TECNALIA studies the measurement of human trust in collaborative robots

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“Workmates of Indumental, Gaiker and Tecnalia interacted with a computer simulation of a manufacturing machine”

Nowadays, in industrial environments the trend is to achieve hybrid human-robot collaboration by replacing multiple currently manual, expensive and time-consuming tasks with correspondingly automatic robotic-based procedures.

More specifically, the goal of HR-Recycler project is to create a hybrid collaborative environment, where humans and robots will share and undertake, at the same time, different processing and manipulation tasks, targeting the industrial application case of WEEE recycling.

However, in order to achieve a successful interaction, a great level of trust is required between human and machine. Therefore, our project considers human factors and social cognition as key components to evaluate robot’s behaviour in terms of trust.

Within the scope of this project, TECNALIA is working in a Human-Robot trust classification model development that will be trained using inputs of psychophysiological signals from Human-Robot interactive collaboration.

Normally, in industrial environments is quite unfriendly to test these type of developments (due to complexity and quantity of items of Personal Protection Equipment used). So, in this research project, the trust classifier will be validated in a realistic 3D Virtual Reality industrial environment, which will be implemented ad hoc for this purpose.

The novelty of this project is the advance in the inference of human trust when interacting with a robot co-worker, in terms of including new psychophysiological signals as respiration and eye-tracker in the development of a trust classifier based on machine learning and the validation in an ad hoc 3D Virtual Reality environment that requires user interaction and physical movements that may generate noise on the desired signals.

The main objective is to detect robust psycho-physiological signals to discriminate between trust and distrust situations in human-robot interaction. This will enable to design machines capable to respond to changes in human trust level and rebalance the robot’s behaviour when a low confidence level is detected.

To achieve this objective, TECNALIA will work in a doble-stage empirical procedure.

The first experiment held on last week with the collaboration of 60 workmates of Indumental, Gaiker and Tecnalia. In this experiment, each person interacted with a computer simulation of a manufacturing machine. Therefore, each trial presented a stimulus (sensor reading), a response (participant’s choice) and an outcome (machine working or not). This studio was a laboratory based and several psychophysiological signals were captured while forcing trust/distrust situations. So, this will allow us to model a Human trust classifier according to the most significant signals.

The second experiment will use a VR environment to recreate real working conditions and validate the previous analytical results. Using a virtual environment allows us to provoke some validating conditions (for instance, robot malfunction) without running unnecessary health risks.

The RV experiment will replicate the exact layout of a Human-Robot collaborative workstation of a manufacturing plant. The participants will be asked to perform the same activities as the workers on the real plant and the virtual robots will interact with them on the same reality basics. However, during the experiments, sometimes robots will malfunction (will have longer waiting time or they will move more abruptly), thus compromising the human trust.

Hr-Recycler workshop “Shared workspace between humans and robots”

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Blog Hr-Recycler workshop Shared workspace between humans and robots”

The Hr-Recycler workshop “Shared workspace between humans and robots” took place on the 28th of July 2020 hosted at the 9th edition of the Living Machines international conference on biomimetics and biohybrid systems.

The aim of this Hr-Recycler workshop was to present and discuss together with scientific and industrial stakeholders novel technological approaches that facilitate the collaboration between robots and humans towards solving challenging tasks in a shared working space without fences.

Human-Robot Collaboration (HRC) has been recently introduced in industrial environments, where the fast and precise, but at the same time dangerous, traditional industrial robots have started being replaced with industrial collaborative robots. The rationale behind the selection of the latter is to combine the endurance and precision of the robot with the dexterity and problem-solving ability of humans. An advantage of industrial collaborative robots (or cobots) is that they can coexist with humans without the need to be kept behind fences. Cobots can be utilised in numerous industrial tasks for automated parts assembly, disassembly, inspection, and co-manipulation.

Embedded in the most exciting environment provided but the Living Machines conference, one of the foremost conferences on robotics in the world, the HR-Recycler workshop was attended by about 50 participants and covered topics related to Smart mechatronics, Computer vision in robot-assisted tasks, Human-robot collaboration, Safety in the workspace. In addition, the workshop not only leveraged on the results of the EU-funded project HR-Recycler (, which introduces the use of industrial collaborative robots for disassembling WEEE devices, but also welcomed contributions from projects in industrial collaborative robotics as well as the broader research community. The other EU projects involved in the workshop were Rossini (, COLLABORATE ( and SHAREWORK (

What all projects have in common is the use of industrial robots that collaborate with humans while performing assembly and disassembly tasks. The overarching goal of these projects is to improve the overall productivity of a hybrid cell (which includes humans and robots), where ultimately, the human will assume a supervisory role, thus decreasing their cognitive load and fatigue and increasing their free time. Here, the human, safe operation, and adaptability are key components for a successful Human-Robot Collaboration task.

