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1 | Issue 6
About the Issue
Contents
Special Interest Articles
Editorial 2
From the Student’s Desk 3
SMART-E Student Activities 15
Individual Highlights
Saber Mahboubi 3
Yasmin Ansari 4
Stefania Russo 5
Andrea Giusti 6
Alexander Bousaid 7
Aaron Pereira 8
Esra Icer 9
Mateo Leco 10
Roy Assaf 11
Syed Taimoor Shah 12
Martijn Zeestraten 13
Stefano Toxiri 14
SMART-E, the Marie Skłodowska-Curie Initial Training Network (ITN) for
Sustainable Manufacturing through Advanced Robotics Training in Europe
aims to revolutionize manufacturing environments and eventually make a
contribution towards Industry 4.0 through the development of
technologies focused on three research thematic areas, namely,
“Dexterous, Soft and Compliant Robotics in Manufacturing”,
“Reconfigurable and Logistics Robotics”, and “Safety and Human-Robot
Interaction and Cooperation”. The project has now entered within its final
year and the research activities of the 12 Early Stage Researchers (ESRs)
have gained an exciting momentum with results being patented or
published in renowned journals, conferences, and book chapters including
Transactions in Control Systems Technology, IEEE Robotics and
Automation Letters, Soft Robotics Journal, ICRA 2017, IROS 2017, etc.
Furthermore, there is an ongoing effort to prepare online tutorials,
lectures, and prototypes in order to reach out to a larger community. In
addition to this, their experience in different industrial/academic contexts
and public events is growing, demonstrating their capabilities and skills.
In the sixth issue of the SMART-E Newsletter, the students share their
research related activities and progress over the past six months.
Additional information reports the major training events organized in the
last few months. In this concluding newsletter, the highlights form the
final conference for the SMART-E project are also provided.
October 2017, Issue 6
2 | Issue 6
Editorial
SMART-E (Sustainable Manufacturing through Advanced robotic Technologies) is a Marie-Curie
Initial Training Network (ITN) that aims to contribute towards the next industrial revolution,
Industry 4.0. The project is composed of 12 early stage researchers (ESRs); 2 experienced researchers
(ERs); 7 academic partners with world-class expertise in robotics, autonomous systems, and
advanced manufacturing; and number of reknowned industrial partners including BMW, RURobots,
Marel, AGCo, Rolls Royce, etc.
The results of the overall three-year project were disseminated in a two-day conference held in Pisa,
Italy on September 7-8 2017. It comprised of a total of four sessions (one morning and one afternoon)
where a single session included talks from the ESRs and industrial partners. This was held in
collaboration with the International Robot Festival 2017 where major institutions/projects/companies
from the Tuscany region came together to share the major breakthroughs in the robotic domain.
Consequently, it was an event open to the public. The program was also attended by a European
Comission ambassador in order to evaluate the outcomes of the project. The presentations of the
conference are intende d to be compiled to formulate a book for the benefit of the larger community.
3 | Issue 6
From the Student’s Desk
Object Slip Preventing Sliding Mode Control
3
This section provides a deeper look into the
experiences, activities, and progress of each
Early Stage Researcher over the past six months.
Further information can be found on their
personalized web-pages on the SMART-E
website at:
http://www.smart-e-mariecurie.eu/
Saber Mahboubi-Hyderabad
Host Institution:
University of Salford
Supervisor:
Prof. Samia Nefti-Meziani
Email:
Grasping an object in presence of unpredictable external forces and/or lack of precise estimation of the geometry and mass distribution of the object itself represents a significant challenge. The main problem is given by the estimation of the exact gripping force to apply that prevents slippage whilst not damaging or deforming the object. We have addresses this challenge first of all by looking for a control architecture that guarantees compensation of external disturbances and unmodelled/unknown characteristics of grasped objects. We found that a second order sliding mode control in combination with feedback of gripping force and slippage can provide the robustness required. An optical flow sensors used to detect slippage and the torque readings taken directly from servo motors control output allow for the system to perform well with a wide variety of grasped objects in pick-and-place tasks characterized by trajectories with steep acceleration and deceleration ramps. In order to reduce the chatter from the control response, the discontinuous switching function was changed with a gradient saturation function. A Reinforcement Learning (RL) based adaptive gradient saturation function established a trade-off between control accuracy and chattering response. The collected experimental results show that our adaptive sliding mode control was able to find optimal value for the saturation function, in order to decrease the chatter with minimum effect on control response. Figure. 1 depicts some of sequences of the robot motion for the given trajectory explained before.
