Vision-Based Solutions for Human-Robot Collaboration in Industrial Workcells

Industrial robots are both versatile and high performant, enabling the flexible automation typical of the modern Smart Factories. For safety reasons, however, they must be relegated inside closed fences and/or virtual safety barriers, to keep them strictly separated from human operators. This can be a limitation in some scenarios in which it is useful to combine the human cognitive skill with the accuracy and repeatability of a robot, or simply to allow a safe coexistence in a shared workspace. Collaborative robots (cobots), on the other hand, are intrinsically limited in speed and power in order to share workspace and tasks with human operators, and feature the very intuitive hand guiding programming method. Cobots, however, cannot compete with industrial robots in terms of performance, and are thus useful only in a limited niche, where they can actually bring an improvement in productivity and/or in the quality of the work thanks to their synergy with human operators. The limitations of both the pure industrial and the collaborative paradigms can be overcome by combining industrial robots with artificial vision. In particular, vision can be exploited for a real-time adjustment of the pre-programmed task-based robot trajectory, by means of the visual tracking of dynamic obstacles (e.g. human operators). This strategy allows the robot to modify its motion only when necessary, thus maintain a high level of productivity but at the same time increasing its versatility. Other than that, vision offers the possibility of more intuitive programming paradigms for the industrial robots as well, such as the programming by demonstration paradigm. These possibilities offered by artificial vision enable, as a matter of fact, an efficacious and promising way of achieving human-robot collaboration, which has the advantage of overcoming the limitations of both the previous paradigms yet keeping their strengths.

Scene Mapping and Understanding by Robotic Vision

The first mechanical Automaton concept was found in a Chinese text written in the 3rd century BC, while Computer Vision was born in the late 1960s. Therefore, visual perception applied to machines (i.e. the Machine Vision) is a young and exciting alliance. When robots came in, the new field of Robotic Vision was born, and these terms began to be erroneously interchanged. In short, we can say that Machine Vision is an engineering domain, which concern the industrial use of Vision. The Robotic Vision, instead, is a research field that tries to incorporate robotics aspects in computer vision algorithms. Visual Servoing, for example, is one of the problems that cannot be solved by computer vision only. Accordingly, a large part of this work deals with boosting popular Computer Vision techniques by exploiting robotics: e.g. the use of kinematics to localize a vision sensor, mounted as the robot end-effector. The remainder of this work is dedicated to the counterparty, i.e. the use of computer vision to solve real robotic problems like grasping objects or navigate avoiding obstacles. Will be presented a brief survey about mapping data structures most widely used in robotics along with SkiMap, a novel sparse data structure created both for robotic mapping and as a general purpose 3D spatial index. Thus, several approaches to implement Object Detection and Manipulation, by exploiting the aforementioned mapping strategies, will be proposed, along with a completely new Machine Teaching facility in order to simply the training procedure of modern Deep Learning networks.

A ROS-based software architecture for a versatile collaborative dual-armed autonomous mobile robot for the manufacturing industry

The industrial context is changing rapidly due to advancements in technology fueled by the Internet and Information Technology. The fourth industrial revolution counts integration, flexibility, and optimization as its fundamental pillars, and, in this context, Human-Robot Collaboration has become a crucial factor for manufacturing sustainability in Europe. Collaborative robots are appealing to many companies due to their low installation and running costs and high degree of flexibility, making them ideal for reshoring production facilities with a short return on investment. The ROSSINI European project aims to implement a true Human-Robot Collaboration by designing, developing, and demonstrating a modular and scalable platform for integrating human-centred robotic technologies in industrial production environments. The project focuses on safety concerns related to introducing a cobot in a shared working area and aims to lay the groundwork for a new working paradigm at the industrial level. The need for a software architecture suitable to the robotic platform employed in one of three use cases selected to deploy and test the new technology was the main trigger of this Thesis. The chosen application consists of the automatic loading and unloading of raw-material reels to an automatic packaging machine through an Autonomous Mobile Robot composed of an Autonomous Guided Vehicle, two collaborative manipulators, and an eye-on-hand vision system for performing tasks in a partially unstructured environment. The results obtained during the ROSSINI use case development were later used in the SENECA project, which addresses the need for robot-driven automatic cleaning of pharmaceutical bins in a very specific industrial context. The inherent versatility of mobile collaborative robots is evident from their deployment in the two projects with few hardware and software adjustments. The positive impact of Human-Robot Collaboration on diverse production lines is a motivation for future investments in research on this increasingly popular field by the industry.

