The BioRobotics Institute of Sant’Anna School of Advanced Studies presented its artificial skin at IEEE Sensors 2022, held in Dallas, TX (30/10-02/11). A one-page paper has been accepted for a Live Demo of the sensing skin during the conference. Mariangela Filosa, PhD student at the Neuro-Robotic Touch Lab headed by Professor Calogero Maria Oddo, showed how it works.

An optical fiber, embedding multiple Fiber Bragg Grating sensors, is integrated in a human forearm-like polymeric substrate. Given the sensitivity to deformations of these sensors, the tactile stimuli over the skin surface can be recognized. A novel neuromorphic approach based on the Izhikevich’s neural model has been implemented for both localizing the touch event and retrieving its intensity. The applications of such a technology are mainly related to the collaborative robotics scenario. Providing robots with artificial skin patches may enable the safe human-machine interaction in several daily life and work tasks.

L'articolo Artificial skin at IEEE Sensors 2022 at Dallas, Texas proviene da TERRINet.

Human and Eco-Centered Robotics and Automation for Logistics 5.0 & the Supply Chain

Type of Event: in person/hybrid

Location: Pontedera (Pisa, Italy), at The BioRobotics Institute, Scuola Superiore Sant’Anna, Pisa.

and via MS Teams / Zoom.

Program Dates (tentative): 5-9 June 2023

Organizers: The co-Chairs of the IEEE RAS Technical Committees (TCs) on:

• Digital Manufacturing and Human-Centered Automation
• Automation in Logistics

Corresponding organizer:

• General Chair: Paolo Dario, Scuola Superiore Sant’Anna, Pisa, Italy
• Scientific and program chair: Gastone Ciuti, Scuola Superiore Sant’Anna, Pisa, Italy

Supported by the following RAS Technical Committees

IEEE Technical Committee on Digital Manufacturing and Human-Centered Automation

IEEE Technical Committee on Automation in Logistics

IEEE Technical Committee Semiconductor Manufacturing Automation

Abstract

The Seasonal School on “Human and Eco-Centered Robotics and Automation for Logistics 5.0 & the Supply Chain” is promoted by the IEEE RAS TCs on Digital Manufacturing and Human-Centered Automation, on Automation in Logistics, and on Semiconductor Manufacturing Automation.

Its technical and educational content is focused on how Digital Technologies, in particular Robotics and Automation, deeply transformed the Manufacturing processes in the light of the worldwide increasing awareness on Sustainability issues and the pivotal role of human workers within the Factory. In addition, this School addresses the immense impact of the Covid-19 pandemic, that pushed the boundaries of technology to their limits with the increasing demand for more accurate global supply chain processes, faster delivery, and greater productivity to ensure that the flow of goods between, and within, countries continued uninterrupted.

Objectives

The teaching objective of this Summer School is to train the audience in terms of both technical and scientific perspective on the Digital Technologies and Solutions which transformed the Manufacturing processes and the Logistics / Supply Chain after the outbreak of the Covid-19 pandemic.

In a nutshell, the topics to be addressed cover the whole path from the production of goods to the final delivery to customers (from the Factory to households):

• Digital Manufacturing Processes
• Technologies (Collaborative Robots, Mobile robots, Drones)
• Sustainable and Human-Centered approaches, including safety and work fatigue mitigating issues
• Logistics 5.0 and the global Supply Chain
• First and Last Mile
• Distribution and fulfilment centres
• High-throughput Dark (automated) Warehouses
• Socio-economic, ethics and legal aspects related to the integration of Digital Technologies and human workers.

Motivation and Background

Building upon the publication of the TC Spotlight Column entitled “Digital Technologies and Automation: The Human and Eco-Centered Foundations for the Factory of the Future ”, included in the September 2021 Issue of the IEEE Robotics & Automation Magazine, the proposal of this Seasonal School comes from the extensive transformation that Manufacturing processes have gone through thanks to the procurement and integration of disruptive technologies (such as Automation, collaborative robots, Digital Manufacturing, 3D printing, industrial Internet of Things (IoT)/Cyber-Physical Systems, and Cloud Computing) safely and smoothly integrated with humans, within Manufacturing/De-manufacturing processes.

