263 research outputs found

    Walk-through programming for robotic manipulators based on admittance control

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    The present paper addresses the issues that should be covered in order to develop walk-through programming techniques (i.e. a manual guidance of the robot) in an industrial scenario. First, an exact formulation of the dynamics of the tool the human should feel when interacting with the robot is presented. Then, the paper discusses a way to implement such dynamics on an industrial robot equipped with an open robot control system and a wrist force/torque sensor, as well as the safety issues related to the walk-through programming. In particular, two strategies that make use of admittance control to constrain the robot motion are presented. One slows down the robot when the velocity of the tool centre point exceeds a specified safety limit, the other one limits the robot workspace by way of virtual safety surfaces. Experimental results on a COMAU Smart Six robot are presented, showing the performance of the walk-through programming system endowed with the two proposed safety strategies

    walk through programming for industrial applications

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    Abstract Collaboration between humans and robots is increasingly desired in several application domains, including the manufacturing domain. The paper describes a software control architecture for industrial robotic applications allowing human-robot cooperation during the programming phase of a robotic task. The control architecture is based on admittance control and tool dynamics compensation for implementing walk-through programming and manual guidance. Further steps to integrate this system on a real set-up include the robot kinematics and a socket communication that sends a binary file to the robot

    A survey of robot manipulation in contact

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    In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of (1) performing tasks that always require contact and (2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks

    Singularity Avoidance for Cart-Mounted Hand-Guided Collaborative Robots: A Variational Approach

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    Most collaborative robots (cobots) can be taught by hand guiding: essentially, by manually jogging the robot, an operator teaches some configurations to be employed as via points. Based on those via points, Cartesian end-effector trajectories such as straight lines, circular arcs or splines are then constructed. Such methods can, in principle, be employed for cart-mounted cobots (i.e., when the jogging involves one or two linear axes, besides the cobot axes). However, in some applications, the sole imposition of via points in Cartesian space is not sufficient. On the contrary, albeit the overall system is redundant, (i) the via points must be reached at the taught joint configurations, and (ii) the undesirable singularity (and near-singularity) conditions must be avoided. The naive approach, consisting of setting the cart trajectory beforehand (for instance, by imposing a linear-in-time motion law that crosses the taught cart configurations), satisfies the first need, but does not guarantee the satisfaction of the second. Here, we propose an approach consisting of (i) a novel strategy for decoupling the planning of the cart trajectory and that of the robot joints, and (ii) a novel variational technique for computing the former in a singularity-aware fashion, ensuring the avoidance of a class of workspace singularity and near-singularity configurations

    Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots

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    We show dynamic locomotion strategies for wheeled quadrupedal robots, which combine the advantages of both walking and driving. The developed optimization framework tightly integrates the additional degrees of freedom introduced by the wheels. Our approach relies on a zero-moment point based motion optimization which continuously updates reference trajectories. The reference motions are tracked by a hierarchical whole-body controller which computes optimal generalized accelerations and contact forces by solving a sequence of prioritized tasks including the nonholonomic rolling constraints. Our approach has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled including the non-steerable wheels attached to its legs. We conducted experiments on flat and inclined terrains as well as over steps, whereby we show that integrating the wheels into the motion control and planning framework results in intuitive motion trajectories, which enable more robust and dynamic locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4 m/s and a reduction of the cost of transport by 83 % we prove the superiority of wheeled-legged robots compared to their legged counterparts.Comment: IEEE Robotics and Automation Letter

    Adaptive physical human-robot interaction (PHRI) with a robotic nursing assistant.

