categories (towel, pants, shirt, etc.) and evaluated experimentally using the two armed robot. The principal novelty with respect to the state of the art is in the new garment polygonal model and its manipulation planning algorithm which leads to the speed up by two orders of magnitude.

This deliverable describes the first demonstrator of the CloPeMa project. Its purpose is to present the state of the project and to make a preliminary evaluation of the performance of the individual subsystems. The demonstrator shows the components of the system separately but a section is presented which demonstrates the first integration and interaction of at least some of the components.