Image preprocessing for artistic robotic painting

Artistic robotic painting implies creating a picture on canvas according to a brushstroke map preliminarily computed from a source image. To make the painting look closer to the human artwork, the source image should be preprocessed to render the effects usually created by artists. In this paper, we consider three preprocessing effects: aerial perspective, gamut compression and brushstroke coherence. We propose an algorithm for aerial perspective amplification based on principles of light scattering using a depth map, an algorithm for gamut compression using nonlinear hue transformation and an algorithm for image gradient filtering for obtaining a well-coherent brushstroke map with a reduced number of brushstrokes, required for practical robotic painting. The described algorithms allow interactive image correction and make the final rendering look closer to a manually painted artwork. To illustrate our proposals, we render several test images on a computer and paint a monochromatic image on canvas with a painting robot

Visual servoing of a five-bar linkage mechanism /

This document is the written product of the graduation project developed: Visual Servoing of a Five-bar Linkage Mechanism. This project means to venture into the fields of a method of control, with visual feedback, known as Visual Servoing. The contents of this document show a summary of all the theory taken into account to realize the project. They also shows how other people have approached this method. These pages present the project establishing its aims, the importance of its realization, a detailed description of how it was carried out - including experiments and obstacles, - and the results obtained. This document also informs how is this work of use and what can be done from it. In the same way, here are consigned the books, articles, and works consulted in the way, which in their own pages provide a large quantity of references and information.Incluye referencias bibliográfica

Workspace optimization for a planar cable-suspended direct-driven robot

The present work is inspired by an industrial task, i.e. spray painting a large area by means of a robotic system consisting in a Cable-Driven Parallel Robot (CDPR). In many cases, the area of the robot workspace is smaller than the area to be painted. For this reason, the base of the robot has to be shifted several times during the painting process. These robots are referred to as Repetitive Workspace Robots (RWR). In other words, in order to accomplish the whole task, they need to be moved after they have completed a sub-task locally. A cable suspended CDPR is an ideal candidate for such tasks; it can be thin, light, flexible and cost-efficient. The question is: which is the best shape of the local workspace in these conditions? In fact, not always a larger area of the local workspace guarantees an efficient painting process. This is because the efficiency relies mainly on the shape rather than on the local workspace area itself. In this work we employ an index [Seriani S, Gallina P, Gasparetto A, 2014] to evaluate the efficiency of the workspace of a 2-link CDPR. Finally, we show how the index value changes in relation to some geometrical parameters of the robot, thus laying the foundations for a general design methodology

The Machine as Art/ The Machine as Artist

The articles collected in this volume from the two companion Arts Special Issues, “The Machine as Art (in the 20th Century)” and “The Machine as Artist (in the 21st Century)”, represent a unique scholarly resource: analyses by artists, scientists, and engineers, as well as art historians, covering not only the current (and astounding) rapprochement between art and technology but also the vital post-World War II period that has led up to it; this collection is also distinguished by several of the contributors being prominent individuals within their own fields, or as artists who have actually participated in the still unfolding events with which it is concerne

Mobile Robotic Painting of Texture

Robotic painting is well-established in controlled factory environments, but there is now potential for mobile robots to do functional painting tasks around the everyday world. An obvious first target for such robots is painting a uniform single color. A step further is the painting of textured images. Texture involves a varying appearance, and requires that paint is delivered accurately onto the physical surface to produce the desired effect. Robotic painting of texture is relevant for architecture and in themed environments. A key challenge for robotic painting of texture is to take a desired image as input, and to generate the paint commands to as closely as possible create the desired appearance, according to the robotic capabilities. This paper describes a deep learning approach to take an input ink map of a desired texture, and infer robotic paint commands to produce that texture. We analyze the trade-offs between quality of reconstructed appearance and ease of execution. Our method is general for different kinds of robotic paint delivery systems, but the emphasis here is on spray painting. More generally, the framework can be viewed as an approach for solving a specific class of inverse imaging problems

Ground Robotic Hand Applications for the Space Program study (GRASP)

This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

Enabling technologies for precise aerial manufacturing with unmanned aerial vehicles

The construction industry is currently experiencing a revolution with automation techniques such as additive manufacturing and robot-enabled construction. Additive Manufacturing (AM) is a key technology that can o er productivity improvement in the construction industry by means of o -site prefabrication and on-site construction with automated systems. The key bene t is that building elements can be fabricated with less materials and higher design freedom compared to traditional manual methods. O -site prefabrication with AM has been investigated for some time already, but it has limitations in terms of logistical issues of components transportation and due to its lack of design exibility on-site. On-site construction with automated systems, such as static gantry systems and mobile ground robots performing AM tasks, can o er additional bene ts over o -site prefabrication, but it needs further research before it will become practical and economical. Ground-based automated construction systems also have the limitation that they cannot extend the construction envelope beyond their physical size. The solution of using aerial robots to liberate the process from the constrained construction envelope has been suggested, albeit with technological challenges including precision of operation, uncertainty in environmental interaction and energy e ciency. This thesis investigates methods of precise manufacturing with aerial robots. In particular, this work focuses on stabilisation mechanisms and origami-based structural elements that allow aerial robots to operate in challenging environments. An integrated aerial self-aligning delta manipulator has been utilised to increase the positioning accuracy of the aerial robots, and a Material Extrusion (ME) process has been developed for Aerial Additive Manufacturing (AAM). A 28-layer tower has been additively manufactured by aerial robots to demonstrate the feasibility of AAM. Rotorigami and a bioinspired landing mechanism demonstrate their abilities to overcome uncertainty in environmental interaction with impact protection capabilities and improved robustness for UAV. Design principles using tensile anchoring methods have been explored, enabling low-power operation and explores possibility of low-power aerial stabilisation. The results demonstrate that precise aerial manufacturing needs to consider not only just the robotic aspects, such as ight control algorithms and mechatronics, but also material behaviour and environmental interaction as factors for its success.Open Acces