Fig. 1. The robot is following the given motion trajectory, characterised by
20 via points with steep ramps of acceleration and deceleration
4 | Issue 6
Octopus-based Technologies for Manipulation in Manufacturing
Manipulators made up of soft materials exhibit intrinsic compliance which
can be exploited for safe human-robot interaction, proving highly useful to
revolutionize the industrial workfloor. However, the soft material results in
non-linear deformation that is highly influenced by external environmental
factors. Furthermore, the soft material can be actuated through various
technologies resulting in a wide range of behaviors. Consequently, kinematic
control of these systems is a challenging problem.
We have initially explored the domain of reinforcement learning to develop
open-loop controllers that enable high-precision point-to-point motion
control. Recently, we extended the approach to accommodate highly
redundant data that represents competing objectives with the aim to find a
solution that simultaneously optimizes each objective. The algorithm was
tested on a soft robot arm module made up of hybrid actuation such that the
overall capabilities comprised of omnidirectional bending, extension,
contraction, and variable stiffness. The arrangement of the actuators is such
that it is able to achieve different stiffness at the same position. The results
demonstrate the ability to find a desired solution for a given position and
stiffness which has been presented in IROS 2017. The results have also been
published in IEEE Robotics and Automation Letters.
However, open-loop control is limited in its capability due to dependency on
a given model. For a practical scenario, a closed-loop controller is more
promising to accommodate for external disturbances, internal failures, and
overall non-stationarity due to elasticity. We developed an algorithm that
collects spatiotemporal data along the trajectory in a manner such that all
the data collected is simultaneously resolved for redundancy. This data is
fed to a supervised learning framework to learn point-to-point motion
control. In a recent study, this has been tested on a manipulator made up of
layer-jamming, cables, and pneumatics to perform self-motion i.e. rotate in
the mid -ection while keeping the end-effector fixed at a given point. The
results of the work have been submitted to ICRA 2018.
Yasmin Ansari
Host Institution:
Scuola Superiore Sant’Anna
Supervisor:
Prof. Cecilia Laschi
Email:
5 | Issue 6
Cognitive multi-agent system framework for reconfigurable systems
In the field of Human–Robot Interaction (HRI) there is an increasing demand for stretchable sensors as unobtrusive devices that do not interfere with the robot’s mechanics. The development of a soft and highly stretchable sensor that could serve as a robotic skin is needed in order to achieve intuitive HRIs. This work aims to develop a stretchable and low cost sensor by using a piezoresistive fabric material which is easily mounted over a robotic arm. An image of the touch inputs over the sensor is displayed at high temporal resolution. In this work the experimental set-up of a low-cost stretchable sensor is developed [1]. This is done through a customised PCB which is low-cost and with low power consumption, ideal for battery-powered operations. The sensor presents electrodes placed at its boundary, so that internal wiring is avoided, thus avoiding any system’s fragility. It also guarantees that the system remains flexible at high strains. Two different set-ups (8 and 16 electrodes) are presented and tested along with 2 different stretchable and piezoresistive materials [2]. Various experiments are conducted demonstrating the quality of the hardware setup and the successful reconstruction of the pressure images.
A work on optimising the sensor’s response is conducted [3-4] by showing that the selection of electrodes on which current injection and voltage readings are performed can be chosen dynamically, resulting in an improved quality of the reconstructed image and system performance. Then, it is proposed to solve the contact detection and image reconstruction with Artificial Neural Networks (ANNs) which can learn to localize the contact position and detect the size of the target with high accuracy. This work is conducted in collaboration with Roy Assaf. Finally, the sensor is placed over a robotic arm, to show its stretching capabilities and accurate response. In conclusion, the contribution of this work is a first step to surmount the major limitations of current soft and flexible sensors and enable their integration in robotic applications, being clearly a key feature for enabling safe and low-cost HRI, as well as their integration in various applications where stretchable and low-cost sensors are needed.