The need to propose this Seasonal School also comes from the increased awareness on global, crucial challenges such as the ethical issues on the future role of humans and Automation as well as the effect of the introduction of these disruptive robotic and robot alike technologies on workers’ conditions in the workplace, well-being at work, valorisation of human skills, and re-shape of production processes.

In fact, along these lines, many initiatives worldwide (the Paris Agreement for Climate Change, the European Green Deal  for a Sustainable Future, the US-China Climate Change joint agreement, the recent EU report “Industry 5.0. Towards a sustainable, human-centric and resilient European industry”, the UAE Energy Strategy 2050, among the others) were launched to secure a new paradigm for a sustainable world to combat Climate Change, to pursue human-centric economy and to support the digital, ecological and energy transitions. These themes are very heart-felt for the younger generations, especially Millennials and Generation-Z, as demonstrated also by the Fridays for Future international movement.

Furthermore, the awareness on the immense impact of the COVID-19 pandemic on Logistics and Supply Chain, that pushed the boundaries of technology to their limits with the increasing demand for more accurate Supply Chain processes, faster delivery, and greater productivity to ensure that the flow of goods between, and within, countries continued uninterrupted, motivated the two IEEE RAS TCs to propose this Seasonal School.

The School will address the current challenges in Logistics and Supply Chain and the application of theoretical and technological automation approaches in different fields such as Digital Manufacturing, , transportation systems, healthcare, human and financial resources.

Program overview

The Seasonal School on “Human and Eco-Centered Robotics and Automation for Logistics 5.0 & the Supply Chain” will be scheduled over 5 days, including a theoretical part and an experimental part. The former part will be a 2-day intensive course on the scientific and technological principles underlying the topics that will be addressed. Complementarily, renowned leaders from Industry and representatives of global players in Logistics will be invited to provide real-life use cases and to share their insights.

The latter part will include hand-on sessions, a hackathon / contest and a final training session on technological transfer and entrepreneurship (how to turn a research ideas into a real product).

The organizers of the School will invite established RAS members,  renowned leaders from Industry, representatives of global players in Logistics. 

A Call for Speakers will also be published to broaden the participation across geographical regions, seniority, and diversity.

Speakers – Tentative list

Paolo Dario, Scuola Superiore Sant’Anna, Italy

Birgit Vogel-Heuser, TUM, Germany

Mengchu Zhou, New Jersey Institute of Technology, USA

George Q. Huang, The University of Hong Kong, China

Maria Pia Fanti, Politecnico di Bari, Italy

Barbara Mazzolai, Ististuto Italiano di Tecnologia, Italy

Barbara Bonciani, City Councilor, Municipality of Livorno, Italy

Tamim Asfour, KIT, Germany

Alberto Sanfeliu, Universitat Politecnica de Catalunya, Spain

Anibal Ollero, University of Seville, Spain

Begoña Arrue Ulles, University of Seville, Spain

Khalifa Al Qama, Dubai Future Labs, UAE

Tarek Taha, Dubai Future Labs, UAE

Jorge-Manuel Miranda-Dias, Khalifa University, UAE

Daniela Rus, MIT, USA

Cecilia Laschi, National University of Singapore, Singapore

Atsuo Takanishi, Waseda University, Japan

Norihiro Hagita, Atr Intelligent Robotics and Communication Laboratories, Japan

Kanako Harada, University of Tokyo, Japan

Type of event

The organizers of the School commit to conduct a hybrid event, that will allow both an in-person and remote participation.

The location for in-person participation is The BioRobotics Institute of the Scuola Superiore Sant’Anna, in Pontedera, Pisa (Italy).

Remote participation will be enabled via MS Teams / Zoom.

In case that the evolution of the pandemic will not allow to conduct the School in-person with a reasonable effort, the organizers reserve the opportunity to conduct a full online version.

Tentative Agenda

Student selection process and Application

The Call for Application (available soon) will stay open for 1 month.

Eligible candidates are students (preferably IEEE RAS student members) with a Master’s Degree in Logistics, Engineering, Economics, Political and Social Sciences, or any other course related to the main topics of the Seasonal School.

Candidates must submit an application form and a CV, including a brief description of their research interests and a list of publications. The form must be accompanied with a motivational letter explaining how participation in this School can contribute to the academic and professional career of the candidate.

The selection results will be released at least 2 months ahead of the School to allow in-person attendees to plan the travel and the accommodation.

