Modeling of physical human–robot interaction : admittance controllers applied to intelligent assist devices with large payload

Enhancement of human performance using an intelligent assist device is becoming more common. In order to achieve effective augmentation of human capacity, cooperation between human and robot must be safe and very intuitive. Ensuring such collaboration remains a challenge, especially when admittance control is used. This paper addresses the issues of transparency and human perception coming from vibration in admittance control schemes. Simulation results obtained with our suggested improved model using an admittance controller are presented, then four models using transfer functions are discussed in detail and evaluated as a means of simulating physical human–robot interaction using admittance control. The simulation and experimental results are then compared in order to assess the validity and limitations of the proposed models in the case of a four-degree-of-freedom intelligent assist device designed for large payload

Active stability observer using artificial neural network for intuitive physical human–robot interaction

Physical human-robot interaction may present an obstacle to transparency and operations’ intuitiveness. This barrier could occur due to the vibrations caused by a stiff environment interacting with the robotic mechanisms. In this regard, this paper aims to deal with the aforementioned issues while using an observer and an adaptive gain controller. The adaptation of the gain loop should be performed in all circumstances in order to maintain operators’ safety and operations’ intuitiveness. Hence, two approaches for detecting and then reducing vibrations will be introduced in this study as follows: 1) a statistical analysis of a sensor signal (force and velocity) and 2) a multilayer perceptron artificial neural network capable of compensating the first approach for ensuring vibrations identification in real time. Simulations and experimental results are then conducted and compared in order to evaluate the validity of the suggested approaches in minimizing vibrations

Identification of Haptic Based Guiding Using Hard Reins

This paper presents identifications of human-human interaction in which one person with limited auditory and visual perception of the environment (a follower) is guided by an agent with full perceptual capabilities (a guider) via a hard rein along a given path. We investigate several identifications of the interaction between the guider and the follower such as computational models that map states of the follower to actions of the guider and the computational basis of the guider to modulate the force on the rein in response to the trust level of the follower. Based on experimental identification systems on human demonstrations show that the guider and the follower experience learning for an optimal stable state-dependent novel 3rd and 2nd order auto-regressive predictive and reactive control policies respectively. By modeling the follower's dynamics using a time varying virtual damped inertial system, we found that the coefficient of virtual damping is most appropriate to explain the trust level of the follower at any given time. Moreover, we present the stability of the extracted guiding policy when it was implemented on a planar 1-DoF robotic arm. Our findings provide a theoretical basis to design advanced human-robot interaction algorithms applicable to a variety of situations where a human requires the assistance of a robot to perceive the environment

Evaluation of human-robot object co-manipulation under robot impedance control

The human-robot collaboration is a promising and challeng- ing field of robotics research. One of the main collaboration tasks is the object co-manipulation where the human and robot are in a continuous physical interaction and forces exerted must be handled. This involves some issues known in robotics as physical Human-Robot Interaction (pHRI), where human safety and interaction comfort are required. Moreover, a definition of interaction quality metrics would be relevant. In the current work, the assessment of Human-Robot object co-manipulation task was explored through the proposed metrics of interaction quality, based on human forces throughout the movement. This analysis is based on co-manipulation of objects with different dynamical properties (weight and inertia), with and without including these properties knowledge in the robot control law. Here, the human is a leader of task and the robot the follower without any information of the human trajectory and movement profile. For the robot control law, a well-known impedance control was applied on a 7-dof Kuka LBR iiwa 14 R820 robot. Results show that the consideration of object dynamical properties in the robot control law is crucial for a good and more comfortable interaction. Besides, human efforts are more significant with a higher no-considered weight, whereas it remains stable when these weights were considered

