Browsing by Author "Peternel, L."

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Assessments on the improved modelling for pneumatic artificial muscle actuators
(IEEE, 2015) Peternel, L.; Uğurlu, Regaip Barkan; Babic, J.; Morimoto, J.; Mechanical Engineering; UĞURLU, Regaip Barkan
In this paper, we present an analysis regarding the pneumatic air muscle modelling, with a particular emphasis on the exoskeleton robot control. We propose two calibration approaches for obtaining the model identification data. We used the measurement data acquired from the proposed approaches to identify different mathematical models of pneumatic muscles. These models specified the necessary muscle control pressure for the desired muscle force at a given muscle length value. We compared the performance between the different types of models identified by either of the calibration method. The identified model with the lowest validation error was implemented in pneumatic muscle control for an elbow exoskeleton system. As a result, the system exhibited satisfactory torque and position control tasks, adequately validating the proposed approach.
Open Access
Challenges and solutions for application and wider adoption of wearable robots
(Cambridge University Press, 2021) Babič, J.; Laffranchi, M.; Tessari, F.; Verstraten, T.; Novak, D.; Šarabon, N.; Uğurlu, Regaip Barkan; Peternel, L.; Torricelli, D.; Veneman, J. F.; Mechanical Engineering; UĞURLU, Regaip Barkan
The science and technology of wearable robots are steadily advancing, and the use of such robots in our everyday life appears to be within reach. Nevertheless, widespread adoption of wearable robots should not be taken for granted, especially since many recent attempts to bring them to real-life applications resulted in mixed outcomes. The aim of this article is to address the current challenges that are limiting the application and wider adoption of wearable robots that are typically worn over the human body. We categorized the challenges into mechanical layout, actuation, sensing, body interface, control, human–robot interfacing and coadaptation, and benchmarking. For each category, we discuss specific challenges and the rationale for why solving them is important, followed by an overview of relevant recent works. We conclude with an opinion that summarizes possible solutions that could contribute to the wider adoption of wearable robots.
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A shared control method for online human-in-the-loop robot learning based on Locally Weighted Regression
(IEEE, 2016) Peternel, L.; Öztop, Erhan; Babič, J.; Computer Science; ÖZTOP, Erhan
We propose a novel method that arbitrates the control between the human and the robot actors in a teaching-by-demonstration setting to form synergy between the two and facilitate effective skill synthesis on the robot. We employed the human-in-the-loop teaching paradigm to teleoperate and demonstrate a complex task execution to the robot in real-time. As the human guides the robot to perform the task, the robot obtains the skill online during the demonstration. To encode the robotic skill we employed Locally Weighted Regression that fits local models to specific state region of the task based on the human demonstration. If the robot is in the state region where no local models exist, the control over the robotic mechanism is given to the human to perform the teaching. When local models are gradually obtained in that region, the control is given to the robot so that the human can examine its performance already during the demonstration stage, and take actions accordingly. This enables a co-adaptation between the agents and contributes to a faster and more efficient teaching. As a proof-of-concept, we realised the proposed robot teaching system on a haptic robot with the task of generation of a desired vertical force on a horizontal plane with unknown stiffness properties.
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Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach
(Springer Science+Business Media, 2014-01) Peternel, L.; Petric, T.; Öztop, Erhan; Babic, J.; Computer Science; ÖZTOP, Erhan
We propose an approach to efﬁciently teach robots how to perform dynamic anipulation tasks in cooperation with a human partner. The approach utilises human sensorimotor learning ability where the human tutor controls the robot through a multi-modal interface to make it perform the desired task. During the tutoring, the robot simultaneously learns the action policy of the tutor and through time gains full autonomy. We demonstrate our approach by an experiment where we taught a robot how to perform a wood sawing task with a human partner using a two-person crosscut saw. The challenge of this experiment is that it requires precise coordination of the robot’s motion and complianceaccording to the partner’s actions. To transfer the sawing skill from the tutor to the robot we used Locally Weighted Regression for trajectory generalisation, and adaptive oscillators for adaptation of the robot to the partner’s motion.