The organizers will take care of prioritizing the selecting of IEEE RAS student members where there is equal merit.

Registration fee:

• USD$50,00 for IEEE RAS student members
• USD$75,00 for non-members

There will be waivers for students from disadvantaged areas of the world. There will be merit-based grants to cover travel and accommodation costs and registration fees.

All participants will receive a certificate of participation in the IEEE RAS School signed by the organizing TCs.

L'articolo 2022 IEEE RAS TECHNICAL EDUCATION PROGRAM – Seasonal School proviene da TERRINet.

Rudolf Szadkowski is a Phd Student at Czech Technical University in Prague (Prague, Czechia). He currently works on biologically inspired gait controller learning, and applied to the TERRINet project to deploy learnable phase-aware gait controller using the I-Support platform, offered by the TERRINet partner The BioRobotics Institute – SSSA (Pontedera, Italy).

The I-Support is a soft-robotic arm composed of two connected, individually pneumatically actuated modules, see Figure 1. The pneumatically actuated soft body provides the compliance which is essential in robot-human interaction, but its dynamics are challenging to learn and control.

Learning the dynamics augmented with Central Pattern Generator

The algorithm incrementally expands an ensemble of expert models by exploring various rhythmic behaviors. The rhythm is internally represented by Central Pattern Generator (CPG), an oscillating system which provides rhythm-phase awareness [1]. As the ensemble grows the periodic behavior is tuned and approximates the desired target behavior, see Figure 2.

Ensemble of CPG augmented models synthesize novel gaits

The implemented algorithm bootstraps multiple phase-aware models which estimate the interaction between commanded pressure and I-Support module positions. For given target position, the ensemble gradually tunes its behavior until the target matches with the measurement. Moreover, the ensemble can synthesize behavior for different hand-set targets without learning new models as is shown in Figure 3.

References:

[1] Yan, T., Parri, A., Ruiz Garate, V. et al. An oscillator-based smooth real-time estimate of gait phase for wearable robotics. Auton Robot 41, 759–774 (2017).

L'articolo TERRINet SUCCESS STORY: Learning the dynamics of soft-robot rhythmic motion proviene da TERRINet.

Marek Vagas is an associate professor at Technical University of Kosice, Slovakia. He currently focus on collaborative robots programming (from various manufacturers), and applied to the TERRINet project to study, testing and verifying mechanical and programming aspects of dual robotic system with focus to level arm motions as well as bi-manual actions to obtain higher degree of such workplaces designing, its operation and increasing of education quality for teachers, students and SMEs employees using COMAU Dual arm robot, offered by the TERRINet partner, The BioRobotics Institute of SSSA, Pisa, Italy.

The Comau Smart Dual arm robot is a robotic system that can be characterized by its many bi-manual tasks that are most often required during main, but also helping processes. Many industrial sectors require specialists for operating automated and robotic workplaces with aim of replacing monotonous and stereotyped repetitive human tasks by powerful advanced dual arms robots. The roles of human moves to more specific and difficulty tasks, where their complex interactions cannot be ever replaced. These innovative bi-manual robotic solutions open new opportunities for human-robot collaboration and bringing together a dream that world dreams about an upcoming new technologies that becomes to be reality.

“Collaborative robots” and their control strategies

Demands and problems for the multi-cobot communication can be accomplished by commonly used methods such is online programming in a following ways. Two control strategies approaches were implemented on UR collaborative robotic system:

1. Basic and advanced programming functionalities throughout the cobot teach pendant that are used obviously by programmers and operators to build and execute cobot jobs, such Shirine El Zaatari et al. (2019). Teach pendants offer a variety of settings to control and are also utilized to design new capabilities and features.
2. TCP/IP programming functionalities based communication that allows connection with standard pc as a Server and cobot system as a Client, such Serhat Demirtas et al. (2022). By developing the program software (C++ or python) we are able to control the cobot sending information in a bidirectional way (from the robot to the host pc – i.e. robot ready to listen for the task, and from the host pc to the robot – i.e. pose to reach (Figure 1).

