Autonomous Mechanical Assembly on the Space Shuttle: An Overview

The space shuttle will be equipped with a pair of 50 ft. manipulators used to handle payloads and to perform mechanical assembly operations. Although current plans call for these manipulators to be operated by a human teleoperator. The possibility of using results from robotics and machine intelligence to automate this shuttle assembly system was investigated. The major components of an autonomous mechanical assembly system are examined, along with the technology base upon which they depend. The state of the art in advanced automation is also assessed

A LVDS Serial AER Link

Address-Event-Representation (AER) is a communication protocol for transferring asynchronous events between VLSI chips, originally developed for bio-inspired processing systems (for example, image processing). Such systems may consist of a complicated hierarchical structure with many chips that transmit data among them in real time, while performing some processing (for example, convolutions). The event information is transferred using a high speed digital parallel bus (typically 16 bits and 20ns-40ns per event). This paper presents a testing platform for AER systems that allows to analyse a LVDS Serial AER link. The interface allows up to 0.7 Gbps (~40Mev/s, 16 bits/ev). The eye diagram ensures that the platform could support 1.2 Gbps.Commission of the European Communities IST-2001-34124 (CAVIAR)Comisión Interministerial de Ciencia y Tecnología TIC-2003-08164-C03-0

Cable Manipulation with a Tactile-Reactive Gripper

Cables are complex, high dimensional, and dynamic objects. Standard approaches to manipulate them often rely on conservative strategies that involve long series of very slow and incremental deformations, or various mechanical fixtures such as clamps, pins or rings. We are interested in manipulating freely moving cables, in real time, with a pair of robotic grippers, and with no added mechanical constraints. The main contribution of this paper is a perception and control framework that moves in that direction, and uses real-time tactile feedback to accomplish the task of following a dangling cable. The approach relies on a vision-based tactile sensor, GelSight, that estimates the pose of the cable in the grip, and the friction forces during cable sliding. We achieve the behavior by combining two tactile-based controllers: 1) Cable grip controller, where a PD controller combined with a leaky integrator regulates the gripping force to maintain the frictional sliding forces close to a suitable value; and 2) Cable pose controller, where an LQR controller based on a learned linear model of the cable sliding dynamics keeps the cable centered and aligned on the fingertips to prevent the cable from falling from the grip. This behavior is possible by a reactive gripper fitted with GelSight-based high-resolution tactile sensors. The robot can follow one meter of cable in random configurations within 2-3 hand regrasps, adapting to cables of different materials and thicknesses. We demonstrate a robot grasping a headphone cable, sliding the fingers to the jack connector, and inserting it. To the best of our knowledge, this is the first implementation of real-time cable following without the aid of mechanical fixtures.Comment: Accepted to RSS 202

Performance evaluation of a six-axis generalized force-reflecting teleoperator

Work in real-time distributed computation and control has culminated in a prototype force-reflecting telemanipulation system having a dissimilar master (cable-driven, force-reflecting hand controller) and a slave (PUMA 560 robot with custom controller), an extremely high sampling rate (1000 Hz), and a low loop computation delay (5 msec). In a series of experiments with this system and five trained test operators covering over 100 hours of teleoperation, performance was measured in a series of generic and application-driven tasks with and without force feedback, and with control shared between teleoperation and local sensor referenced control. Measurements defining task performance included 100-Hz recording of six-axis force/torque information from the slave manipulator wrist, task completion time, and visual observation of predefined task errors. The task consisted of high precision peg-in-hole insertion, electrical connectors, velcro attach-de-attach, and a twist-lock multi-pin connector. Each task was repeated three times under several operating conditions: normal bilateral telemanipulation, forward position control without force feedback, and shared control. In shared control, orientation was locally servo controlled to comply with applied torques, while translation was under operator control. All performance measures improved as capability was added along a spectrum of capabilities ranging from pure position control through force-reflecting teleoperation and shared control. Performance was optimal for the bare-handed operator