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    Recently, more and more robots are being investigated for future applications in health-care. For instance, in nursing assistance, seamless Human-Robot Interaction (HRI) is very important for sharing workspaces and workloads between medical staff, patients, and robots. In this thesis we introduce a novel robot - the Adaptive Robot Nursing Assistant (ARNA) and its underlying components. ARNA has been designed specifically to assist nurses with day-to-day tasks such as walking patients, pick-and-place item retrieval, and routine patient health monitoring. An adaptive HRI in nursing applications creates a positive user experience, increase nurse productivity and task completion rates, as reported by experimentation with human subjects. ARNA has been designed to include interface devices such as tablets, force sensors, pressure-sensitive robot skins, LIDAR and RGBD camera. These interfaces are combined with adaptive controllers and estimators within a proposed framework that contains multiple innovations. A research study was conducted on methods of deploying an ideal HumanMachine Interface (HMI), in this case a tablet-based interface. Initial study points to the fact that a traded control level of autonomy is ideal for tele-operating ARNA by a patient. The proposed method of using the HMI devices makes the performance of a robot similar for both skilled and un-skilled workers. A neuro-adaptive controller (NAC), which contains several neural-networks to estimate and compensate for system non-linearities, was implemented on the ARNA robot. By linearizing the system, a cross-over usability condition is met through which humans find it more intuitive to learn to use the robot in any location of its workspace, A novel Base-Sensor Assisted Physical Interaction (BAPI) controller is introduced in this thesis, which utilizes a force-torque sensor at the base of the ARNA robot manipulator to detect full body collisions, and make interaction safer. Finally, a human-intent estimator (HIE) is proposed to estimate human intent while the robot and user are physically collaborating during certain tasks such as adaptive walking. A NAC with HIE module was validated on a PR2 robot through user studies. Its implementation on the ARNA robot platform can be easily accomplished as the controller is model-free and can learn robot dynamics online. A new framework, Directive Observer and Lead Assistant (DOLA), is proposed for ARNA which enables the user to interact with the robot in two modes: physically, by direct push-guiding, and remotely, through a tablet interface. In both cases, the human is being “observed” by the robot, then guided and/or advised during interaction. If the user has trouble completing the given tasks, the robot adapts their repertoire to lead users toward completing goals. The proposed framework incorporates interface devices as well as adaptive control systems in order to facilitate a higher performance interaction between the user and the robot than was previously possible. The ARNA robot was deployed and tested in a hospital environment at the School of Nursing of the University of Louisville. The user-experience tests were conducted with the help of healthcare professionals where several metrics including completion time, rate and level of user satisfaction were collected to shed light on the performance of various components of the proposed framework. The results indicate an overall positive response towards the use of such assistive robot in the healthcare environment. The analysis of these gathered data is included in this document. To summarize, this research study makes the following contributions: Conducting user experience studies with the ARNA robot in patient sitter and walker scenarios to evaluate both physical and non-physical human-machine interfaces. Evaluation and Validation of Human Intent Estimator (HIE) and Neuro-Adaptive Controller (NAC). Proposing the novel Base-Sensor Assisted Physical Interaction (BAPI) controller. Building simulation models for packaged tactile sensors and validating the models with experimental data. Description of Directive Observer and Lead Assistance (DOLA) framework for ARNA using adaptive interfaces

    Kontextsensitive Körperregulierung für redundante Roboter

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    In the past few decades the classical 6 degrees of freedom manipulators' dominance has been challenged by the rise of 7 degrees of freedom redundant robots. Similarly, with increased availability of humanoid robots in academic research, roboticists suddenly have access to highly dexterous platforms with multiple kinematic chains capable of undertaking multiple tasks simultaneously. The execution of lower-priority tasks, however, are often done in task/scenario specific fashion. Consequently, these systems are not scalable and slight changes in the application often implies re-engineering the entire control system and deployment which impedes the development process over time. This thesis introduces an alternative systematic method of addressing the secondary tasks and redundancy resolution called, context aware body regulation. Contexts consist of one or multiple tasks, however, unlike the conventional definitions, the tasks within a context are not rigidly defined and maintain some level of abstraction. For instance, following a particular trajectory constitutes a concrete task while performing a Cartesian motion with the end-effector represents an abstraction of the same task and is more appropriate for context formulation. Furthermore, contexts are often made up of multiple abstract tasks that collectively describe a reoccurring situation. Body regulation is an umbrella term for a collection of schemes for addressing the robots' redundancy when a particular context occurs. Context aware body regulation offers several advantages over traditional methods. Most notably among them are reusability, scalability and composability of contexts and body regulation schemes. These three fundamental concerns are realized theoretically by in-depth study and through mathematical analysis of contexts and regulation strategies; and are practically implemented by a component based software architecture that complements the theoretical aspects. The findings of the thesis are applicable to any redundant manipulator and humanoids, and allow them to be used in real world applications. Proposed methodology presents an alternative approach for the control of robots and offers a new perspective for future deployment of robotic solutions.Im Verlauf der letzten Jahrzehnte wich der Einfluss klassischer Roboterarme mit 6 Freiheitsgraden zunehmend denen neuer und vielfältigerer Manipulatoren mit 7 Gelenken. Ebenso stehen der Forschung mit den neuartigen Humanoiden inzwischen auch hoch-redundante Roboterplattformen mit mehreren kinematischen Ketten zur Verfügung. Diese überaus flexiblen und komplexen Roboter-Kinematiken ermöglichen generell das gleichzeitige Verfolgen mehrerer priorisierter Bewegungsaufgaben. Die Steuerung der weniger wichtigen Aufgaben erfolgt jedoch oft in anwendungsspezifischer Art und Weise, welche die Skalierung der Regelung zu generellen Kontexten verhindert. Selbst kleine Änderungen in der Anwendung bewirken oft schon, dass große Teile der Robotersteuerung überarbeitet werden müssen, was wiederum den gesamten Entwicklungsprozess behindert. Diese Dissertation stellt eine alternative, systematische Methode vor um die Redundanz neuer komplexer Robotersysteme zu bewältigen und vielfältige, priorisierte Bewegungsaufgaben parallel zu steuern: Die so genannte kontextsensitive Körperregulierung. Darin bestehen Kontexte aus einer oder mehreren Bewegungsaufgaben. Anders als in konventionellen Anwendungen sind die Aufgaben nicht fest definiert und beinhalten eine gewisse Abstraktion. Beispielsweise stellt das Folgen einer bestimmten Trajektorie eine sehr konkrete Bewegungsaufgabe dar, während die Ausführung einer Kartesischen Bewegung mit dem Endeffektor eine Abstraktion darstellt, die für die Kontextformulierung besser geeignet ist. Kontexte setzen sich oft aus mehreren solcher abstrakten Aufgaben zusammen und beschreiben kollektiv eine sich wiederholende Situation. Durch die Verwendung der kontextsensitiven Körperregulierung ergeben sich vielfältige Vorteile gegenüber traditionellen Methoden: Wiederverwendbarkeit, Skalierbarkeit, sowie Komponierbarkeit von Konzepten. Diese drei fundamentalen Eigenschaften werden in der vorliegenden Arbeit theoretisch mittels gründlicher mathematischer Analyse aufgezeigt und praktisch mittels einer auf Komponenten basierenden Softwarearchitektur realisiert. Die Ergebnisse dieser Dissertation lassen sich auf beliebige redundante Manipulatoren oder humanoide Roboter anwenden und befähigen diese damit zur realen Anwendung außerhalb des Labors. Die hier vorgestellte Methode zur Regelung von Robotern stellt damit eine neue Perspektive für die zukünftige Entwicklung von robotischen Lösungen dar