Stefania Russo
Host Institution:
University of Salford
Supervisor:
Prof. Samia Nefti-Meziani
Email:
[1] Russo, S., Carbonaro, N., Tognetti, A., Nefti-Meziani, S., “Development of a High-Speed Current Injection and Voltage Measurement System for EIT-based Stretchable Sensors”, Accepted for publication as a journal article for Technologies. [2] Russo S., Meziani S.N., Gulrez T., Carbonaro N., Tognetti A. (2016) “Towards the Development of an EIT-based Stretchable Sensor for Multi-Touch Industrial Human-Computer Interaction Systems”. Lecture Notes in Computer Science, vol 9741. Springer, Cham. HCI conference 2016 [3] Russo, S.; Carbonaro, N.; Tognetti, A.; Nefti-Meziani, S. “A Quantitative Evaluation of Drive Patterns in Electrical Impedance Tomography.” Wireless Mobile Communication and Healthcare. Springer International Publishing, 2017, pp. 337–344. Mobihealth2016, Milan. [4] Russo, S., Carbonaro, N., Tognetti, A., Nefti-Meziani, S., “A Quantitative Evaluation for Dynamically Optimizing Drive Patterns Selection in EIT-based Stretchable Sensors”, submitted for publication as a journal article for Sensors.
6 | Issue 6
Control and Design of compliant and reconfigurable modular robots
We extended the framework for automatic centralized controller design of modular robots first introduced in [1], and published it in the IEEE Transactions of Control System Technology [2]. The experimental application has been conducted on a real, commercially available modular robot which shows direct applicability for industrial purposes. Concerning handling of significant model uncertainties and input disturbances, our recently developed interval-arithmetic-based robust control approach for rigid robots in [3] has been complemented with the work in [4] to remove its main drawback of high computational cost. In fact, in [4] we proposed an effective, efficient algorithm for numerical implementation of this method which enables online computability. Interestingly, the proposed interval-arithmetic-based control approach also paved the way to the development of an effective robust control method for continuum robots (see Fig. 1) which has been recently accepted for publication in the Proceedings of the IFAC World Congress 2017 [5]. The secondment performed at the Department of Advanced Robotics of the Italian Institute of Technology last spring has been very fruitful. A novel, effective control approach for elastic-joint robots has been introduced and recently published in the Proceedings of the International Conference on Robotics and Automation (ICRA) 2017 [6]. An additional secondment at the same institute has been carried out, to extend and apply the new control method on a complex robotic arm with elastic joints. A paper which contains the new theoretical and experimental results as an outcome of the second secondment is currently under development. A new framework for enhancing flexibility in automation as a result of a combination of the research efforts of multiple ESRs (Martijn, Esra, Aaron and myself) has been conceived and is currently being finalized. The results of the successful application of this approach with experiments on realistic industrial applications (see Fig. 2) are promising and a related paper in currently under development.
Andrea Giusti
Host Institution:
Technische Universität München
Supervisor:
Prof Matthias Althoff
Email:
[1] A. Giusti and M. Althoff, “Automatic centralized controller design for modular and reconfigurable robot manipulators,” Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2015, pp. 3268-3275. [2] A. Giusti and M. Althoff, “On-the-fly control design of modular robot manipulators,” IEEE Transactions on Control Systems Technology, 2017, Vol. PP, Issue: 99. [3] A. Giusti and M. Althoff, “Ultimate robust performance control of rigid robot manipulators using interval arithmetic,” Proc. of the American Control Conference, 2016, pp. 2995-3001. [4] A. Giusti and M. Althoff, “Efficient Computation of Interval-Arithmetic-Based Robust Controllers for Rigid Robots”, Proc. IEEE Int. Conf. on Robotic Computing (IRC), 2017, pp. 129-135. [5] F. Hisch, A. Giusti, and M. Althoff, “Robust control of continuum robots using interval arithmetic”, Proc. of the 20th IFAC World Congress, 2017, (accepted for publication). [6] A. Giusti, J. Malzahn, N. Tsagarakis, and M. Althoff, “Combined inverse-dynamics/passivity-based control for robots with elastic joints,” Proc. IEEE Int. Conf. on Robotics and Automation, 2017, pp. 5281–5288.