Doctor of Philosophy

dissertationHumans generally have difficulty performing precision tasks with their unsupported hands. To compensate for this difficulty, people often seek to support or rest their hand and arm on a fixed surface. However, when the precision task needs to be performed over a workspace larger than what can be reached from a fixed position, a fixed support is no longer useful. This dissertation describes the development of the Active Handrest, a device that expands its user's dexterous workspace by providing ergonomic support and precise repositioning motions over a large workspace. The prototype Active Handrest is a planar computer-controlled support for the user's hand and arm. The device can be controlled through force input from the user, position input from a grasped tool, or a combination of inputs. The control algorithm of the Active Handrest converts the input(s) into device motions through admittance control where the device's desired velocity is calculated proportionally to the input force or its equivalent. A robotic 2-axis admittance device was constructed as the initial Planar Active Handrest, or PAHR, prototype. Experiments were conducted to optimize the device's control input strategies. Large workspace shape tracing experiments were used to compare the PAHR to unsupported, fixed support, and passive moveable support conditions. The Active Handrest was found to reduce task error and provide better speedaccuracy performance. Next, virtual fixture strategies were explored for the device. From the options considered, a virtual spring fixture strategy was chosen based on its effectiveness. An experiment was conducted to compare the PAHR with its virtual fixture strategy to traditional virtual fixture techniques for a grasped stylus. Virtual fixtures implemented on the Active Handrest were found to be as effective as fixtures implemented on a grasped tool. Finally, a higher degree-of-freedom Enhanced Planar Active Handrest, or E-PAHR, was constructed to provide support for large workspace precision tasks while more closely following the planar motions of the human arm. Experiments were conducted to investigate appropriate control strategies and device utility. The E-PAHR was found to provide a skill level equal to that of the PAHR with reduced user force input and lower perceived exertion

Operator Impedance During Physical Human-Robot Interaction: Estimation and Validation

As the frequency and complexity of physical human-robot interaction (pHRI) increases, so does the need to understand the dynamics of this coupled system in real-time. For haptic displays, which provide information to our senses of touch and proprioception, information regarding the human impedance and intent can drastically improve transparency while maintaining operator safety. Numerous online impedance estimators have been proposed in the literature which make continuous approximations of the coupled dynamics available online, obviating the need for perturbations or cumbersome sensors. However, character- izing and validating the performance of these estimators for pHRI is challenging, since it requires precise knowledge of known, time-varying reference impedances. This thesis explores the characterization of online impedance estimators using two serial manipulators (known as the BURT system), coupled at their end effectors, to physically simulate pHRI. One acts as the human, displaying a reference impedance relative to a desired trajectory, while the other runs the estimation algorithm and tracks a perturbing trajectory. Several least-squared impedance estimation strategies from the literature are validated on the experimental setup, demonstrating its efficacy and utility. The importance of accounting for the desired human trajectory is highlighted, and future research to apply results from the field of human motor control is proposed. A bimanual circle-drawing sensorimotor experiment was also conducted with the BURT systsem. Visual feedback on the task performance was withdrawn from one hand at a time and relationships between subject handedness, visual feedback, tracing speed, and circu- larity were studied. Results show that the non-dominant hand tends to trace less circular trajectories than the dominant hand while both are visible, but inconsistent differences in circularity are present across participants when comparing the performance of each hand in its invisible condition. Both hands showed higher tracing speed when only a single hand was receiving visual feedback, and traced more slowly in the free visual condition

Assessing Performance, Role Sharing, and Control Mechanisms in Human-Human Physical Interaction for Object Manipulation

abstract: Object manipulation is a common sensorimotor task that humans perform to interact with the physical world. The first aim of this dissertation was to characterize and identify the role of feedback and feedforward mechanisms for force control in object manipulation by introducing a new feature based on force trajectories to quantify the interaction between feedback- and feedforward control. This feature was applied on two grasp contexts: grasping the object at either (1) predetermined or (2) self-selected grasp locations (“constrained” and “unconstrained”, respectively), where unconstrained grasping is thought to involve feedback-driven force corrections to a greater extent than constrained grasping. This proposition was confirmed by force feature analysis. The second aim of this dissertation was to quantify whether force control mechanisms differ between dominant and non-dominant hands. The force feature analysis demonstrated that manipulation by the dominant hand relies on feedforward control more than the non-dominant hand. The third aim was to quantify coordination mechanisms underlying physical interaction by dyads in object manipulation. The results revealed that only individuals with worse solo performance benefit from interpersonal coordination through physical couplings, whereas the better individuals do not. This work showed that naturally emerging leader-follower roles, whereby the leader in dyadic manipulation exhibits significant greater force changes than the follower. Furthermore, brain activity measured through electroencephalography (EEG) could discriminate leader and follower roles as indicated power modulation in the alpha frequency band over centro-parietal areas. Lastly, this dissertation suggested that the relation between force and motion (arm impedance) could be an important means for communicating intended movement direction between biological agents.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Kontextsensitive Körperregulierung für redundante Roboter