As highlighted during the workshop’s presentations and discussion, the human worker is central to all the participating projects. When developing systems with collaborative robotic agents, the human-related factors need to be included, as they may influence the quality of the robotic operations. An important aspect that was raised during the workshop is the need for new Key Performance Indicators (KPIs) to measure the HRC ergonomics. Participants also acknowledged the value of systematically evaluating the performance between humans and robots, as well as the perceived collaboration from the human workers. Ethics were also discussed, as all systems should operate taking into consideration the regulatory, legal, ethical and societal challenges of robotics in industrial automation. Human safety is essential, and participants reflected on the challenge that rises is the trade-off between performance, accuracy and safe operation. Finally, to develop collaborative systems that will interact with a variety of users in dynamic settings, their design should include a dynamic adaptation to both the operator and the environment.

Although the time of the workshop was limited, a lot of interesting and crucial topics arose for a safe and successful Human-Robot Collaboration. These fruitful discussions not only brought forward the current challenges that this field is facing, but also the opportunity and necessity for the relevant stakeholders to meet, discuss, exchange ideas and collaborate.

We hope to be part of more similar events!

Examples of the workshop’s presentations. The full program of the workshop can be found HERE.


– Apostolos Axenopoulos: Centre for Research and Technology Hellas – Information Technologies Institute
– Dimitrios Giakoumis: Centre for Research and Technology Hellas – Information Technologies Institute
– Eva Salgado:  Etxebarria, Tecnalia
– Vicky Vouloutsi, Institute for Bioengineering of Catalonia (IBEC), SPECS -Lab,
– Anna Mura: Institute for Bioengineering of Catalonia (IBEC), SPECS -Lab,

Autonomous pallet transportation in factory floor environments

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Autonomous pallet transportation in factory floor environments

Multi-pallet detection in factory floor environments

Within the HR-Recycler a novel pallet-truck AGV for the autonomous transportation of the pallets between classification and the workstations will be developed to enable automation in intra-factory logistics transportations of WEEE devices within a collaborative factory floor environment. The pallet-truck to be developed should be endowed with multi-pallet detection and pallet pick up navigation and control capabilities.

Technical Challenges

Pallet-truck AGVs’ operation in human populated factory environments is a challenging task, especially when there is a demand to operate without following fixed paths defined by guide wires, magnetic tape, magnets, or transponders embedded in the floor. There are several methods that tackle the task of autonomous pallet transportation and they are usually relied on computer vision approaches varying for indoor/outdoor environments. Yet, most of them are devoted to operate in predetermined paths and their global navigation is controlled by a central system typically linked to the Enterprise Resources Planning (ERP) of the factory.

Robotic Solution: Multi-pallet detection and docking strategy

A dedicated method has been developed by CERTH-ITI for the pallet-truck AGV developed by Robotnik partner within the HR-Recycler project. The solution comprises a vision-based method that enables safe and autonomous operation of pallet moving vehicles that accommodate pallet detection, pose estimation, docking control and pallet pick up in such industrial environments. A dedicated perception topology has been applied relying on stereo vision and laser-based measurements installed on-board a nominal pallet truck model. Pallet detection and pose estimation is performed with model-based geometrical pattern matching on point cloud data, exploiting CAD models of universal types of pallets, while robot alignment to candidate pallet is performed with a dedicated visual servoing controller. To allow safe and unconstrained operation, a human-aware navigation method has been developed to cope with human presence both during global path planing and during pallet-docking navigation phase. The developed method has been assessed in Gazebo simulation environment with a pallet truck model and proved to have real-time performance achieving increased accuracy in navigation, pallet detection and pick-up (see Figure1)

The Visual Analytics Lab (VARLab) of CERTH-ITI

Figure 1 In first figure, the robot approaches the pallet form-up area in Gazebo simulation environment. In the second figure the multi-pallet detection algorithm is applied along with the global planner algorithm that navigates the robot towards the alignment state. A point-to-point visual servoing controller drives the pallet-truck towards the selected pallet, as illustrated in third figure. The last figure outlines the docking and final pick-up of the pallet with a joint state controller.