    Hybrid Open-Loop Closed-Loop Control of Coupled Human-Robot Balance During Assisted Stance Transition with Extra Robotic Legs

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    A new approach to the human-robot shared control of the Extra Robotic Legs (XRL) wearable augmentation system is presented. The XRL system consists of two extra legs that bear the entirety of its backpack payload, as well as some of the human operator's weight. The XRL System must support its own balance and assist the operator stably while allowing them to move in selected directions. In some directions of the task space the XRL must constrain the human motion with position feedback for balance, while in other directions the XRL must have no position feedback, so that the human can move freely. Here, we present Hybrid Open-Loop / Closed-Loop Control Architecture for mixing the two control modes in a systematic manner. The system is reduced to individual joint feedback control that is simple to implement and reliable against failure. The method is applied to the XRL system that assists a human in conducting a nuclear waste decommissioning task. A prototype XRL system has been developed and demonstrated with a simulated human performing the transition from standing to crawling and back again while coupled to the prototype XRL system

    Collaborative and Cooperative Robotics Applications using Visual Perception

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    The objective of this Thesis is to develop novel integrated strategies for collaborative and cooperative robotic applications. Commonly, industrial robots operate in structured environments and in work-cell separated from human operators. Nowadays, collaborative robots have the capacity of sharing the workspace and collaborate with humans or other robots to perform complex tasks. These robots often operate in an unstructured environment, whereby they need sensors and algorithms to get information about environment changes. Advanced vision and control techniques have been analyzed to evaluate their performance and their applicability to industrial tasks. Then, some selected techniques have been applied for the first time to an industrial context. A Peg-in-Hole task has been chosen as first case study, since it has been extensively studied but still remains challenging: it requires accuracy both in the determination of the hole poses and in the robot positioning. Two solutions have been developed and tested. Experimental results have been discussed to highlight the advantages and disadvantages of each technique. Grasping partially known objects in unstructured environments is one of the most challenging issues in robotics. It is a complex task and requires to address multiple subproblems, in order to be accomplished, including object localization and grasp pose detection. Also for this class of issues some vision techniques have been analyzed. One of these has been adapted to be used in industrial scenarios. Moreover, as a second case study, a robot-to-robot object handover task in a partially structured environment and in the absence of explicit communication between the robots has been developed and validated. Finally, the two case studies have been integrated in two real industrial setups to demonstrate the applicability of the strategies to solving industrial problems

    Task Oriented Programming and Service Algorithms for Smart Robotic Cells

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    L'abstract è presente nell'allegato / the abstract is in the attachmen
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