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

Conception, réalisation et évaluation d’une commande robotique interactive et d’un guide haptique interfacé par la technologie réalité augmentée dédiés à l’interaction physique humain-robot

Depuis quelques décennies, nous témoignons une progression significative des systèmes interactifs tels que les robots agissant en coopération avec l’humain. Ces derniers ont fait leurs preuves dans l’amélioration de la compétitivité des industries. Ceci est rendu possible grâce à leur potentiel à augmenter les performances humaines et à favoriser une plus grande flexibilité tout en laissant le processus décisionnel à l’opérateur. Une telle amélioration est obtenue grâce à une synergie efficace entre l’intelligence des humains, leurs connaissances, leurs dextérités et la force des robots industriels, leurs endurances et leurs précisions. En outre, l’interactivité robotique permet d’assister les humains dans des tâches dangereuses et difficiles. De plus, elle permet d’améliorer et d’éviter les postures inadéquates, pouvant provoquer des douleurs musculo-squelettiques, grâce à un ordonnancement optimal des activités de production et de fabrication. Ainsi, ces deux avantages pourraient réduire le développement des troubles musculo-squelettiques (TMS). D’ailleurs, l’utilisation d’un robot dans une cellule de travail hybride, dans le but de remplacer une tâche répétitive caractérisée par une posture contraignante, pourrait avoir l’avantage de réduire le développement des TMS grâce à un partage adapté des activités de production. Par conséquent, les travaux de ce projet de recherche sont encadrés par une grande problématique qui est la réduction des TMS, dus à des postures contraignantes, grâce à un robot interactif. En effet, les symptômes dus aux TMS constituent, aujourd’hui, l’une des questions les plus préoccupantes en santé et en sécurité au travail du fait de leur forte prévalence et de leurs conséquences tant sur la santé des individus que sur le fonctionnement des entreprises. D’ailleurs, d’après les statistiques, près de 15 % de l’ensemble des travailleurs actifs, au Québec, ont un TMS de longue durée. Toutefois, l’ajout d’un robot possède ses défis : une mauvaise Interaction physique Humain-Robot (IpHR), via un contact direct entre le robot et l’humain à travers un système de captation (par exemple une poignée instrumentée d’un capteur d’efforts à six degrés de liberté), peut générer des vibrations qui demeurent une source d’inconfort pour les opérateurs. En effet, une augmentation de la rigidité structurelle du bras humain peut occasionner un mouvement vibratoire du robot expliqué par le déplacement des pôles (c.-àd. de la dynamique dominante) près de l’axe imaginaire. Ce projet de recherche comporte deux parties. La première traite de deux approches visant à satisfaire une interaction humain-robot plus intuitive et plus sécuritaire tout en détectant et en minimisant les vibrations mécaniques qui pourraient être générées lors d’une telle interaction. La première approche consiste à détecter et à minimiser les vibrations par un observateur de vibrations de type analyse statistique. Cette dernière a été réalisée avec un signal électrique prélevé par le biais de deux capteurs de force et de vitesse qui sont localisés sur un mécanisme robotique à un degré de liberté lors d’une IpHR dans un contexte réel. La deuxième approche, quant à elle, consiste à concevoir et à développer un second observateur de vibrations actif de type réseau de neurones artificiels dans le but de détecter et de minimiser, en temps réel, les vibrations lors d’une IpHR. Ces algorithmes seront optimisés et comparés pour des fins de mise en oeuvre pratique. La deuxième partie de ce projet de recherche traite d’une mise en oeuvre d’une commande d’un mécanisme robotique à quatre degrés de liberté avec un système haptique virtuel, composé de deux objets virtuels interfacés par la réalité augmentée (RA) grâce aux lunettes Epson Moverio BT-200. Ce système vise à assister et à faciliter les tâches d’assemblages en industrie, surtout dans le cas de la présence d’un obstacle situé dans le champ visuel entre l’opérateur et les pièces à assembler. L’interaction avec ce système virtuel a été introduite, dans un premier temps, par le biais d’un dispositif haptique (le PHANToM Omni) dans le but de tester la plateforme d’assemblage en réalité augmentée. Dans les travaux futurs, le PHANToM Omni sera remplacé par un mécanisme parallèle entraîné par des câbles afin de simuler différents types de robot industriel. Dans cette recherche, le PHANToM permettra de télé-opérer l’effecteur d’un robot industriel simulé dans Robotic Operating System (ROS)