7 | Issue 6
Introducing a novel Marker-based Geometry Model for position extraction
The project takes advantage of the use of multi-robot cooperation for agriculture purposes which offers big advantages on single robot manipulation. Allocating task to multi-robots can save a huge amount of time and reduce production cost and area usage space. Robots cooperation is an effective solution when it comes to simultaneous localization. Our method is a combination of marker-based and marker-less localization. We use a calibrated monocular cameras as a primary sensor for each robot to estimate the relative position of the mobile robot with respect to each other. The relative position estimation using a Marker is a P3P (Perspective three points) problem out of which we extract parameters for pose estimation. To estimate the absolute pose a Visual Odometry algorithm is developed using the same camera sensor. These two measurements are then fused to reduce the drift error from the Visual Odometry (VO) data. To reset the absolute position at long distances, static markers placed at known location are employed. With the incorporation of VO, pose estimation is obtained that is more accurate than conventional techniques such odometry wheel and INS. It is an inexpensive and alternative odometry technique in which we tackle the high cost problem and accuracy at the same time.
For the purpose of testing the marker-based accuracy, a circular marker-based platform has been use and developed lately. The robot holding the marker is tied to the platform to restrict its motion. Experimental results on monocular vision using the marker-based localization algorithm developed have shown significate improvement. Real time results will be publish soon. Simulation results are published in the IEEE Positioning, Navigation and Communications (WPNC’2016) 2016 13th Workshop.
Alexandre Bousaid
Host Institution:
AGCO/ Fendt GmbH
Supervisor:
Dr. Benno Pichlmaier
Email:
[1] A. Bousaid, T. Theodoridis and S. Nefti-Meziani, “Introducing a novel marker-based geometry model in monocular vision” in IEEE The 13th Workshop on Positioning, Navigation and Communication 2016.
8 | Issue 6
Formal Garauntees and Trajectory Planning for Safe Human-Robot Interaction
The right tools can make a huge difference. With a 6-camera infrared motion-tracking system from the teaching fund of the Faculty of Informatics, TU München, Aaron Pereira (ESR 9) was able to test some of the ideas that he had been working on under the supervision of Prof. Matthias Althoff. The results showed that the approach of formally verified trajectory planning – the subject of Aaron's PhD – was not only feasible but worked well. We have been proactive in dissemination to possible industrial partners, since this system is immediately applicable to industry. The motion-tracking system tracked the human in the robot workspace with millisecond latencies and sub-millimetre precision. Prediction algorithms developed during Aaron's PhD were able to predict the human's future movements conservatively and in far less than a millisecond. These are used to check which of the robot's movements were safe and finally, state-of-the-art torque control developed by Andrea Giusti (ESR 6) executes this guaranteed safe robot movement. One paper on prediction algorithms and another on verification, both tested using this system, at will be presented at IROS and CDC later this year. As a quantitative measure of how effective our approach is, we are currently conducting an extensive, long-term user study. Watch this space!
Caption: human is tracked by state-of-the-art motion-tracking system
Robot performs an emergency manoeuvre when the human comes too close.
Aaron Pereira
Host Institution:
Technische Universität München
Supervisor:
Prof Matthias Althoff
Email:
9 | Issue 6
Cost Optimal Composition of Modular Robots for Specific Tasks
Modular robots consisting of several interchangeable, pre-designed
modules which enable one to configure various manipulators are a
solution for solving different tasks and operating in changing
environments. However, modular robot synthesis is a complex and time-
consuming combinatorial problem since the robot structure is different
for each module combination. To solve this problem, we have proposed a
brute-force algorithm-based hierarchical composition synthesis
algorithm in [1] and an algorithm which eliminates the cost-inefficient
module combinations during the optimization process in [2] so far.