Lifelong mapping for dynamic factory floor environments

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Lifelong mapping for dynamic factory floor environments

Mapping the factory floor environments

The HR-Recycler project aims to automate the transportation of WEEE devices and disassembled components within a collaborative factory floor environment. To achieve this, different type of AGVs dedicated to serve specific roles during the disassembly process will be developed. To enable AGVs navigation in such environments, simultaneously localization and mapping (SLAM) methods will be utilized. However, when it comes to highly dynamic environments such as factory floors with moving objects the built maps with classic SLAM are soon get obsolete and the robot navigation performance degrades.

Technical Challenges

Despite the leaps of progress that have been made in the field of mobile robotics in recent years, one major challenge that AGVs still face is that of long-term autonomous operation in dynamic environments. In the HR-Recycler scenario where the AGV operates in a factory floor (see Figure 1), it has to deal with changing conditions where other robots, workers, moving objects such as pallets and even commodities move around the factory environment. In this scenario with the typical SLAM mapping the static objects (such as walls) will constitute only a fraction of the existing information in the map during robot navigation. If we consider this map as the ground truth, and use it disregarding ongoing changes, it is very challenging to maintain stable robot localization even if a robust localization filter will be employed.

Figure 1 Gazebo simulation of the BIANATT S.A. (End-User) factory floor environment. Note that the majority   of the existing information in the illustrated infrastructure corresponds to dynamic objects.

Robotic Solution: Life-long mapping

CERTH-ITI developed an essential solution on solving this issue by employing the life-long mapping approach which will has the ability to distinguish static and dynamic areas and handle this information accordingly during robot navigation. The ability to identify areas that exhibit high or low dynamics can improve the navigation of mobile robots as well as improve the process of long-term mapping of the environment. We utilized temporal persistence modeling in order to predict the state of cells in the life-long map by gathering rare observations from the on-board the robot sensors. This allows the modeling of the objects persistence in the map and provides to the system with prior knowledge regarding the occupancy of the area where robot operates. The method allows robot navigation by avoiding congested areas deteriorating the re-plans leading thus the robot to its target location without unnecessary maneuvering. Simulation results on life-long mapping with temporal persistence modelling are outlined in Figure 2, which illustrates the static metric map (left) and the probability of the dynamic areas through temporal persistence modeling (right).

The Visual Analytics Lab (VARLab) of CERTH-ITI

Figure 2  The outcome of life-long mapping . The left image illustrates the metric map produced by the classical 2D SLAM, while the  right image corresponds to the dynamic obstacles persistence probability calculated from the temporal persistence modeling.

3D modeling a virtual training system

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HR-Recycler aims at developing a hybrid human-robot collaborative environment for the disassembly of waste of electrical and electronic equipment. Within this environment, humans and robots will be working collaboratively by sharing different processing and manipulation tasks.

The HR-Recycler project will implement learning paradigms in virtual reality (VR) settings, for which a recycling plant will be modeled in 3D to be displayed in a VR environment. One objective is to capture accurately the interaction of the workers with the environment and especially the objects present in the factory. Another objective is to allow workers to practice disassembly procedures and to properly interact with the environment. It is necessary to deliver a VR environment as realistic as possible and to model the procedures so that they can lead to more efficient training of the workers.

Within the project DIGINEXT is developing an effective virtual training system, targeting the WEEE recycling industry. The authoring tool (Procedure Editor) aims at easing the creation of WEEE disassembly virtual training experiences by minimizing production efforts.

Unlike a generic 3D tool, DIGINEXT’s solution does not require any specific programming or 3D modeling skills and allows us to adopt a smoother and faster workflow as compared to classical solutions based on a 3D engine. This means that a field expert can create procedures for virtual training even with very limited programming skills.

The tool is used by following these simple steps:

The first step consists of 3D modeling and animation: setting the scene (building, furniture) and selecting the WEEE that will be recycled.

The 3D model is next split and each part that requires an operation (e.g. screws) has an action attributed (e.g. unscrew, open, etc.). The figure below shows the list of parts for a microwave oven, and for a specific screw it can be seen that it is a “screwable” part that has 2 possible actions “screw” and “unscrew” (circled in red).

The procedure is then constructed very simply by assembling boxes within the graphical environment, by attributing these actions in a linear way that follows the processing as done in the actual factories.

