Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints

Physical human-robot collaboration (pHRC) requires both compliance and safety guarantees since robots coordinate with human actions in a shared workspace. This paper presents a novel fixed-time adaptive neural control methodology for handling time-varying workspace constraints that occur in physical human-robot collaboration while also guaranteeing compliance during intended force interactions. The proposed methodology combines the benefits of compliance control, time-varying integral barrier Lyapunov function (TVIBLF) and fixed-time techniques, which not only achieve compliance during physical contact with human operators but also guarantee time-varying workspace constraints and fast tracking error convergence without any restriction on the initial conditions. Furthermore, a neural adaptive control law is designed to compensate for the unknown dynamics and disturbances of the robot manipulator such that the proposed control framework is overall fixed-time converged and capable of online learning without any prior knowledge of robot dynamics and disturbances. The proposed approach is finally validated on a simulated two-link robot manipulator. Simulation results show that the proposed controller is superior in the sense of both tracking error and convergence time compared with the existing barrier Lyapunov functions based controllers, while simultaneously guaranteeing compliance and safety

Adaptive human force scaling via admittance control for physical human-robot interaction

The goal of this article is to design an admittance controller for a robot to adaptively change its contribution to a collaborative manipulation task executed with a human partner to improve the task performance. This has been achieved by adaptive scaling of human force based on her/his movement intention while paying attention to the requirements of different task phases. In our approach, movement intentions of human are estimated from measured human force and velocity of manipulated object, and converted to a quantitative value using a fuzzy logic scheme. This value is then utilized as a variable gain in an admittance controller to adaptively adjust the contribution of robot to the task without changing the admittance time constant. We demonstrate the benefits of the proposed approach by a pHRI experiment utilizing Fitts’ reaching movement task. The results of the experiment show that there is a) an optimum admittance time constant maximizing the human force amplification and b) a desirable admittance gain profile which leads to a more effective co-manipulation in terms of overall task performance.WOS:000731146900006Scopus - Affiliation ID: 60105072Q2ArticleUluslararası işbirliği ile yapılan - EVETOctober2021YÖK - 2021-22Eki

Human–Robot Role Arbitration via Differential Game Theory

The industry needs controllers that allow smooth and natural physical Human-Robot Interaction (pHRI) to make production scenarios more flexible and user-friendly. Within this context, particularly interesting is Role Arbitration, which is the mechanism that assigns the role of the leader to either the human or the robot. This paper investigates Game-Theory (GT) to model pHRI, and specifically, Cooperative Game Theory (CGT) and Non-Cooperative Game Theory (NCGT) are considered. This work proposes a possible solution to the Role Arbitration problem and defines a Role Arbitration framework based on differential game theory to allow pHRI. The proposed method can allow trajectory deformation according to human will, avoiding reaching dangerous situations such as collisions with environmental features, robot joints and workspace limits, and possibly safety constraints. Three sets of experiments are proposed to evaluate different situations and compared with two other standard methods for pHRI, the Impedance Control, and the Manual Guidance. Experiments show that with our Role Arbitration method, different situations can be handled safely and smoothly with a low human effort. In particular, the performances of the IMP and MG vary according to the task. In some cases, MG performs well, and IMP does not. In some others, IMP performs excellently, and MG does not. The proposed Role Arbitration controller performs well in all the cases, showing its superiority and generality. The proposed method generally requires less force and ensures better accuracy in performing all tasks than standard controllers. Note to Practitioners—This work presents a method that allows role arbitration for physical Human-Robot Interaction, motivated by the need to adjust the role of leader/follower in a shared task according to the specific phase of the task or the knowledge of one of the two agents. This method suits applications such as object co-transportation, which requires final precise positioning but allows some trajectory deformation on the fly. It can also handle situations where the carried obstacle occludes human sight, and the robot helps the human to avoid possible environmental obstacles and position the objects at the target pose precisely. Currently, this method does not consider external contact, which is likely to arise in many situations. Future studies will investigate the modeling and detection of external contacts to include them in the interaction models this work addresses

Neural networks enhanced adaptive admittance control of optimized robot-environment interaction

In this paper, an admittance adaptation method has been developed for robots to interact with unknown environments. The environment to be interacted with is modeled as a linear system. In the presence of the unknown dynamics of environments, an observer in robot joint space is employed to estimate the interaction torque, and admittance control is adopted to regulate the robot behavior at interaction points. An adaptive neural controller using the radial basis function is employed to guarantee trajectory tracking. A cost function that defines the interaction performance of torque regulation and trajectory tracking is minimized by admittance adaptation. To verify the proposed method, simulation studies on a robot manipulator are conducted

Collaborative human-machine interfaces for mobile manipulators.