During this period, we focused on evolutionary-algorithm-based
algorithms. A novel evolutionary algorithm-based composition synthesis
method for modular and reconfigurable robot which considers
additional task-related objectives in the evaluation of the compositions
has been accepted for publication at IEEE/RSJ International Conference
on Intelligent Robots and Systems [3]. In addition, academic secondment
at Istanbul Technical University (ITU) and industrial secondment at
BMW has completed during this period. Throughout my academic
secondment in (ITU), the research focused on path planning algorithms
and a time-efficient, collision-free motion planning algorithm for
redundant manipulators has been proposed. A paper which contains the
theoretical and experimental results as an outcome of the secondment is
currently under development. In the industrial secondment, the
approaches in [1] and [2] have implemented on real manufacturing
scenarios provided by BMW and the cost-optimal module compositions
are selected for different tasks.
Moreover, a new framework for enhancing the flexibility of current
robotic systems in automation, ESR6, ESR9, ESR13 and I combined our
research results. Considering realistic industrial scenarios, we performed
the experiments and a related paper is currently under development.
Esra Icer
Host Institution:
Technische Universität München
Supervisor:
Prof Matthias Althoff
Email:
[1] E. Icer, A. Giusti, and M. Althoff. A task-driven algorithm for configuration synthesis of modular robots. In Proc. of the IEEE International Conference on Robotics and Automation, pages 5203 - 5209, 2016. [2] E. Icer and M. Althoff. Cost-optimal composition synthesis for modular robots. In Proc. of the IEEE Multi-Conference on Systems and Control, pages 1408-1413, 2016. [3] E. Icer, H. A. Hassan, K. El-Ayat and M. Althoff. Evolutionary composition synthesis of modular robots for a given task. In Proc. of the IEEE International Conference on Intelligent Robots and Systems, 2017 (Accepted).
10 | Issue 6
Real-time Monitoring Systems for multi-cell robots
This work aims to design and build an intelligent monitoring system intended to be used for robotic machining cells. Due to the limited and variable stiffness of robots, the machining process is subject to variation in the desired response variables. The proposed system applies a machine learning approach to deal with such challenges by first sensing the behaviour of the machining process during cutting and then correcting the target variable (based on process model predictions) to achieve the correct result.
Current focus of the research is in the development of an iterative learning approach to take place during production, which allows the proposed system to estimate, given the current data set, whether the prediction is likely to be correct. The estimated value can then be used to make adjustments on the target and rework the workpiece for the desired result. The method consists in expanding the training data set in an iterative way by introduction of an in-line direct measurement of the process response variable (depth) to be collected in production. The final goal of the system is to completely remove the direct measurement step from the process, once enough training data will allow making accurate predictions. On-going work involves setting up an experiment to test such learning functionality.
The expected results of this research are as follows: (1) an innovative ML-based approach to deal with the problem of the limited stiffness in robotic machining cells and (2) a method to address the problem of the production cells to be taken off line for gathering training data.
Secondments:
1. Industrial placement at AIRBUS – Broughton (Chester), UK – 27th March
to 14th April 2017
During my industrial placement at the Airbus production site in Broughton I had the opportunity to apply the signal processing methods implemented in my research in a different application which involved electric drilling of stacks of materials (Ti/Al). The work consisted in sensing the drilling process and analysing the sensor signals for detecting the changes of material while cutting through the stack. The results of this analysis indicated a potential implementation of an automatic procedure for material change detection, however more experiments were necessary. Considering the lack of experimental data due to the fact that the process was entirely performed by human operators, it was difficult to apply any learning approach for estimation of the tool wear, based on the sensory data collected during cutting. Therefore, the implementation of this monitoring system for tool wear estimation was proposed as a possible future collaboration in case the industrial partner was interested.
2. Academic secondment at Salford University – Salford, UK – planned to
start on Aug 2017
This secondment is still in the planning phase. Discussions are taking place for the setup of an experiment that will allow me to test the iterative learning approach I’m currently developing. The system will be tested in a robotic cell application for prediction of the countersink depth-of-cut.