Once the procedure is finished it can be either played using the mouse (see picture below) or exported following the S1000D norm and be used e.g. for I.A. training by other members of the project’s consortium.

Michael Darques, DXT

On improving the data collection process, an end user’s view

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With the advancement of technology, the adopted processes need to accompany this advance. The main objective of the changes seeks to meet continuous improvement, in order to improve working conditions and the satisfaction of employees who work at the factory, reducing the risks to human health and safety. The processes of recycling waste electrical and electronic equipment are composed of numerous dangerous, monotone, and time-consuming tasks that must be replaced. It is here that automatic processes based on robotics appear as a solution. Through the robotics solutions proposed in the HR-Recycler project, workers and robots will collaborate in a synchronized manner. With this, the risk to the health and safety of workers due to the handling of potentially hazardous waste will be reduced and workers will be able to focus on tasks raising the level of quality, value, and qualification. And as a result, the companies will be able to increase their recycling rates.

It was expected to have collected images between partners Sadako and Interecycling. But unfortunately, with some delays and the difficult situation of COVID-19 that is affecting the whole world, this stage is still to be accomplished. We are hopeful that this whole scenario will soon improve rapidly in order to complete this stage.

Regarding the recordings, we will collect data for the Classification step in the same way as we have done in the previous recording campaigns, in the other end-users partners, Indumetal and Bianatt. For the Disassembly step, we will record hand-held camera data, later used for the co-bot operation, as well as whole-body images used for action recognition software.

The recordings will not be fundamentally different from those made previously, in the end-users mentioned above. In order to improve and to make better the results already obtained, we can try some calibration of the camera for the disassembly step and use the images later at Sadako to assess operational performance rather than only to build neural networks. This camera calibration would allow us to better locate the operator in space and use all the developed software functionalities.

So with this stage and recordings, we hope to gather data to improve our existing neural networks, both for the Classification and Disassembly steps, and to use some of this data to measure our operational performance. To get to know a little better the work developed in the area of ​​image collection you can consult the blog post of SDK from last 26/06/2020 (

Ana Catarina, INT
Albert Tissot, SDK

Monitoring and reviewing the impact assessment

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Monitoring and reviewing the impact assessment [in HR Recycler]

 In HR Recycler the consortium aims to develop a collaborative human-robot  to aid workers in the performance of their heavy tasks in recycling plants of waste electrical and electronic (WEEE) materials. The objective is that workers and robots “collaborate” in a joint and synchronised manner. What are the impacts of such a development? How it might affect workers rights and society as a whole? One main tool to ensure legal and ethical compliance is the performance of an impact assessment.

An impact assessment evaluates the origin, nature and severity of impacts that the (processing) operations, real or hypothetical, of a specific project entail. The ‘architecture’ for impact assessment typically consists of three main elements, the ‘framework’, the ‘method’ and the ‘template’. A framework constitutes an ‘essential supporting structure’ or organizational arrangement for something, which, in this context, concerns the policy for impact assessment, and defines and describes the conditions and principles thereof. In turn, a method, which is a ‘particular procedure for accomplishing or approaching something’, concerns the practice of impact assessment and defines the consecutive and/or iterative steps to be undertaken to perform such a process. A method corresponds to a framework and can be seen as a practical reflection thereof. These two elements have been identified in D2.2, being public. Lastly, a template for the assessment process can be seen as a practical implementation of a method (i.e. a procedure therefor, comprising consecutive and/or iterative steps) for impact assessment, itself reflecting a framework therefor (i.e. conditions and principles).

Building on the TARES impact assessment (Truthfulness, Authenticity, Respect, Equity and Social Responsibility), elaborated previously in another blogpost, VUB continues to identify legal, ethical and societal impacts that the project technology might entail, and to provide recommendations. This exercise necessitates broadening the picture of the project, so as to include relevant societal concerns, instead of solely focusing on legal matters (e.g. data protection).

To report the first version of the TARES impact assessment, an initial template had been sent out, where all partners had to answer specific questions from a technical and end-user point of view. The process of impact assessment shall be receptive and collaborative, i.e. technology developers work alongside the assessor’s team. During the first version of the TARES impact assessment, several issues were identified, such as that workers are considered to be ‘vulnerable subjects’, and consent to participate in the research project is not deemed to be freely given by data protection authorities. For instance, as a recommendation, an independent observer role to collect consent forms is warranted. The results of the report are confidential, for the time being, due to legitimate secrecy, and are illustrated in D2.3. However, a summary of the process may be published in the future.