Two-arm robotic systems from COMAU and soft conformable artificial skins

Collaborative robots are expected to physically interact with humans in daily living and workplace, including industrial and healthcare settings. A related key enabling technology is tactile sensing, which currently requires addressing the outstanding scientific challenge to simultaneously detect contact location and intensity by means of soft conformable artificial skins adapting over large areas to the complex curved geometries of robot embodiments (Massari L. et al 2022). In context of this research were collaborative robotic system (COMAU dual arm robotic system that is available at SSSA, Pisa, Italy) tested with the proposed algorithms for post-processing data collection from an accelerometer mounted on this robotic platform to evaluate platform vibrations (Figure 2).

References:

• Shirine El Zaatari, Mohamed Marei, Weidong Li, Zahid Usman, Cobot programming for collaborative industrial tasks: An overview. Robotics and Autonomous Systems, Volume 116, 2019, Pages 162-180, ISSN 0921-8890,https://doi.org/10.1016/j.robot.2019.03.003.
• Serhat Demirtas, Tolga Cankurt, Evren Samur, Development and Implementation of a Collaborative Workspace for Industrial Robots Utilizing a Practical Path Adaptation Algorithm and Augmented Reality, Mechatronics, Volume 84, 2022, 102764, ISSN 0957-4158, https://doi.org/10.1016/j.mechatronics.2022.102764.
• Massari, L., Fransvea, G., D’Abbraccio, J. et al. Functional mimicry of Ruffini receptors with fibre Bragg gratings and deep neural networks enables a bio-inspired large-area tactile-sensitive skin. Nat Mach Intell 4, 425–435 (2022). https://doi.org/10.1038/s42256-022-00487-3

L'articolo TERRINet SUCCESS STORY: Skilled and Innovative Training Approach – Dual Arm Robotics proviene da TERRINet.

Don’t miss this year’s edition of SUMMER SCHOOL 2022: ROBOTICS & AI organised by our partner, The Institut de Robòtica i Informàtica Industrial – IRI (Spain).

The Summer School on Robotics and Artificial Intelligence will be held online from July 4 to July 6, 2022. Access to all the talks and content is free. The event is aimed at students, researchers, and industry practitioners.

Three days with an intensive agenda on current main robotic issues will be presented: Deep learning perception on Robotics, Human-Robot Interaction and Robot Navigation, and Assistive Robotics. The Summer School will include experimentation sessions.

Submit your registration here (deadline: May 16, 2022).

More info: https://www.iri.upc.edu/workshops/RoboticsAISummerSchool2022

The initiative is supported by TERRINet, The Spanish Society for Research and Development in Robotics (SEIDROB) and Cloth Manipulation Learning from Demonstration (CLOTHILDE). 

L'articolo ONLINE SUMMER SCHOOL 2022: ROBOTICS & AI – July 4 – 6, 2022 proviene da TERRINet.

The world is slowly recovering from coronavirus pandemics. The time has come to announce the re-launch of the TERRINet project, with an important novelty – enabling remote access to our infrastructure to anyone in the world.

This is just the first step towards a sustainable and long term prosecution of the TERRINet project along the lines of our vision for an expansion to a more international and global audience either in terms of end-users and of platforms providers.

With that purpose we organised the first VIRTUAL TOUR of the TERRINet robotics platforms at The European Robotics Forum – ERF 2021, taking place online on April 13, 2021.

Each of the 15 TERRINet partners and leading European robotics laboratories video presented the cutting-edge robotics platforms they offer to students, researchers, and entrepreneurs worldwide.

Did you find the TERRINet platform that fits to your research idea?

You can apply for your fully-covered access here. The submission deadline is May 31, 2021.

L'articolo TERRINet Platforms Virtual Lab Tour on European Robotics Forum – ERF 2021 proviene da TERRINet.

Irene Rivas Blanco is an Assistant Lecturer in the Department of Systems Engineering and Automation at the University of Malaga (Malaga, Spain). She applied to the TERRINet project to build a dataset of robotic surgical tasks using the Da Vinci Research Kit platform (dVRK), offered by the TERRINet partner The BioRobotics Institute – SSSA (Pontedera, Italy).

Da Vinci Research Kit (Figure 1) is the leading reference in the field of surgical robotics. The platform consists of a surgeon’s console to tele-operate the surgery and a patient side system where the surgery takes place. The platform enables the execution of complex motions due to additional degrees of freedom of the instruments. The users can perform the motions that are not possible on other platforms.

Designing collaborative strategies for robots to aid surgeons in performing certain actions with autonomy can lead to increased precision and accuracy of robot-assisted operations, improved consistency in treatments, and higher dexterity and access to tissues.