Mateo Leco
Host Institution:
University of Sheffield/AMRC
Supervisor:
Prof. Sam Turner
Email:
11 | Issue 6
Mathematical Modelling for Seal-healing Robotic Cell
The degradation process of complex dynamic mechatronic systems are highly stochastic in nature. After successfully developing a general degradation model for describing the highly stochastic processes, I have developed a methodology for extracting health state indicators from multi-component systems which then represent a time series signal that can be analysed, this has led to work submitted to the IEEE PHM 2017 conference, were the work received the Best Paper Award. I have also worked on unsupervised-learning approaches for the diagnostics and prognostics of complex systems, namely Gaussian mixture models, this work has been presented at the IEEE AIM 2017conference. I have been recently collaborating with Stefania Russo on applying learning techniques for sensitive skin sensors, also I am working on improving positioning accuracy for robots using monocular vision with Alexander Bousaid.
Fig 1. Clustering the three phases of rate-state interactions in a gearbox
system using Gaussian Mixture Models.
Roy Assaf
Host Institution:
University of Salford
Supervisor:
Prof. Samia Nefti-Meziani
Email:
12 | Issue 6
Soft Robotics and Morphological Computation
Active-Braid, is a novel continuum manipulator that takes its inspiration from nature, more specifically from muscular hydrostats and has the ability to expand, contract and bend in 3D-space with varying stiffness. The concept of the design is based on helically braided structures. The braids were previously analyzed for the bi-directional pneumatic braided muscle actuator. These crossed-linked helical array structures, ideally composed of non-extensible yet flexible fibers, exhibit some special modes of structural deformation. When extended or contracted axially, the structure is capable of considerable accommodation of strain since the angle between the fibers and the longitudinal axis of the structure can change. As the structure extends the radius decreases and conversely when the radius increases the structure contracts. Moreover the structure can bend smoothly without kinking under external forces. For the manipulator design the braid structure was scaled up and was first analyzed using finite element analysis, the results were validated through mechanical characterization of physical prototypes. Two prototypes were developed for the omni-directional manipulator with different materials used for the braid structure. By using the longitudinal tendons and the radial actuators the manipulator is able to achieve elongation/ shortening and bending with varying stiffness. Moreover the manipulator is also able to change its "morphology", It can change its shape from a cylindrical form to a conical form. The design of the manipulator has already been published at IEEE Robotics and Automation Letters [1]. It was also presented at IROS 2017. Another publication with the controller for the manipulator has been submitted at ICRA 2018. More over the manipulator design was selected as one of the finalist for the 2017 ASME Student Mechanism & Robotics Design Competition.
Simulation and experimental results. The radial actuator is constrained at the minimum
diameter. The retracted longitudinal tendons are indicated in each case.
Syed Taimoor Hassan
Shah
Host Institution:
Scuola Superiore Sant’Anna
Supervisor:
Prof. Paolo Dario
Email:
[1] Hassan, T., Cianchetti, M., Mazzolai, B., Laschi, C. and Dario, P., 2017. Active-Braid, a Bioinspired Continuum Manipulator. IEEE Robotics and Automation Letters, 2(4), pp.2104-2110.
13 | Issue 6
Dexterous teleoperation for a compliant robot within unstructured spaces
CERN’s Large Hadron Collider (LHC) is the largest particle
accelerator in the world. To keep it operational, regular maintenance
is required. Human operated maintenance is only possible when
radiation levels have decayed to a safe level. To remove downtime
caused by the radiation decay period, CERN would like to perform
maintenance through teleoperation.
Teleoperation enables humans to act in environments that are remote
or too dangerous for humans to enter. However, it is also a tedious
task for the operator to perform. Reduced situation awareness—the
feeling of ‘being there’—and control of dexterous robotic
manipulators make teleoperation mentally exhausting. Besides
methods that restore situation awareness, discomforts might be
reduced by providing teleoperator with task-specific assistance.
Such assistance can partly take over the task, or guide the
teleoperator while performing it. In case of CERN the latter is
preferred because it keeps the operator in full control of the
maintenance task. But how do we achieve such synergy between the
user and the assistive controller? How do we train the such a system
to assist in the teleoperation?
Over the past few months, ESR Martijn Zeestraten investigated one
possible answer to these questions: Provide the operator with
assistance by communicating the intention to the operator through
forces, and by training the assistive system based on demonstrations
of the task. The proposed system is currently under evaluation in a
user study. The experimental setup resembles a realistic maintenance
scenario and is performed on a system mock-up provided by CERN.
Illustration 1: The goal of the teleoperation is to remove a protective cap (in green) from the
collimator mock-up.