The next tasks comprise mostly monitoring, review and update – to be achieved periodically with three deliverables – reports. The continuity of the TARES impact assessment aims at anticipating the risks and at adopting a mitigation strategy with recommendations for the further development of the technology. As the system is integrated, tested and evaluated, VUB will repeat this impact assessment, in three phases every 6 months, reporting after each phase of the project pilots. This means that a similar questionnaire, duly adapted, will be shared with all partners, so as to monitor closely the progress of each phase and the compliance to the mitigation strategy and recommendations; the latter will be adjusted to take into account the responses received. Assessors are currently revising their IAs, because things do change and there is a need to keep up in order to appropriately address upcoming issues. By doing this, the impact assessment aspires to be a ‘living instrument’, that evolves with the project and is able to assess ongoing changes and impacts.

To that end, VUB and specifically d.pia.lab recently announced for public consultation the third policy brief, which proposes a template for a report from the process of data protection impact assessment (DPIA); this reflects the best practice for impact assessment and, at the same time, conforms to the requirements of the General Data Protection Regulation GDPR. By utilizing the template as well as its subsequent modifications after the public consultation, VUB aims to better structure and perform the monitoring and review phase (including updates) of the assessment process in HR-Recycler.

If you need wish to receive further information, do not hesitate to subscribe to the HR-Recycler newsletter here.

Nikos Ioannidis & Sergi Vazquez Maymir

August 2020

Tactile and haptic exploration to overcome uncertainty!

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Exploring the Environment: Haptic regression

The HR-Recycler project aims at creating a hybrid collaboration environment for disassembly of Waste of Electrical and Electronic Equipment (WEEE) materials, where humans and robots collaborate with the goal of disassembling WEEE products. Even for a single device type (e.g., microwave), recycling plants are interested in many variations that come in different shapes, weights, and disassembly steps. The complexity is further exacerbated when the mechanical structures of these devices are damaged which is often the case for disposed WEEE materials. When disassembling devices without knowing their construction plans, fixtures are easily not detected by visual sensors – either because they are not visible, such as snap-in fixtures, or because they are not properly recognized, such as recessed screws. In these situations, a solution is required for understanding the object and identifying its components.  Hence, a major challenge in disassembling an unknown object is to overcome these various sources of uncertainty. The shape of an object can be approximated well from vision and depth sensors. However, the information may be incomplete as visually occluded structures and the material composition that affects mechanical characteristics remains out of reach for such sensors. Thus, we are working at providing a robot with the ability to exploit its force sensing capability to refine the knowledge about these objects by interacting with the object.

Technical Challenges

By observing how robot force applied to the object affects interaction dynamics between them, we can improve the knowledge about the object properties, particularly material properties and estimation of structural features. This approach known as tactile and haptic exploration can significantly improve the results of visual object identification methods for complex objects with hidden structures.

Robotic Solution: Haptic SLAM

Haptic SLAM is dedicated to modeling the environment using haptic sensory data without visual feedback, for which haptic regression is an important technology. The entire space of the environment is divided into grids on which the probabilistic environmental model is constructed. Specifically, the probabilistic density functions of the objects in the environment are updated based on collected haptic data using the Bayesian inference and haptic regression. To improve the efficiency of large state space, occupancy and inference grids are applied and adopted with the discretized representation of the workspace. The represented model is built from iteratively collected sensor data given an initial belief over the geometry of the object.  An imminent challenge lies in the refinement of the estimated geometric shape of the underlying object. The critical point is to infer the shape candidates represented via analytical parameterization of different shapes from the initial sensor data and add this prior knowledge to further measurements. As a result, the overall dataset allows not only to update the geometric decomposition but also the material decomposition of the object. An example of the modeling process of an object can be referred in Figure 1, and the modeling results of the object are illustrated in Figure 2.

Indumetal discusses Industry 4.0 integration with Circular Economy practices

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The Industry 4.0 concept refers to the so-called fourth industrial revolution, which involves digital transformation of the industry with the integration and digitization of all the industrial processes that make up the value chain. The proper integration of the technologies that industry 4.0 offers, enable significant benefits such as greater productivity and better resource management, more efficient decision making based on real information, optimal and integrated production processes, increased flexibility to achieve massive and personalized production in real time, reduction of operational time and reduction of defects percentage in factories. Therefore, Industry 4.0 is characterized by adaptability, flexibility and efficiency.