The use of mobile manipulators in service industries as both agents in physical Human Robot Interaction (pHRI) and for social interactions has been on the increase in recent times due to necessities like compensating for workforce shortages and enabling safer and more efficient operations amongst other reasons. Collaborative robots, or co-bots, are robots that are developed for use with human interaction through direct contact or close proximity in a shared space with the human users. The work presented in this dissertation focuses on the design, implementation and analysis of components for the next-generation collaborative human machine interfaces (CHMI) needed for mobile manipulator co-bots that can be used in various service industries. The particular components of these CHMI\u27s that are considered in this dissertation include: Robot Control: A Neuroadaptive Controller (NAC)-based admittance control strategy for pHRI applications with a co-bot. Robot state estimation: A novel methodology and placement strategy for using arrays of IMUs that can be embedded in robot skin for pose estimation in complex robot mechanisms. User perception of co-bot CHMI\u27s: Evaluation of human perceptions of usefulness and ease of use of a mobile manipulator co-bot in a nursing assistant application scenario. To facilitate advanced control for the Adaptive Robotic Nursing Assistant (ARNA) mobile manipulator co-bot that was designed and developed in our lab, we describe and evaluate an admittance control strategy that features a Neuroadaptive Controller (NAC). The NAC has been specifically formulated for pHRI applications such as patient walking. The controller continuously tunes weights of a neural network to cancel robot non-linearities, including drive train backlash, kinematic or dynamic coupling, variable patient pushing effort, or slope surfaces with unknown inclines. The advantage of our control strategy consists of Lyapunov stability guarantees during interaction, less need for parameter tuning and better performance across a variety of users and operating conditions. We conduct simulations and experiments with 10 users to confirm that the NAC outperforms a classic Proportional-Derivative (PD) joint controller in terms of resulting interaction jerk, user effort, and trajectory tracking error during patient walking. To tackle complex mechanisms of these next-gen robots wherein the use of encoder or other classic pose measuring device is not feasible, we present a study effects of design parameters on methods that use data from Inertial Measurement Units (IMU) in robot skins to provide robot state estimates. These parameters include number of sensors, their placement on the robot, as well as noise properties on the quality of robot pose estimation and its signal-to-noise Ratio (SNR). The results from that study facilitate the creation of robot skin, and in order to enable their use in complex robots, we propose a novel pose estimation method, the Generalized Common Mode Rejection (GCMR) algorithm, for estimation of joint angles in robot chains containing composite joints. The placement study and GCMR are demonstrated using both Gazebo simulation and experiments with a 3-DoF robotic arm containing 2 non-zero link lengths, 1 revolute joint and a 2-DoF composite joint. In addition to yielding insights on the predicted usage of co-bots, the design of control and sensing mechanisms in their CHMI benefits from evaluating the perception of the eventual users of these robots. With co-bots being only increasingly developed and used, there is a need for studies into these user perceptions using existing models that have been used in predicting usage of comparable technology. To this end, we use the Technology Acceptance Model (TAM) to evaluate the CHMI of the ARNA robot in a scenario via analysis of quantitative and questionnaire data collected during experiments with eventual uses. The results from the works conducted in this dissertation demonstrate insightful contributions to the realization of control and sensing systems that are part of CHMI\u27s for next generation co-bots

Neural-learning-based force sensorless admittance control for robots with input deadzone

This paper presents a neural networks based admittance control scheme for robotic manipulators when interacting with the unknown environment in the presence of the actuator deadzone without needing force sensing. A compliant behaviour of robotic manipulators in response to external torques from the unknown environment is achieved by admittance control. Inspired by broad learning system (BLS), a flatted neural network structure using Radial Basis Function (RBF) with incremental learning algorithm is proposed to estimate the external torque, which can avoid retraining process if the system is modelled insufficiently. To deal with uncertainties in the robot system, an adaptive neural controller with dynamic learning framework is developed to ensure the tracking performance. Experiments on the Baxter robot have been implemented to test the effectiveness of the proposed method