Building a Dataset of Surgical Tasks for Autonomous Auxiliary Assistance

Surgical datasets containing surgical videos of real interventions are no novelty to the public. So far, they were mostly used for the recognition of surgical actions.

The objective of this experiment was to build a dataset of surgical tasks with different levels of complexity combining camera image and robotic system sensory data, which would serve for further exploration of deep learning techniques for autonomous auxiliary tasks in laparoscopic procedures.

In the execution period, 16 users performed the surgical tasks to obtain the required data (Figure 2). 10 users were male and 6 females ranging between 21 and 42 years old. Each user performed 6 trials of each exercise – 3 trials using the right hand and 3 using the left hand. 

 

The surgical tasks were the following:

• Post and Sleeve: The goal was to move the coloured sleeves from side to side, whereas the user was allowed to take a sleeve with one hand, pass it to the other, and place it over a peg on the other side.
• Pea on a Peg: The goal was to take beads from the cup and place them on top of the pegs using only a single hand.
• Wire Chaser: The goal was to move a ring from one side to the other using only one hand.

The surgical tasks addressed motions and skills necessary for laparoscopic surgery, such as hand-eye coordination, bimanual dexterity, depth perception, and interaction between the dominant and non-dominant hand. The teleoperation of the tools was performed with haptic guidance, using the dVRK console. The control of the teleoperation was carried out with a ROS software provided by the host institution.

Dataset of Surgical Tasks Based On 282 Recordings

The final dataset contained 282 recordings in total – 92 of post and sleeve task, 95 of pea on a peg, and 95 of wire chaser. The parameters were recorded on the master tool manipulators (44 recordings), patient side manipulators (80), and the pedal (40).

The mean scores of the surgical tasks (Figure 4) were 138.72 for post and sleeve, 152.25 for pea on a peg, and 42.94 for wire chaser. The mean score of the latter was lower due to a shorter duration of the task.

The dataset included the following data:

• Images (each exercise was recorded using the mp4 format),
• Kinematic data (kinematic data of the two master tool manipulators and patient’s side manipulators),
• Skills evaluation (the user performance evaluation based on time and task efficiency),
• Questionnaire (about the personal data, experience with teleoperated systems and visuo-motor skills).

“The TERRINet project was a unique opportunity to work with da Vinci Research Kit that I can’t access at my home institution. It was an amazing experience to get to know the platform, work on it, and collect data, which enabled further research on the topic”, Irene described her TERRINet experience.

She suggests applying to students and researchers who would like to implement their research experiment on a platform not available at their home institution and would like to expand the knowledge from a different research group.

Lear more about Irene’s project. Click on the video below:

References

L'articolo TERRINet SUCCESS STORY: Robotics Surgical Tasks Data Acquisition on the da Vinci Research Kit Platform proviene da TERRINet.

Young post-doctoral researcher of the Center for Medical Robotics at the Korean Institute of Science and Technology (KIST) (Seoul, Republic of Korea) applied to conduct his experiment of an inertial parameter identification by using a geometric algorithm on a challenging platform.

TERRINet project gives an opportunity to excel the research ideas (beyond the limits of home infrastructures) also to non-European students, researchers, and industry.

Byungchul An’s experiment on ARMAR-6 platform (offered by the TERRINet partner Karlsruhe Institute of Technology (KIT) – Germany) is definitely a story of success.

»ARMAR-6 dual-armed platform (Figure 1) is not challenging only hardware-wise, but also software-wise. It is hard to find a platform with a combination of torque-controlled, redundant, and dual-arm manipulators, that is mobile and also with broad-spectrum of well-working libraries«, explained Byungchul his motivation for participating in the TERRINet project.

The main purpose of his experiment was to identify the inertial parameters of an object held by two arms, which leads to improved control performance and better manipulation strategy.

By conducting such experiments on a robot, like ARMAR-6, which was assembled for a safe humanoid robot collaborative assistance for industrial environments, we can make an important step forward in a smart factory’s automatization.

The Key to Human-Robot Collaboration in an Industrial Environment is Efficient Manipulation Of Large and Heavy Objects

To enable a close human-robot collaboration in an industrial environment, a quick and efficient manipulation of not only light and small object, but also large and heavy ones, needs to be assured.

