Improving human-robot interactivity for tele-operated industrial and service robot applications

In industrial robotics applications, teach pendant has been widely used by human operators to pre-define action trajectories for robot manipulators to execute as primitives. This hard-coding approach is only good for low-mix-highvolume jobs with sparse trajectory way-points. In this paper, we present a novel industrial robotic system designed for applications where human-robot interaction is key for efficient execution of actions such as high-mix-low-volume jobs. The proposed system comprises a robot manipulator that controls a tool (such as a soldering iron) to interact with the required workpiece, a networking server for remote tele-operation, and an integrated user interface that allows the human operator to better perceive the remote operation and to execute actions with greater ease. A user study is conducted to understand the merits of the proposed system. Results indicate that human can operate the system with ease and complete tasks more quickly and that the system can improve application efficiency

On the Value of Estimating Human Arm Stiffness during Virtual Teleoperation with Robotic Manipulators

Teleoperated robotic systems are widely spreading in multiple different fields, from hazardous environments exploration to surgery. In teleoperation, users directly manipulate a master device to achieve task execution at the slave robot side; this interaction is fundamental to guarantee both system stability and task execution performance. In this work, we propose a non-disruptive method to study the arm endpoint stiffness. We evaluate how users exploit the kinetic redundancy of the arm to achieve stability and precision during the execution of different tasks with different master devices. Four users were asked to perform two planar trajectories following virtual tasks using both a serial and a parallel link master device. Users' arm kinematics and muscular activation were acquired and combined with a user-specific musculoskeletal model to estimate the joint stiffness. Using the arm kinematic Jacobian, the arm end-point stiffness was derived. The proposed non-disruptive method is capable of estimating the arm endpoint stiffness during the execution of virtual teleoperated tasks. The obtained results are in accordance with the existing literature in human motor control and show, throughout the tested trajectory, a modulation of the arm endpoint stiffness that is affected by task characteristics and hand speed and acceleration

Autonomous Robotic Screening of Tubular Structures based only on Real-Time Ultrasound Imaging Feedback

Ultrasound (US) imaging is widely employed for diagnosis and staging of peripheral vascular diseases (PVD), mainly due to its high availability and the fact it does not emit radiation. However, high inter-operator variability and a lack of repeatability of US image acquisition hinder the implementation of extensive screening programs. To address this challenge, we propose an end-to-end workflow for automatic robotic US screening of tubular structures using only the real-time US imaging feedback. We first train a U-Net for real-time segmentation of the vascular structure from cross-sectional US images. Then, we represent the detected vascular structure as a 3D point cloud and use it to estimate the longitudinal axis of the target tubular structure and its mean radius by solving a constrained non-linear optimization problem. Iterating the previous processes, the US probe is automatically aligned to the orientation normal to the target tubular tissue and adjusted online to center the tracked tissue based on the spatial calibration. The real-time segmentation result is evaluated both on a phantom and in-vivo on brachial arteries of volunteers. In addition, the whole process is validated both in simulation and physical phantoms. The mean absolute radius error and orientation error ($\pm$ SD) in the simulation are $1.16\pm0.1~mm$ and $2.7\pm3.3^{\circ}$, respectively. On a gel phantom, these errors are $1.95\pm2.02~mm$ and $3.3\pm2.4^{\circ}$. This shows that the method is able to automatically screen tubular tissues with an optimal probe orientation (i.e. normal to the vessel) and at the same to accurately estimate the mean radius, both in real-time.Comment: Accepted for publication in IEEE Transactions on Industrial Electronics Video: https://www.youtube.com/watch?v=VAaNZL0I5i

A Review of Pneumatic Actuators Used for the Design of Medical Simulators and Medical Tools

International audienc

Konzeption und Entwicklung einer Applikation zur robotergestützten Ultraschallbildgebung

In dieser Bachelor-Thesis wird ein Ansatz zur robotergestützten Ultraschall-Bildgebun entwickelt. Es wird hierbei hauptsächlich die Kommunikation zwischen Roboter und Computer, sowie die Steuerung des Roboters, betrachtet. Die Bilderfassung und Darstellung ist nicht Teil dieser Arbeit. Ergebnis dieser Arbeit sind eine detaillierte Anforderungsanalyse, eine GUI zum Steuern der erstellten Roboterteilprogramme und das Roboterprogramm an sich. Auf Seiten des Roboters sind Programme für das Beibringen von Positionen, das Anfahren dieser und das Verfahren des Ultraschallkopfes an diesen (manuelle Steuerung mit einem Joystick) implementiert. Für eine Atembewegungskompensation sind Beispielprogramme erstellt worden. Die Kommunikation zwischen Roboter und Computer baut auf OpenIGTLink auf. Ein weiteres Ergebnis der Thesis ist die Architektur des Programms, die es ermöglicht, dem Roboter beliebig viele neue Befehle beizubringen