Active Training and Assistance Device for an Individually Adaptable Strength and Coordination Training

Das Altern der Weltbevölkerung, insbesondere in der westlichen Welt, stellt die Menschheit vor eine große Herausforderung. Zu erwarten sind erhebliche Auswirkungen auf den Gesundheitssektor, der im Hinblick auf eine steigende Anzahl von Menschen mit altersbedingtem körperlichem und kognitivem Abbau und dem damit erhöhten Bedürfnis einer individuellen Versorgung vor einer großen Aufgabe steht. Insbesondere im letzten Jahrhundert wurden viele wissenschaftliche Anstrengungen unternommen, um Ursache und Entwicklung altersbedingter Erkrankungen, ihr Voranschreiten und mögliche Behandlungen, zu verstehen. Die derzeitigen Modelle zeigen, dass der entscheidende Faktor für die Entwicklung solcher Krankheiten der Mangel an sensorischen und motorischen Einflüssen ist, diese wiederum sind das Ergebnis verringerter Mobilität und immer weniger neuer Erfahrungen. Eine Vielzahl von Studien zeigt, dass erhöhte körperliche Aktivität einen positiven Effekt auf den Allgemeinzustand von älteren Erwachsenen mit leichten kognitiven Beeinträchtigungen und den Menschen in deren unmittelbarer Umgebung hat. Diese Arbeit zielt darauf ab, älteren Menschen die Möglichkeit zu bieten, eigenständig und sicher ein individuelles körperliches Training zu absolvieren. In den letzten zwei Jahrzehnten hat die Forschung im Bereich der robotischen Bewegungsassistenten, auch Smarte Rollatoren genannt, den Fokus auf die sensorische und kognitive Unterstützung für ältere und eingeschränkte Personen gesetzt. Durch zahlreiche Bemühungen entstand eine Vielzahl von Ansätzen zur Mensch-Rollator-Interaktion, alle mit dem Ziel, Bewegung und Navigation innerhalb der Umgebung zu unterstützen. Aber trotz allem sind Trainingsmöglichkeiten zur motorischen Aktivierung mittels Smarter Rollatoren noch nicht erforscht. Im Gegensatz zu manchen Smarten Rollatoren, die den Fokus auf Rehabilitationsmöglichkeiten für eine bereits fortgeschrittene Krankheit setzen, zielt diese Arbeit darauf ab, kognitive Beeinträchtigungen in einem frühen Stadium soweit wie möglich zu verlangsamen, damit die körperliche und mentale Fitness des Nutzers so lang wie möglich aufrechterhalten bleibt. Um die Idee eines solchen Trainings zu überprüfen, wurde ein Prototyp-Gerät namens RoboTrainer-Prototyp entworfen, eine mobile Roboter-Plattform, die mit einem zusätzlichen Kraft-Momente-Sensor und einem Fahrradlenker als Eingabe-Schnittstelle ausgestattet wurde. Das Training beinhaltet vordefinierte Trainingspfade mit Markierungen am Boden, entlang derer der Nutzer das Gerät navigieren soll. Der Prototyp benutzt eine Admittanzgleichung, um seine Geschwindigkeit anhand der Eingabe des Nutzers zu berechnen. Desweiteren leitet das Gerät gezielte Regelungsaktionen bzw. Verhaltensänderungen des Roboters ein, um das Training herausfordernd zu gestalten. Die Pilotstudie, die mit zehn älteren Erwachsenen mit beginnender Demenz durchgeführt wurde, zeigte eine signifikante Steigerung ihrer Interaktionsfähigkeit mit diesem Gerät. Sie bewies ebenfalls den Nutzen von Regelungsaktionen, um die Komplexität des Trainings ständig neu anzupassen. Obwohl diese Studie die Durchführbarkeit des Trainings zeigte, waren Grundfläche und mechanische Stabilität des RoboTrainer-Prototyps suboptimal. Deswegen fokussiert sich der zweite Teil dieser Arbeit darauf, ein neues Gerät zu entwerfen, um die Nachteile des Prototyps zu beheben. Neben einer erhöhten mechanischen Stabilität, ermöglicht der RoboTrainer v2 eine Anpassung seiner Grundfläche. Dieses spezifische Merkmal der Smarten Rollatoren dient vor allem dazu, die Unterstützungsfläche für den Benutzer anzupassen. Das ermöglicht einerseits ein agiles Training mit gesunden Personen und andererseits Rehabilitations-Szenarien bei Menschen, die körperliche Unterstützung benötigen. Der Regelungsansatz für den RoboTrainer v2 erweitert den Admittanzregler des Prototypen durch drei adaptive Strategien. Die erste ist die Anpassung der Sensitivität an die Eingabe des Nutzers, abhängig von der Stabilität des Nutzer-Rollater-Systems, welche Schwankungen verhindert, die dann passieren können, wenn die Hände des Nutzers versteifen. Die zweite Anpassung beinhaltet eine neuartige nicht-lineare, geschwindigkeits-basierende Änderung der Admittanz-Parameter, um die Wendigkeit des Rollators zu erhöhen. Die dritte Anpassung erfolgt vor dem eigentlichen Training in einem Parametrierungsprozess, wo nutzereigene Interaktionskräfte gemessen werden, um individuelle Reglerkonstanten fein abzustimmen und zu berechnen. Die Regelungsaktionen sind Verhaltensänderungen des Gerätes, die als Bausteine für unterstützende und herausfordernde Trainingseinheiten mit dem RoboTrainer dienen. Sie nutzen das virtuelle Kraft-Feld-Konzept, um die Bewegung des Gerätes in der Trainingsumgebung zu beeinflussen. Die Bewegung des RoboTrainers wird in der Gesamtumgebung durch globale oder, in bestimmten Teilbereichen, durch räumliche Aktionen beeinflusst. Die Regelungsaktionen erhalten die Absicht des Nutzers aufrecht, in dem sie eine unabhängige Admittanzdynamik implementieren, um deren Einfluss auf die Geschwindigkeit des RoboTrainers zu berechnen. Dies ermöglicht die entscheidende Trennung von Reglerzuständen, um während des Trainings passive und sichere Interaktionen mit dem Gerät zu erreichen. Die oben genannten Beiträge wurden getrennt ausgewertet und in zwei Studien mit jeweils 22 bzw. 13 jungen, gesunden Erwachsenen untersucht. Diese Studien ermöglichen einen umfassenden Einblick in die Zusammenhänge zwischen unterschiedlichen Funktionalitäten und deren Einfluss auf die Nutzer. Sie bestätigen den gesamten Ansatz, sowie die gemachten Vermutungen im Hinblick auf die Gestaltung einzelner Teile dieser Arbeit. Die Einzelergebnisse dieser Arbeit resultieren in einem neuartigen Forschungsgerät für physische Mensch-Roboter-Interaktionen während des Trainings mit Erwachsenen. Zukünftige Forschungen mit dem RoboTrainer ebnen den Weg für Smarte Rollatoren als Hilfe für die Gesellschaft im Hinblick auf den bevorstehenden demographischen Wandel

