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

Automated Configuration of Gripper Fingers from a Construction Kit for Robotic Applications

Gripper finger design is a complex process that requires a lot of experience, time, and effort. For this reason, automating this design process is an important area of research that has the potential to improve the efficiency and effectiveness of robotic systems. The current approaches are aimed at the automated design of monolithic gripper fingers, which have to be manufactured additively or by machining. This paper describes a novel approach for the automated design of gripper fingers. The motivation for this work stems from the increasing demand for flexible, adaptable handling systems in various industries in response to the increasing individualization of products as well as the increasing volatility in the markets. Based on the CAD data of the handling objects, the most suitable configuration of gripper fingers can be determined from the existing modules of a construction kit for the respective handling object, which can significantly reduce the provisioning time for new gripper fingers. It can be shown that gripper fingers can be effectively configured for a variety of objects and two different grippers, increasing flexibility in industrial handling processes

Specification and installation of a robotic system to improve the production efficiency of a cutting station

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáOne of the main technologies in Industry 4.0 are collaborative robots (cobot), which allow humans to work alongside them while respecting the necessary safety standards. The use of robots in industry is generally done to improve production, quality, reduce repetitive efforts and heavy manual labor. In order to save time and, consequently, money. With this, the Catraport company presented the problem of automating a cutting station consisting of two machines. In the development of the automated solution, simulations were used to analyze which would be the most viable option for the company. The company chose the hybrid solution formed by a robot and a worker. With the requirements defined, they purchased the robot, which they required to be a cobot. The model is the UR-10e, from Universal Robots, and also, as accessories, the adaptive gripper and the wrist camera, both from Robotiq. After the purchase, the hardware part of the robot was installed and the software for the accessories was configured. With this, the visual recognition of the part was calibrated to identify its position, the gripper was adjusted to better fit the piece and possible solutions to position the parts in the exit box. For the last step it was necessary to develop an individual programming case for each piece, because they have shapes that do not allow a simple fit between them. It was also used a resource of the camera to identify tags, which was used for the system to recognize the position and orientation of each pallet.Uma das principais tecnologias da Indústria 4.0 são robôs colaborativos (cobot), que permitem o trabalho ao lado de humanos respeitando as normas de segurança necessárias. O uso de robôs na indústria é geralmente feito para melhorar produção, qualidade, reduzir esforços repetitivos e trabalhos manuais pesados. De forma a economizar tempo e, consequentemente, dinheiro. Com isso, a empresa Catraport apresentou o problema de automatizar uma estação de corte composta por duas máquinas. No desenvolvimento da solução automatizada, foram utilizadas simulações para a análise de qual seria a opção mais viável para a empresa. A empresa optou pela solução híbrida formada por um robô e um trabalhador. Com os requisitos definidos, fez a aquisição do robô, que eles tinham como exigência ser um cobot. O modelo é o UR-10e, da Universal Robots, e também, como acessórios, a garra adaptativa e a câmera de pulso, ambos da Robotiq. Após a compra, a parte de hardware do robô foi instalada e os softwares para os acessórios configurados. Com isso, o reconhecimento visual da peça foi calibrado para identificar sua posição, a garra foi ajustada para encaixar melhor na peça e possíveis soluções para posicionar as partes na caixa de saída. Para a última etapa foi necessário desenvolver um caso de programação individual para cada peça, pois elas possuem formatos que não permitem um encaixe simples entre elas. Também foi utilizado um recurso da câmera de identificar tags, que foi utilizado para que o sistema reconhecesse a posição e orientação de cada pallet

Part clamping and fixture geometric adaptability for reconfigurable assembly systems.