Towards the development of safe, collaborative robotic freehand ultrasound

The use of robotics in medicine is of growing importance for modern health services, as robotic systems have the capacity to improve upon human tasks, thereby enhancing the treatment ability of a healthcare provider. In the medical sector, ultrasound imaging is an inexpensive approach without the high radiation emissions often associated with other modalities, especially when compared to MRI and CT imaging respectively. Over the past two decades, considerable effort has been invested into freehand ultrasound robotics research and development. However, this research has focused on the feasibility of the application, not the robotic fundamentals, such as motion control, calibration, and contextual awareness. Instead, much of the work is concentrated on custom designed robots, ultrasound image generation and visual servoing, or teleoperation. Research based on these topics often suffer from important limitations that impede their use in an adaptable, scalable, and real-world manner. Particularly, while custom robots may be designed for a specific application, commercial collaborative robots are a more robust and economical solution. Otherwise, various robotic ultrasound studies have shown the feasibility of using basic force control, but rarely explore controller tuning in the context of patient safety and deformable skin in an unstructured environment. Moreover, many studies evaluate novel visual servoing approaches, but do not consider the practicality of relying on external measurement devices for motion control. These studies neglect the importance of robot accuracy and calibration, which allow a system to safely navigate its environment while reducing the imaging errors associated with positioning. Hence, while the feasibility of robotic ultrasound has been the focal point in previous studies, there is a lack of attention to what occurs between system design and image output. This thesis addresses limitations of the current literature through three distinct contributions. Given the force-controlled nature of an ultrasound robot, the first contribution presents a closed-loop calibration approach using impedance control and low-cost equipment. Accuracy is a fundamental requirement for high-quality ultrasound image generation and targeting. This is especially true when following a specified path along a patient or synthesizing 2D slices into a 3D ultrasound image. However, even though most industrial robots are inherently precise, they are not necessarily accurate. While robot calibration itself has been extensively studied, many of the approaches rely on expensive and highly delicate equipment. Experimental testing showed that this method is comparable in quality to traditional calibration using a laser tracker. As demonstrated through an experimental study and validated with a laser tracker, the absolute accuracy of a collaborative robot was improved to a maximum error of 0.990mm, representing a 58.4% improvement when compared to the nominal model. The second contribution explores collisions and contact events, as they are a natural by-product of applications involving physical human-robot interaction (pHRI) in unstructured environments. Robot-assisted medical ultrasound is an example of a task where simply stopping the robot upon contact detection may not be an appropriate reaction strategy. Thus, the robot should have an awareness of body contact location to properly plan force-controlled trajectories along the human body using the imaging probe. This is especially true for remote ultrasound systems where safety and manipulability are important elements to consider when operating a remote medical system through a communication network. A framework is proposed for robot contact classification using the built-in sensor data of a collaborative robot. Unlike previous studies, this classification does not discern between intended vs. unintended contact scenarios, but rather classifies what was involved in the contact event. The classifier can discern different ISO/TS 15066:2016 specific body areas along a human-model leg with 89.37% accuracy. Altogether, this contact distinction framework allows for more complex reaction strategies and tailored robot behaviour during pHRI. Lastly, given that the success of an ultrasound task depends on the capability of the robot system to handle pHRI, pure motion control is insufficient. Force control techniques are necessary to achieve effective and adaptable behaviour of a robotic system in the unstructured ultrasound environment while also ensuring safe pHRI. While force control does not require explicit knowledge of the environment, to achieve an acceptable dynamic behaviour, the control parameters must be tuned. The third contribution proposes a simple and effective online tuning framework for force-based robotic freehand ultrasound motion control. Within the context of medical ultrasound, different human body locations have a different stiffness and will require unique tunings. Through real-world experiments with a collaborative robot, the framework tuned motion control for optimal and safe trajectories along a human leg phantom. The optimization process was able to successfully reduce the mean absolute error (MAE) of the motion contact force to 0.537N through the evolution of eight motion control parameters. Furthermore, contextual awareness through motion classification can offer a framework for pHRI optimization and safety through predictive motion behaviour with a future goal of autonomous pHRI. As such, a classification pipeline, trained using the tuning process motion data, was able to reliably classify the future force tracking quality of a motion session with an accuracy of 91.82 %

Design and validation of a system for controlling a robot for 3D ultrasound scanning of the lower limbs

Peripheral arterial disease (PAD) is a common circulatory problem featured by arterial narrowing or stenosis, usually in the lower limbs (i.e. legs). Without sufficient blood supply, in the case of PAD, the patient may suffer from intermittent claudication, or even require an amputation. Due to the PAD’s high prevalence yet low public awareness in the early stages, its diagnosis becomes very important. Among the most common medical imaging technologies in PAD diagnosis, the ultrasound probe has the advantages of lower cost and non-radiation. Traditional ultrasound scanning is conducted by sonographers and it causes musculoskeletal disorders in the operators. In addition, the data obtained from the manual operation are unable for the three-dimensional reconstruction of the artery needed for further study. Medical ultrasound robots release sonographers from routine lifting strain and provide accurate data for three-dimensional reconstruction. However, most existing medical ultrasound robots are designed for other purposes, and are unsuited to PAD diagnosis in the lower limbs. In this study, we present a novel medical ultrasound robot designed for PAD diagnosis in the lower limbs. The robot platform and the system setup are illustrated. Its forward and inverse kinematic models are solved by decomposing a complex parallel robot into several simple assemblies. Singularity issues and workspace are also discussed. Robots need to meet certain accuracy requirements to perform dedicated tasks. Our robot is calibrated by direct measurement with a laser tracker. The calibration method used is easy to implement without requiring knowledge of advanced calibration or heavy computation. The calibration result shows that, as an early prototype, the robot has noticeable errors in manufacturing and assembling. The implemented calibration method greatly improves the robot's accuracy. A force control design is essential when the robot needs to interact with an object/environment. Variable admittance controllers are implemented to adapt the variable stiffness encountered in human-robot interaction. An intuitive implementation of the passivity theory is proposed to ensure that the admittance model possesses a passivity property. Finally, experiments involving human interaction demonstrate the effectiveness of the proposed control design