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

High-speed electrical connector assembly by structured compliance in a finray-effect gripper

Fine assembly tasks such as electrical connector insertion have tight tolerances and sensitive components, requiring compensation of alignment errors while applying sufficient force in the insertion direction, ideally at high speeds and while grasping a range of components. Vision, tactile, or force sensors can compensate alignment errors, but have limited bandwidth, limiting the safe assembly speed. Passive compliance such as silicone-based fingers can reduce collision forces and grasp a range of components, but often cannot provide the accuracy or assembly forces required. To support high-speed mechanical search and self-aligning insertion, this paper proposes monolithic additively manufactured fingers which realize a moderate, structured compliance directly proximal to the gripped object. The geometry of finray-effect fingers are adapted to add form-closure features and realize a directionally-dependent stiffness at the fingertip, with a high stiffness to apply insertion forces and lower transverse stiffness to support alignment. Design parameters and mechanical properties of the fingers are investigated with FEM and empirical studies, analyzing the stiffness, maximum load, and viscoelastic effects. The fingers realize a remote center of compliance, which is shown to depend on the rib angle, and a directional stiffness ratio of $14-36$. The fingers are applied to a plug insertion task, realizing a tolerance window of $7.5$ mm and approach speeds of $1.3$ m/s.Comment: Under review. arXiv admin note: substantial text overlap with arXiv:2301.0843

Localization and Manipulation of Small Parts Using GelSight Tactile Sensing

Robust manipulation and insertion of small parts can be challenging because of the small tolerances typically involved. The key to robust control of these kinds of manipulation interactions is accurate tracking and control of the parts involved. Typically, this is accomplished using visual servoing or force-based control. However, these approaches have drawbacks. Instead, we propose a new approach that uses tactile sensing to accurately localize the pose of a part grasped in the robot hand. Using a feature-based matching technique in conjunction with a newly developed tactile sensing technology known as GelSight that has much higher resolution than competing methods, we synthesize high-resolution height maps of object surfaces. As a result of these high-resolution tactile maps, we are able to localize small parts held in a robot hand very accurately. We quantify localization accuracy in benchtop experiments and experimentally demonstrate the practicality of the approach in the context of a small parts insertion problem.National Science Foundation (U.S.) (NSF Grant No. 1017862)United States. National Aeronautics and Space Administration (NASA under Grant No. NNX13AQ85G)United States. Office of Naval Research (ONR Grant No. N000141410047

Bin-Picking Solution for Randomly Placed Automotive Connectors Based on Machine Learning Techniques

This paper presents the development of a bin-picking solution based on low-cost vision systems for the manipulation of automotive electrical connectors using machine learning techniques. The automotive sector has always been in a state of constant growth and change, which also implies constant challenges in the wire harnesses sector, and the emerging growth of electric cars is proof of this and represents a challenge for the industry. Traditionally, this sector is based on strong human work manufacturing and the need arises to make the digital transition, supported in the context of Industry 4.0, allowing the automation of processes and freeing operators for other activities with more added value. Depending on the car model and its feature packs, a connector can interface with a different number of wires, but the connector holes are the same. Holes not connected with wires need to be sealed, mainly to guarantee the tightness of the cable. Seals are inserted manually or, more recently, through robotic stations. Due to the huge variety of references and connector configurations, layout errors sometimes occur during seal insertion due to changed references or problems with the seal insertion machine. Consequently, faulty connectors are dumped into boxes, piling up different types of references. These connectors are not trash and need to be reused. This article proposes a bin-picking solution for classification, selection and separation, using a two-finger gripper, of these connectors for reuse in a new operation of removal and insertion of seals. Connectors are identified through a 3D vision system, consisting of an Intel RealSense camera for object depth information and the YOLOv5 algorithm for object classification. The advantage of this approach over other solutions is the ability to accurately detect and grasp small objects through a low-cost 3D camera even when the image resolution is low, benefiting from the power of machine learning algorithms.info:eu-repo/semantics/publishedVersio