An Integrated Decision Making Approach for Adaptive Shared Control of Mobility Assistance Robots

© 2016, Springer Science+Business Media Dordrecht. Mobility assistance robots provide support to elderly or patients during walking. The design of a safe and intuitive assistance behavior is one of the major challenges in this context. We present an integrated approach for the context-specific, on-line adaptation of the assistance level of a rollator-type mobility assistance robot by gain-scheduling of low-level robot control parameters. A human-inspired decision-making model, the drift-diffusion Model, is introduced as the key principle to gain-schedule parameters and with this to adapt the provided robot assistance in order to achieve a human-like assistive behavior. The mobility assistance robot is designed to provide (a) cognitive assistance to help the user following a desired path towards a predefined destination as well as (b) sensorial assistance to avoid collisions with obstacles while allowing for an intentional approach of them. Further, the robot observes the user long-term performance and fatigue to adapt the overall level of (c) physical assistance provided. For each type of assistance a decision-making problem is formulated that affects different low-level control parameters. The effectiveness of the proposed approach is demonstrated in technical validation experiments. Moreover, the proposed approach is evaluated in a user study with 35 elderly persons. Obtained results indicate that the proposed gain-scheduling technique incorporating ideas of human decision-making models shows a general high potential for the application in adaptive shared control of mobility assistance robots