Collaborative and Cooperative Robotics Applications using Visual Perception

The objective of this Thesis is to develop novel integrated strategies for collaborative and cooperative robotic applications. Commonly, industrial robots operate in structured environments and in work-cell separated from human operators. Nowadays, collaborative robots have the capacity of sharing the workspace and collaborate with humans or other robots to perform complex tasks. These robots often operate in an unstructured environment, whereby they need sensors and algorithms to get information about environment changes. Advanced vision and control techniques have been analyzed to evaluate their performance and their applicability to industrial tasks. Then, some selected techniques have been applied for the first time to an industrial context. A Peg-in-Hole task has been chosen as first case study, since it has been extensively studied but still remains challenging: it requires accuracy both in the determination of the hole poses and in the robot positioning. Two solutions have been developed and tested. Experimental results have been discussed to highlight the advantages and disadvantages of each technique. Grasping partially known objects in unstructured environments is one of the most challenging issues in robotics. It is a complex task and requires to address multiple subproblems, in order to be accomplished, including object localization and grasp pose detection. Also for this class of issues some vision techniques have been analyzed. One of these has been adapted to be used in industrial scenarios. Moreover, as a second case study, a robot-to-robot object handover task in a partially structured environment and in the absence of explicit communication between the robots has been developed and validated. Finally, the two case studies have been integrated in two real industrial setups to demonstrate the applicability of the strategies to solving industrial problems

Grasping of Solid Industrial Objects Using 3D Registration

Robots allow industrial manufacturers to speed up production and to increase the product’s quality. This paper deals with the grasping of partially known industrial objects in an unstructured environment. The proposed approach consists of two main steps: (1) the generation of an object model, using multiple point clouds acquired by a depth camera from different points of view; (2) the alignment of the generated model with the current view of the object in order to detect the grasping pose. More specifically, the model is obtained by merging different point clouds with a registration procedure based on the iterative closest point (ICP) algorithm. Then, a grasping pose is placed on the model. Such a procedure only needs to be executed once, and it works even in the presence of objects only partially known or when a CAD model is not available. Finally, the current object view is aligned to the model and the final grasping pose is estimated. Quantitative experiments using a robot manipulator and three different real-world industrial objects were conducted to demonstrate the effectiveness of the proposed approach

Selective Grasping for Complex-Shaped Parts Using Topological Skeleton Extraction

To enhance the autonomy and flexibility of robotic systems, a crucial role is played by the capacity to perceive and grasp objects. More in detail, robot manipulators must detect the presence of the objects within their workspace, identify the grasping point, and compute a trajectory for approaching the objects with a pose of the end-effector suitable for performing the task. These can be challenging tasks in the presence of complex geometries, where multiple grasping-point candidates can be detected. In this paper, we present a novel approach for dealing with complex-shaped automotive parts consisting of a deep-learning-based method for topological skeleton extraction and an active grasping pose selection mechanism. In particular, we use a modified version of the well-known Lightweight OpenPose algorithm to estimate the topological skeleton of real-world automotive parts. The estimated skeleton is used to select the best grasping pose for the object at hand. Our approach is designed to be more computationally efficient with respect to other existing grasping pose detection methods. Quantitative experiments conducted with a 7 DoF manipulator on different real-world automotive components demonstrate the effectiveness of the proposed approach with a success rate of 87.04%

Vision-enhanced Peg-in-Hole for automotive body parts using semantic image segmentation and object detection

Artificial Intelligence (AI) is an enabling technology in the context of Industry 4.0. In particular, the automotive sector is among those who can benefit most of the use of AI in conjunction with advanced vision techniques. The scope of this work is to integrate deep learning algorithms in an industrial scenario involving a robotic Peg-in-Hole task. More in detail, we focus on a scenario where a human operator manually positions a carbon fiber automotive part in the workspace of a 7 Degrees of Freedom (DOF) manipulator. To cope with the uncertainty on the relative position between the robot and the workpiece, we adopt a three stage strategy. The first stage concerns the Three-Dimensional (3D) reconstruction of the workpiece using a registration algorithm based on the Iterative Closest Point (ICP) paradigm. Such a procedure is integrated with a semantic image segmentation neural network, which is in charge of removing the background of the scene to improve the registration. The adoption of such network allows to reduce the registration time of about 28.8%. In the second stage, the reconstructed surface is compared with a Computer Aided Design (CAD) model of the workpiece to locate the holes and their axes. In this stage, the adoption of a Convolutional Neural Network (CNN) allows to improve the holes’ position estimation of about 57.3%. The third stage concerns the insertion of the peg by implementing a search phase to handle the remaining estimation errors. Also in this case, the use of the CNN reduces the search phase duration of about 71.3%. Quantitative experiments, including a comparison with a previous approach without both the segmentation network and the CNN, have been conducted in a realistic scenario. The results show the effectiveness of the proposed approach and how the integration of AI techniques improves the success rate from 84.5% to 99.0%

The automotive industry: when regulated supply fails to meet demand. The case of Italy

This article studies the effects of the latest European regulations on carbon emissions on the Italian car market and discusses the possibility of achieving climate neutrality of road transport through the "mere" replacement of cars currently on the road with new zeroemission cars. Since 2016, au-tomakers' production strategies have changed dramatically, with an increasing number of zero (and low) emission models on car lists. To date, these changes on the supply side have not been matched by similar changes in purchasing habits. In recent years, not only have few zero (and low) emission cars been sold, but also few new cars. Unless epoch-making changes occur, it is com-pletely unrealistic to think that we can achieve climate neutrality by 2050 by leveraging exclusively on the renewal of the fleet

The automotive industry: when regulated supply fails to meet demand. The Case of Italy

This paper studies the effects of the latest European regulations on carbon emissions on the Italian car market and discusses the possibility of achieving climate neutrality of road transport through the “mere” replacement of cars currently on the road with new zero-emission cars. Since 2016, automakers’ production strategies have changed dramatically, with an increasing number of zero (and low) emission models on car lists. To date, these changes on the supply side have not been matched by similar changes in purchasing habits. In recent years, not only have few zero (and low) emission cars been sold, but also few new cars. Unless epoch-making changes occur, it is completely unrealistic to think that we can achieve climate neutrality by 2050 by leveraging exclusively on the renewal of the fleet

Gas storage services and regulation in Italy: A Delphi analysis

The objective of this paper is to assess to which extent gas market inefficiencies, such as weak competition, import dependence and lack of flexibility tools, affect operation and usage of storage services in Italy in the aftermath of the EU liberalization process. The analysis is supported by the empirical results of a Delphi survey that we have conducted to investigate storage service provision and regulation in Italy. We argue that the Italian storage sector is at a crossroads. The policy-driven phase of liberalization is ending and the market-driven phase has just begun. The former phase has granted fair access to storage, narrowed the likelihood of strategic behaviour by the incumbent and secured residential users against supply disruptions, but it has proved dynamically inefficient. Cost-reflective tariffs and low penalties for unbalances have both lowered incentives to expand the range of flexibility tools and penalized industrial customers demand. The market-driven phase has just started. The expected increase in working capacity and the entry of newcomers in the authorization process for new facilities are a progress towards the commercial use of storage. To this end, however, a further change in gas market design is needed: the creation of a well functioning spot market.Italian gas market Storage Delphi survey

Final Remarks and Policy Recommendations

We discuss the main results of the models presented in the book and draw some policy lesson

Gas storage services and regulation in Italy:A Delphi analysis

The objective of this paper is to assess to which extent gas market inefficiencies, such as weak competition, import dependence and lack of flexibility tools, affect operation and usage of storage services in Italy in the aftermath of the EU liberalization process