The manipulation of the latter usually requires the usage of bi-manual handling, which can significantly improve performance by identifying an object’s physical parameters (the inertial parameter identification).

The inertial parameters of an object can be expressed as

• elements in the vector space, which values must satisfy a certain physical consistency condition (Traversaro et al. 2016);
• 4×4 symmetric positive-defined matrixes (Wensing et al. 2018).

The identification can be conducted by force/torque (F/T) sensors on two arms’ wrist, regardless of potential non-linear effects on the joints.

Two-Step Application of The Geometric Inertial Parameter Identification Algorithm

The experiment was conducted in two steps:

STEP 1: Execution of series of bimanual tasks to obtain the data using the ARMAR-6 platform:

For this purpose, the implementation of one of the bi-manual compliance controllers developed by the host institute was conducted (Figure 2). As a result, the data for the inertial parameters identification of a handled object was obtained.

STEP 2: Application of the geometric inertial parameter identification algorithm to dual-arm manipulation:

Dual-arm manipulation is accompanied by a closed-loop kinematics structure. To apply the proposed geometric algorithm (Lee & Park, 2018; Lee et al. 2020), the robot’s dynamics equations should take into account the kinematic constraint.

Figure 3: A human model identification results using the geometric method (left, Lee & Park 2018) and the previous method (right). Ellipsoids are equivalent to the identified inertial parameters of human body parts.

The Validation Of Identified Inertial Parameters by the Usage Of Geometric Algorithm

The conducted experiment showed, that to validate identified inertial parameters by the usage of geometric algorithm we can compare directly the mass and joint torques (obtained by solving the inverse dynamics). However, inertia matrices cannot be compared directly.

We can support also your scientific idea! Check out the platforms we offer.

Find out how to be granted with fully-covered access to 15 leading robotics research infrastructures in Europe.

References:

1. Traversaro, S. Mrossette, A. Escande, and F. Nori, “Identification of fully physical consistent inertial parameters using optimization on manifolds,” in Proc. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems., pp. 5446-5451, 2016.
2. M. Wensing, S. Kim and J. J. E. Slotine, “Linear matrix inequalities for physically consistent inertial parameter identification: a statistical perspective on the mass distribution”, IEEE Robotics and Automation Letters, vol. 3, no. 1, pp. 60-67, Jan. 2018.

3. Gao, Y. Zhou, and T. Asfour, “Projected Force-Admittance Control for Compliant Bimanual Tasks,” in Proc. 2018 IEEE-RAS International Conference on Humanoid Robots, pp. 607-613, 2018.

4. Lee and F. C. Park, “A Geometric Algorithm for Robust Multibody Inertial Parameter Identification,” IEEE Robotics and Automation Letters, vol. 3, no. 3, July 2018.

5. Lee, P. M. Wensing and F. C. Park, “Geometric Robot Dynamic Identification: A Convex Programming Approach,” in IEEE Transactions on Robotics, vol. 36, no. 2, pp. 348-365, April 2020.

L'articolo TERRINet SUCCESS STORY: Inertial Parameter Identification of a Hand-held Object With ARMAR-6 Humanoid proviene da TERRINet.

A team of the Scuola Superiore Sant’Anna (SSSA), Italy, led by Prof. Arianna Menciassi, won the prestigious International KUKA Innovation Award 2020 for the development of an innovative platform for focused ultrasound surgery.

As Prof. Menciassi said, the platform is able to increase accuracy, speed, and therapeutic opportunities.  The Da Vinci Research Kit (DVRK) platform that Prof. Menciassi’s team used to compete for the Kuka Award is the same that SSSA is offering within the TERRINet Trans-National Access (TNA) framework. Find more information here.

The DVRK was also used by dr. Irene Rivas Blanco (University of Malaga, Spain) during her TNA at The BioRobotics Institute of the Scuola Superiore Sant’Anna, Italy.

Irene carried out a brilliant experiment aiming at building a large dataset of robotic surgical manoeuvrers for collaborative surgical robotics by exploring the use of Deep Learning techniques to perform autonomous auxiliary tasks in laparoscopy.

Another TERRINet TNA user, Dr. Endika Gil-Uriarte working at Alias Robotics, Spain, has been granted access to the DVRK platform available at The BioRobotics Institute, and he is queued to access as soon as the pandemic will be over.