Martijn Zeestraten
Host Institution:
Istituto Italianio di Tecnologia
Supervisor:
Prof. Darwin Caldwell
Email:
14 | Issue 6
Actuation and Control of a Powered Wearable Exoskeleton for Manual Handling in Industry
Figure 1 The latest prototype exoskeleton, during a simulated handling task
Assistive exoskeletons are wearable devices that aim to support their users
in performing physical tasks. Possible target scenarios include factories,
construction sites and hospitals, where workers are exposed to high risk of
injury due to repetitive and physically demanding tasks.
The present work has contributed to the development of a powered
exoskeleton designed as a back support to reduce spinal loads during
manual material handling [1]. Specific aspects of interest have been its
torque-based actuation scheme and the user-centered control strategy [2].
The two actuators are based on a mechanical spring that acts in parallel to
an electromagnetic gear motor [3]. Given a set of torque and speed
requirements, this configuration (known as Parallel-Elastic Actuator)
enables a more convenient choice of gear motor by better utilizing its
working area, matching it to the asymmetrical torque range required by the
physical task. As a result, better torque-control performance can be
achieved, which increases the potential impact of the exoskeleton in the
application.
The user-centered control strategy is responsible to match the assistance
provided by the exoskeleton to the user needs during a given task,
potentially adapting to different task parameters. In this direction, the
choice of sensors for the user interface and associated strategies has been
driven by experimental studies that compared a number of possible
solutions [4]. An electromyographic interface based on an inexpensive
commercial device has been selected as suitable for tasks that involve
lifting and carrying of objects. The device measures the muscular activity at
the forearm and is therefore considered unobtrusive for the target task.
The applicability of the resulting prototype exoskeleton will be explored in
automotive assembly lines, in collaboration with a car manufacturer
(Centro Ricerche Fiat).
Stefano Toxiri
Host Institution:
Istituto Italianio di Tecnologia
Supervisor:
Prof. Darwin Caldwell
Email:
[1] Toxiri et al (2015) A Wearable Device for Reducing Spinal Loads during Lifting Tasks: Biomechanics and Design Concepts, ROBIO 2015
[2] Toxiri et al (2016) A powered low-back exoskeleton for industrial handling: considerations on controls, WeRob 2016
[3] Masood et al (2016) Mechanical design and analysis of light weight hip joint Parallel Elastic Actuator for industrial exoskeleton,
BioRob 2016
[4] Toxiri et al (2017) Assistive strategies for a back-support exoskeleton: experimental evaluation, RAAD 2017
15 | Issue 6
SMART-E SUMMER SCHOOL 2017
• • •
SMART-E Activities
In July 2016, SMART-E participated in the European Project
Space at the International Conference on Informatics in
Control, Automation and Robotics, Portugal. The aim of the
event was to bring together the current major European
projects and discuss their vision and objectives. SMART-E was
represented by Experienced Researcher Amr El-Sayed and
Early Stage Researcher Yasmin Ansari who disseminated the
work done within SMARTE through posters and available
prototypes of developed systems.
How to quickly automate my task? This is a central question for a production engineer. In classical industrial automation— think of a car factory—robots perform one single task for long periods of time. They were acquired because their kinematic structure fitted the task requirement, and their task was hard-coded by a skilled programmer. In nowadays manufacturing environments, tasks may change from day to day or hour to hour. The classical approach to automation is unsuitable for such scenarios: buying a dedicated robot for each task is uneconomical and hard coding each task is too time consuming. Not to mention that qualified programmers with the requisite knowledge might simply not be available.
ESRs Esra Içer, Aaron Pereira, Andrea Giusti
and Martijn Zeestraten have combined their
individual projects to address this challenge.
The result is a robotic system that automates
design and deployment based on human
demonstrations. The core idea is to combine
state-of-the-art techniques for Programming
by Demonstration (PbD), composition and
control of reconfigurable modular
manipulators. This combination allows us to
merge the structural flexibility introduced by
modular robots with user-friendly skills
transfer from PbD. The proposed approach is
evaluated on a realistic manufacturing
scenario provided by SMART-E partner
BMW.
16 | Issue 6
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