Contribution of Vision and Proprioception to the Precision of Reaching Movements

Ren and colleagues (2006) found that saccades to visual targets became less accurate when somatosensory information about hand location was added, suggesting that saccades rely mainly on vision. We conducted two kinematic experiments to examine whether or not reaching movements would also show such strong reliance on vision. In Experiment 1, subjects used their dominant right hand to perform reaches, with or without a delay, to an external visual target or to their own left fingertip positioned either by the experimenter or by the participant. Unlike saccades, reaches became more accurate and precise when proprioceptive information was available. In Experiment 2, subjects reached toward external or bodily targets with differing amounts of visual information. Proprioception improved performance only when vision was limited. Our results indicate that reaching movements, unlike saccades, are improved rather than impaired by the addition of somatosensory information.

Nonlinear Control Strategies for Cooperative Control of Multi-Robot Systems

This thesis deals with distributed control strategies for cooperative control of multi-robot systems. Specifically, distributed coordination strategies are presented for groups of mobile robots. The formation control problem is initially solved exploiting artificial potential fields. The purpose of the presented formation control algorithm is to drive a group of mobile robots to create a completely arbitrarily shaped formation. Robots are initially controlled to create a regular polygon formation. A bijective coordinate transformation is then exploited to extend the scope of this strategy, to obtain arbitrarily shaped formations. For this purpose, artificial potential fields are specifically designed, and robots are driven to follow their negative gradient. Artificial potential fields are then subsequently exploited to solve the coordinated path tracking problem, thus making the robots autonomously spread along predefined paths, and move along them in a coordinated way. Formation control problem is then solved exploiting a consensus based approach. Specifically, weighted graphs are used both to define the desired formation, and to implement collision avoidance. As expected for consensus based algorithms, this control strategy is experimentally shown to be robust to the presence of communication delays. The global connectivity maintenance issue is then considered. Specifically, an estimation procedure is introduced to allow each agent to compute its own estimate of the algebraic connectivity of the communication graph, in a distributed manner. This estimate is then exploited to develop a gradient based control strategy that ensures that the communication graph remains connected, as the system evolves. The proposed control strategy is developed initially for single-integrator kinematic agents, and is then extended to Lagrangian dynamical systems.

The posterior parietal cortex: a bridge between vision and action

The present work takes into account three posterior parietal areas, V6, V6A, and PEc, all operating on different subsets of signals (visual, somatic, motor). The work focuses on the study of their functional properties, to better understand their respective contribution in the neuronal circuits that make possible the interactions between subject and external environment. In the caudalmost pole of parietal lobe there is area V6. Functional data suggest that this area is related to the encoding of both objects motion and ego-motion. However, the sensitivity of V6 neurons to optic flow stimulations has been tested only in human fMRI experiments. Here we addressed this issue by applying on monkey the same experimental protocol used in human studies. The visual stimulation obtained with the Flow Fields stimulus was the most effective and powerful to activate area V6 in monkey, further strengthening this homology between the two primates. The neighboring areas, V6A and PEc, show different cytoarchitecture and connectivity profiles, but are both involved in the control of reaches. We studied the sensory responses present in these areas, and directly compared these.. We also studied the motor related discharges of PEc neurons during reaching movements in 3D space comparing also the direction and depth tuning of PEc cells with those of V6A. The results show that area PEc and V6A share several functional properties. Area PEc, unlike V6A, contains a richer and more complex somatosensory input, and a poorer, although complex visual one. Differences emerged also comparing the motor-related properties for reaches in depth: the incidence of depth modulations in PEc and the temporal pattern of modulation for depth and direction allow to delineate a trend among the two parietal visuomotor areas.

La vision d'Athenes et de Constantinople dans les recits de voyage du XIXᵉ siecle

Le sujet de cette recherche est la perception chez les voyageurs occidentaux et grecs du XIXᵉ siècle de zones de la Méditerranée depuis toujours point d’intersections culturelles : les villes d’Athènes et de Constantinople. L’objectif de la recherche est de reconstruire la contribution des hommes de lettres, français et grecs, à la constitution de l’identité nationale selon le schéma mis en évidence par Benedict Anderson dans « Communautés Imaginées » On a tenté en se référant au corpus d’identifier dans la littérature de voyage du XIXᵉ siècle, dans le sillage de « Orientalismo » de Edward Said, comment Philhellénisme et Exotisme orientalisant, tous deux d’empreinte romantique, ont contribué à inventer pour Athènes une identité occidentale et pour Istanbul une identité orientale, ignorant presque l’existence entre les deux villes d’une commune matrice byzantine–ottomane ou mieux, l’appartenance commune à l’ensemble géopolitique de la Région Intermédiaire identifiée par Dimitri Kitsikis.