Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary

The complex physical properties of highly deformable materials such as clothes pose significant challenges fanipulation systems. We present a novel visual feedback dictionary-based method for manipulating defoor autonomous robotic mrmable objects towards a desired configuration. Our approach is based on visual servoing and we use an efficient technique to extract key features from the RGB sensor stream in the form of a histogram of deformable model features. These histogram features serve as high-level representations of the state of the deformable material. Next, we collect manipulation data and use a visual feedback dictionary that maps the velocity in the high-dimensional feature space to the velocity of the robotic end-effectors for manipulation. We have evaluated our approach on a set of complex manipulation tasks and human-robot manipulation tasks on different cloth pieces with varying material characteristics.Comment: The video is available at goo.gl/mDSC4

Co-manipulation of soft-materials estimating deformation from depth images

Human-robot co-manipulation of soft materials, such as fabrics, composites, and sheets of paper/cardboard, is a challenging operation that presents several relevant industrial applications. Estimating the deformation state of the co-manipulated material is one of the main challenges. Viable methods provide the indirect measure by calculating the human-robot relative distance. In this paper, we develop a data-driven model to estimate the deformation state of the material from a depth image through a Convolutional Neural Network (CNN). First, we define the deformation state of the material as the relative roto-translation from the current robot pose and a human grasping position. The model estimates the current deformation state through a Convolutional Neural Network, specifically a DenseNet-121 pretrained on ImageNet.The delta between the current and the desired deformation state is fed to the robot controller that outputs twist commands. The paper describes the developed approach to acquire, preprocess the dataset and train the model. The model is compared with the current state-of-the-art method based on a skeletal tracker from cameras. Results show that our approach achieves better performances and avoids the various drawbacks caused by using a skeletal tracker.Finally, we also studied the model performance according to different architectures and dataset dimensions to minimize the time required for dataset acquisitionComment: Pre-print, submitted to Journal of Intelligent Manufacturin

Desenvolvimento de equipamento de manipulação de objectos deformáveis e a sua interacção com uma máquina de injecção de plásticos

In this project, our objective was to thoroughly investigate the feasibility of automating a process at Ficocables by integrating a robotic arm. Specifically, we focused on automating the joining of two separate processes while eliminating the need for manual intervention in the second operation. The equipment involved in the process includes a Roboco Zamak injection machine and a Babyplast polymer injection machine. With well-defined project requirements, we explored various solutions and sought guidance from Fluidotronica, a renowned expert in this domain. With their support, we identified the collaborative robot JAKA Zu 3s, equipped with a long-finger gripper, as the optimal solution for our needs. To assess the financial viability, we conducted a meticulous financial analysis using methods like NPV and payback period, both of which demonstrated promising results. Although the implementation of the robotic arm is still pending, the outcomes of our study highlight its remarkable versatility for future applications within Ficocables. This project exemplifies the potential advantages of automation and offers valuable insights for forthcoming initiatives in this field.Neste projeto, o objetivo era investigar exaustivamente a viabilidade de automatizar um processo na Ficocables através da integração de um braço robótico. Especificamente, concentrámo-nos em automatizar a junção de dois processos separados, eliminando a necessidade de intervenção manual na segunda operação. O equipamento envolvido no processo inclui uma máquina de injeção de Zamak, denominada Robocop e uma máquina de injeção de polímero denominada Babyplast. Com os requisitos de projeto bem definidos, explorámos várias soluções e procurámos orientação junto da Fluidotronica, um especialista de renome neste domínio. Com o seu apoio, identificámos o robô colaborativo JAKA Zu 3s, equipado com uma pinça de dedos longos como a solução ideal para as necessidades deste projeto. Para avaliar a viabilidade financeira, efetuou-se uma análise financeira meticulosa utilizando métodos como o NPV e o período de retorno do investimento, tendo ambos demonstrado resultados promissores. Embora a implementação do braço robótico ainda esteja pendente, os resultados do nosso estudo destacam a sua notável versatilidade para futuras aplicações na Ficocables. Este projeto exemplifica as vantagens potenciais da automatização e oferece uma visão valiosa para iniciativas futuras neste domínio

Computer- and robot-assisted Medical Intervention

Medical robotics includes assistive devices used by the physician in order to make his/her diagnostic or therapeutic practices easier and more efficient. This chapter focuses on such systems. It introduces the general field of Computer-Assisted Medical Interventions, its aims, its different components and describes the place of robots in that context. The evolutions in terms of general design and control paradigms in the development of medical robots are presented and issues specific to that application domain are discussed. A view of existing systems, on-going developments and future trends is given. A case-study is detailed. Other types of robotic help in the medical environment (such as for assisting a handicapped person, for rehabilitation of a patient or for replacement of some damaged/suppressed limbs or organs) are out of the scope of this chapter.Comment: Handbook of Automation, Shimon Nof (Ed.) (2009) 000-00

HRS: Rover Technologies

No abstract availabl

RoboToy Demoulding: Robotic Demoulding System for Toy Manufacturing Industry

Industrial environments and product manufacturing processes are currently being automated and robotized. Nowadays, it is common to have robots integrated in the automotive industry, robots palletizing in the food industry and robots performing welding tasks in the metal industry. However, there are many traditional and manual sectors out of date with technology, such as the toy manufacturing industry. This work describes a new robotic system able to perform the demoulding task in a toy manufacturing process, which is a tedious labor-intensive and potentially hazardous task for human operators. The system is composed of specialised machinery about the rotational moulding manufacturing process, cameras, actuators, and a collaborative robot. A vision-based algorithm makes this system capable of handling soft plastic pieces which are deformable and flexible during demoulding. The system reduces the stress and potential injuries to human operators, allowing them to perform other tasks wit h higher dexterity requirements or relocate to other sub-tasks of the process where the physical effort is minor