Masters of Science in Mechanical Engineering. University of KwaZulu-Natal. Durban, 2017.The Fourth Industrial Revolution is leading towards cyber-physical systems which justified research efforts in pursuing efficient production systems incorporating flexible grippers. Due to the complexity of assembly processes, reconfigurable assembly systems have received considerable attention in recent years. The demand for the intricate task and complicated operations, demands the need for efficient robotic manipulators that are required to manoeuvre and grasp objects effectively. Investigations were performed to understand the requirements of efficient gripping systems and existing gripping methods. A biologically inspired robotic gripper was investigated to establish conformity properties for the performance of a robotic gripper system. The Fin Ray Effect® was selected as a possible approach to improve effective gripping and reduce slippage of component handling with regards to pick and place procedures of assembly processes. As a result, the study established the optimization of self-adjusting end-effectors. The gripper system design was simulated and empirically tested. The impact of gripping surface compliance and geometric conformity was investigated. The gripper system design focused on the response of load applied to the conformity mechanism called the Fin Ray Effect®. The appendages were simulated to determine the deflection properties and stress distribution through a finite element analysis. The simulation proved that the configuration of rib structures of the appendages affected the conformity to an applied force representing an object in contact. The system was tested in real time operation and required a control system to produce an active performance of the system. A mass loading test was performed on the gripper system. The repeatability and mass handling range was determined. A dynamic operation was tested on the gripper to determine force versus time properties throughout the grasping movement for a pick and place procedure. The fluctuating forces generated through experimentation was related to the Lagrangian model describing forces experienced by a moving object. The research promoted scientific contribution to the investigation, analysis, and design of intelligent gripping systems that can potentially be implemented in the operational processes of on-demand production lines for reconfigurable assembly systems

Design, evaluation, and control of nexus: a multiscale additive manufacturing platform with integrated 3D printing and robotic assembly.

Additive manufacturing (AM) technology is an emerging approach to creating three-dimensional (3D) objects and has seen numerous applications in medical implants, transportation, aerospace, energy, consumer products, etc. Compared with manufacturing by forming and machining, additive manufacturing techniques provide more rapid, economical, efficient, reliable, and complex manufacturing processes. However, additive manufacturing also has limitations on print strength and dimensional tolerance, while traditional additive manufacturing hardware platforms for 3D printing have limited flexibility. In particular, part geometry and materials are limited to most 3D printing hardware. In addition, for multiscale and complex products, samples must be printed, fabricated, and transferred among different additive manufacturing platforms in different locations, which leads to high cost, long process time, and low yield of products. This thesis investigates methods to design, evaluate, and control the NeXus, which is a novel custom robotic platform for multiscale additive manufacturing with integrated 3D printing and robotic assembly. NeXus can be used to prototype miniature devices and systems, such as wearable MEMS sensor fabrics, microrobots for wafer-scale microfactories, tactile robot skins, next generation energy storage (solar cells), nanostructure plasmonic devices, and biosensors. The NeXus has the flexibility to fixture, position, transport, and assemble components across a wide spectrum of length scales (Macro-Meso-Micro-Nano, 1m to 100nm) and provides unparalleled additive process capabilities such as 3D printing through both aerosol jetting and ultrasonic bonding and forming, thin-film photonic sintering, fiber loom weaving, and in-situ Micro-Electro-Mechanical System (MEMS) packaging and interconnect formation. The NeXus system has a footprint of around 4m x 3.5m x 2.4m (X-Y-Z) and includes two industrial robotic arms, precision positioners, multiple manipulation tools, and additive manufacturing processes and packaging capabilities. The design of the NeXus platform adopted the Lean Robotic Micromanufacturing (LRM) design principles and simulation tools to mitigate development risks. The NeXus has more than 50 degrees of freedom (DOF) from different instruments, precise evaluation of the custom robots and positioners is indispensable before employing them in complex and multiscale applications. The integration and control of multi-functional instruments is also a challenge in the NeXus system due to different communication protocols and compatibility. Thus, the NeXus system is controlled by National Instruments (NI) LabVIEW real-time operating system (RTOS) with NI PXI controller and a LabVIEW State Machine User Interface (SMUI) and was programmed considering the synchronization of various instruments and sequencing of additive manufacturing processes for different tasks. The operation sequences of each robot along with relevant tools must be organized in safe mode to avoid crashes and damage to tools during robots’ motions. This thesis also describes two demonstrators that are realized by the NeXus system in detail: skin tactile sensor arrays and electronic textiles. The fabrication process of the skin tactile sensor uses the automated manufacturing line in the NeXus with pattern design, precise calibration, synchronization of an Aerosol Jet printer, and a custom positioner. The fabrication process for electronic textiles is a combination of MEMS fabrication techniques in the cleanroom and the collaboration of multiple NeXus robots including two industrial robotic arms and a custom high-precision positioner for the deterministic alignment process

Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching

This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses a category-agnostic affordance prediction algorithm to select and execute among four different grasping primitive behaviors. It then recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT-Princeton Team system that took 1st place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at http://arc.cs.princeton.eduComment: Project webpage: http://arc.cs.princeton.edu Summary video: https://youtu.be/6fG7zwGfIk