 The current ‘linear economy’ where everyday products are made, used and disposed should no longer work for businesses, society or environment, as earth’s raw materials are not limitless. Nowadays, there is a need for finding a new path of economic development that includes dealing with waste generation. Hence, there is a need for a transition into a new circular business model that recycles and reuses such waste with the objective of transforming it into higher value-added products to meet the current demands of society.

The integration of I4.0 technologies with circular economy practices have the capacity to significantly reduce  natural resources consumption. New and innovative solutions could make possible to extract even more raw materials, especially critical raw materials, from waste components in order to return them to the cycle. Thus, becoming circular in industrial settings not only involves saving a considerable amount of time, raw materials and consequently, money. It also opens all kinds of opportunities to innovation. To progress in this path, the recycling plant of the future should integrate all kind of I4.0 solutions, having as a result a smart plant, capable of easily adapting to changes in the composition and quantity of the materials to be treated and ensuring at the same time workers safety, avoiding human contact with waste as much as possible. HR-Recycler will help Indumetal in this approach, since it will create a hybrid collaboration environment, where humans and robots will harmoniously share and undertake at the same time different processing and manipulation tasks.

This is not only an improvement for the company’s competitiveness but it is also a step forward towards improving the WEEE collection rate in the European Union. For instance, in 2017, the collection rate of WEEE was 46 % in the EU and its recycling rate was 38,8 %. These rates are no longer acceptable for transitioning EU industry into a circular economy model.

In Indumetal we believe that circular economy strategies are the key for successfully implementing Industry 4.0, at the same time that Industry 4.0 provides technology to activates those strategies.

SADAKO showcase their developed technologies!

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SADAKO explains their role in HR-Recycler and illustrates how their developed technologies and models are applied to various stages of the project.

As an AI and computer vision company, the role of Sadako in the HR-Recycler project is to provide the crucial vision algorithms to the robotic systems, allowing the perception of the environment in which the machines evolve.

Because of the wide variety of vision-dependent systems being employed in this project (different types of robot arms and Automatically Guided Vehicles, central factory planner), many different challenges are encountered which need each their own specific solution. All of the designed solutions rely on the use of RGB-D cameras and computers capable of Neural Network inference, and will be a good illustration of the many capabilities and applications of computer vision.

Until now, Sadako has developed solutions for the Classification and Disassembly stages of the recycling process, as well as for the AGV navigation.

Classification: 3D perception and object recognition for bin picking
Disassembly: Video-inference for gesture recognition, needed for Human-Robot collaboration and task status management
AGV: Human location perception, needed for optimal AGV path planning and human safety

  • Classification:

This stage is the first of the recycling process, where WEEE arrives mixed in baskets and has to be sorted by type of object (Microwave, Flat Panel Display, PC Tower, Emergency Lamp)
The goal in this stage of the process is to identify the type and 3D orientation of each object lying in the basket, and provide the robotic arm with a picking decision. This is achieved through the use of Deep Learning vision algorithms and point cloud processing

  • Disassembly:

The following process step, Disassembly, is where the previously sorted objects are dismantled and their parts are sorted by material to be sent to their final recycling location. The dismantling is made by an operator, with the support of a collaborative robotic arm. In order to achieve collaboration with the robot, Sadako has developed software based on Neural Networks capable of detecting actions in a video, in contrast with standard computer vision that detects objects in an image. This software is able to interpret the gestures being made by the operator, allowing him or her for example to stop or start the robot at any time. The software also allows the monitoring of the dismantling task’s status.

  • AGV:

AGVs are used to transport WEEE objects between each stage of the dismantling process, reducing high-strain and repetitive physical work for the operators. To ensure that the AGV trajectory does not interfere with the movement of humans around the factory, the central system has to be aware of their position. To achieve this, Sadako has developed a solution based on Openpose, a software that extracts the position of the joints of humans on an image, crossed with depth information provided by the camera, thus allowing to perceive the position and orientation of operators in three dimensions. This software can be deployed at the Disassembly workstation as well as onboard the AGVs.

In its compromise of developing technology to improve the world and reduce health risks endured by humans in the industry, Sadako is staying strongly active and working in the progress of the HR-Recycler project, despite the hard times imposed by the pandemic situation