Browsing by Keyword "info:eu-repo/grantAgreement/EC/H2020/820689/EU/Seamless and safe human - centred robotic applications for novel collaborative workplaces/SHERLOCK"
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Item Dual arm co-manipulation architecture with enhanced human–robot communication for large part manipulation(2020-10-29) Ibarguren, Aitor; Eimontaite, Iveta; Outón, José Luis; Fletcher, Sarah; ROBOTICA_AUTOMA; ROBOTICA_FLEXThe emergence of collaborative robotics has had a great impact on the development of robotic solutions for cooperative tasks nowadays carried out by humans, especially in industrial environments where robots can act as assistants to operators. Even so, the coordinated manipulation of large parts between robots and humans gives rise to many technical challenges, ranging from the coordination of both robotic arms to the human–robot information exchange. This paper presents a novel architecture for the execution of trajectory driven collaborative tasks, combining impedance control and trajectory coordination in the control loop, as well as adding mechanisms to provide effective robot-to-human feedback for a successful and satisfactory task completion. The obtained results demonstrate the validity of the proposed architecture as well as its suitability for the implementation of collaborative robotic systems.Item Histogram-Based Descriptor Subset Selection for Visual Recognition of Industrial Parts(2020-06-01) Merino, Ibon; Azpiazu, Jon; Remazeilles, Anthony; Sierra, Basilio; Tecnalia Research & Innovation; ROBOTICA_FLEX; Robótica MédicaThis article deals with the 2D image-based recognition of industrial parts. Methods based on histograms are well known and widely used, but it is hard to find the best combination of histograms, most distinctive for instance, for each situation and without a high user expertise. We proposed a descriptor subset selection technique that automatically selects the most appropriate descriptor combination, and that outperforms approach involving single descriptors. We have considered both backward and forward mechanisms. Furthermore, to recognize the industrial parts a supervised classification is used with the global descriptors as predictors. Several class approaches are compared. Given our application, the best results are obtained with the Support Vector Machine with a combination of descriptors increasing the F1 by 0.031 with respect to the best descriptor alone.Item Path Driven Dual Arm Mobile Co-Manipulation Architecture for Large Part Manipulation in Industrial Environments(2021-10-05) Ibarguren, Aitor; Daelman, Paul; Tecnalia Research & Innovation; ROBOTICA_AUTOMACollaborative part transportation is an interesting application as many industrial sectors require moving large parts among different areas of the workshops, using a large amount of the workforce on this tasks. Even so, the implementation of such kinds of robotic solutions raises technical challenges like force-based control or robot-to-human feedback. This paper presents a path-driven mobile co-manipulation architecture, proposing an algorithm that deals with all the steps of collaborative part transportation. Starting from the generation of force-based twist commands, continuing with the path management for the definition of safe and collaborative areas, and finishing with the feedback provided to the system users, the proposed approach allows creating collaborative lanes for the conveyance of large components. The implemented solution and performed tests show the suitability of the proposed architecture, allowing the creation of a functional robotic system able to assist operators transporting large parts on workshops.