The integration of robots into shared workspaces alongside humans is the basis of Human-Robot Collaboration (HRC). This field of research has changed the paradigm of the industrial context, making HRC of pivotal importance for both researchers and the industry. In this context, a suitable task scheduling and trajectory planning strategy are crucial to achieve good performances and create a synergy between the two actors. Indeed, the task scheduling should be able to optimally distribute the tasks between the actors and recover from possible failures, i.e. by rescheduling the tasks. The trajectory planning strategy must comply with the safety standards that impose a reduction of velocity based on human behaviour. To this end, the monitoring system must also be safe-certified; otherwise, safety cannot be guaranteed. This paper proposes a novel architecture that integrates a dynamic task scheduling module with a dynamic trajectory planning module that explicitly considers ISO/TS 15066. For this purpose, the framework exploits a secure and certified monitoring system capable of tracking the human operator even in case of occlusions. The overall platform has been extensively validated both in a real and complex industrial scenario within the context of the ROSSINI EU project, where a dual-arm mobile robot collaborates with a human operator in an automatic machine-tending operation, and in a mock-up scenario.

In this work, we present a vision system particularly suited to the automatic recognition of reels in the field of automatic packaging machines. The output of the vision system is used to guide the autonomous grasping of the reels by a robot for a subsequent manipulation task. The proposed solution is built around three different methods to solve the ellipse-detection problem in an image. Such methods leverage standard image processing and mathematical algorithms, which are tailored to the targeted application. An experimental campaign demonstrates the efficacy of the proposed approach, even in the presence of low computational power and limited hardware resources, as in the use-case at hand.

In the pharmaceutical industry, bins need to be cleaned up to a critical level because the products that they contain are often incompatible with each other, and their mixture can facilitate the formation of bacterial fauna. In this work, a strategy is presented to fully automatize the procedure of cleanliness quality inspection of a pharmaceutical bin through a robotic arm and the use of both traditional and artificial-intelligence-based computer-vision techniques. An autonomous mobile robot is used to mimic the approach of a manipulator to a bin inserted in a washing cabin with an uncertain position. The manipulator is equipped with an eye-on-hand color camera and it carries out the binary classification of the bin surface status (e.g. clean vs dirty) through a convolutional neural network based on ResNet. The viewpoints from which the images are taken are the result of an optimization that, starting from the digital three-dimensional model of the bin and exploiting a virtual-twin-based planning scene, minimizes their number while maximizing the visible area of the bin from the current location of the robot. The results of this optimization are used to set up a pipeline that is entirely bin-independent. The same procedure may also be employed to generate the best washing trajectories to be performed by the cleaning robot, by simply replacing the inspection camera mounted on the robot end-effector with a washing nozzle. Though a complete tuning session is still required, preliminary experimental results are very promising, reaching a classifier accuracy (namely a capability of distinguishing clean and dirty surfaces) of 98% on conditioned data, showing that this work has the potential of becoming an effective and versatile industrial product.