“This story indicates that TERRINet is really offering users worldwide the opportunity to work on world-class robots with the supervision of international leaders and, not least, in cooperation with other young qualified researchers.” (Prof. Paolo Dario, Scuola Superiore Sant’Anna, Italy, TERRINet Scientific Coordinator) 

L'articolo A team of the Scuola Superiore Sant’Anna, Italy, won the prestigious International KUKA Innovation Award 2020 on the TERRINet platform Da Vinci Research Kit (DVRK) proviene da TERRINet.

TERRINet partners offer a great variety of the most advanced robotic platforms in Europe. We are presenting another inspiring project conducted by Alfonso Alcántara, a Ph.D. student of the Group of Robotics, Vision and Control (GRVC) at the University of Seville (Spain).

Alfonso works on the European project MULTUDRONE, where he is developing a multi-drone collision avoidance algorithm for outdoor media production.

He applied to TERRINet to improve the impact of his research in Smart Experience Laboratory (Smart XP Lab) offered by the University of Twente (The Netherlands), one of the TERRINet partners.

Smart XP Lab is a facility that enables researchers and students to develop technical knowledge and skills. The lab is equipped with educational applications for building or testing robotic devices; distance, pressure/force, touch, and rotation testing sensors; human and indoor drone motion capture facilities; space for testing indoor robots and drones; and rapid prototyping facilities such as a maker space, an electronics lab, laser cutting and 3D printing facilities.

OPTIMAL TRAJECTORY PLANNING ALGORITHM

Alfonso’s multi-drone collision avoidance algorithm is a non-linear, optimization-based method for trajectory planning. The trajectories are planned and executed in a distributed manner by a team of drones using a receding horizon scheme. The method considers drone dynamic constraints and imposes them to avoid predefined no-fly zones or collisions with others.

He implemented his trajectory planner using Forces Pro [1], which is a software that creates domain-specific solvers in C++ language for non-linear optimization problems.

AUTONOMOUS FLIGHTS PERFORMED WITH THE OPTITRACK MOTION TRACKING SYSTEM

Alfonso’s experiment aimed at testing the generation of the optimal trajectories for drone navigation based on the optimization-bashed method.

A flying arena with Optitrack motion tracking system was used to get the experimental results for the drone control performance. The goal was to perform autonomous flights in a formation around the target.

The targets were virtually placed in the middle of the drone trajectory. The movement of the targets was simulated along with the testbed. The navigation control of the drone was conducted based on the use of the precise positioning system, with a challenge to avoid the generated obstacles.

The provided data enabled the evaluation of the trajectories’ precision in accordance with the obstacle avoidance. The results served for further analysis of the logs of the trajectories and opened new possibilities for the software development to more drones. These autonomous flights served as a starting point for the following work: https://arxiv.org/abs/2009.04234

WITH TERRINet TO NEW KNOWLEDGE & THE IMPROVEMENT OF THE RESEARCH IMPACT

 During the stay at the TERRINet host infrastructure, Alfonso was able

• to experiment on new installations;
• to learn how to use the Optitrack technology;
• to learn how to navigate and control the drone in manual flight;
• to test the autonomously generated trajectories; and
• with all the newly obtained knowledge to set new research directions for his research.

FULLY-COVERED ACCESS TO 100+ S.O.A. ROBOTIC PLATFORMS

“TERRINet is a very interesting way to access new infrastructures and is enabling researchers to test and improve their research data,” states Alfonso.

It allows students and researchers to access the most prominent robotic labs across Europe and to get “1-on1” support from the leading robotics experts.

Students and researchers! Do you have a brilliant research idea? Apply now. You can be granted with fully-covered access (including accommodation & travel costs) to 100+ S.O.A. robotic platforms.

The forthcoming application deadline is January 31, 2021.

Check the eligibility and conditions on www.terrinet.eu.

REFERENCE

[1] A.Zanelli,    Domahidi,  J.  Jerez,  M.  Morari, FORCES  NLP:  An Efficient  Implementation  Of  Interior-point  Methods  For  Multistage  Non-linear  Nonconvex  Programs, International  Journal of  Control (2017). DOI: 10.1080/00207179.2017.1316017

L'articolo TERRINet SUCCESS STORY: The Efficiency of Multi-robot Optimal Trajectory Planning Method for Drones Tested in Optitrack Motion Tracking System proviene da TERRINet.