FASTEN: EU-Brazil cooperation in IoT for manufacturing. The Embraer use

Reis, Ricardo; Diniz, Flávio; Mizioka, Luciana; Yamasaki, Rosana; Lemos, Gléverson; Quintiães, Marta; Menezes, Ruben; Caldas, Narciso; Vita, Roberto; Schultz, Ralph; Arrais, Rafael; Pereira, A.

doi:10.1051/matecconf/201930404007

Cited by 2 publications

(2 citation statements)

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“…In the third scenario, we demonstrate a prototype of an industrial application scenario where this autonomous exploration system would allow the unveiling of parts inside containers for further manipulation in bin-picking operations. The industrial scenario was considered on the scope of the H2020 FASTEN R&D project [34] and required an alteration on the hardware architecture employed.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed system was integrated in the Embraer use case of the FASTEN project, where a mobile manipulator is being developed for interfacing with an automated warehouse for the collaborative assembly of kits of aircraft wings components that are then to be transported to the production lines [34]. This use case aims to enable flexible automation of the parts kit assembly operation by the integration of IoT data streams from the collaborative robotic system with data from the corporate systems, monitoring the operation and coordinating the robot while dealing with a high diversity of parts, dynamism of orders, and unstructured environment.…”

Section: Test Scenario 3: Industrial Applicationmentioning

confidence: 99%

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Autonomous Scene Exploration for Robotics: A Conditional Random View-Sampling and Evaluation Using a Voxel-Sorting Mechanism for Efficient Ray Casting

Santos

Oliveira

Arrais

et al. 2020

Sensors

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Carrying out the task of the exploration of a scene by an autonomous robot entails a set of complex skills, such as the ability to create and update a representation of the scene, the knowledge of the regions of the scene which are yet unexplored, the ability to estimate the most efficient point of view from the perspective of an explorer agent and, finally, the ability to physically move the system to the selected Next Best View (NBV). This paper proposes an autonomous exploration system that makes use of a dual OcTree representation to encode the regions in the scene which are occupied, free, and unknown. The NBV is estimated through a discrete approach that samples and evaluates a set of view hypotheses that are created by a conditioned random process which ensures that the views have some chance of adding novel information to the scene. The algorithm uses ray-casting defined according to the characteristics of the RGB-D sensor, and a mechanism that sorts the voxels to be tested in a way that considerably speeds up the assessment. The sampled view that is estimated to provide the largest amount of novel information is selected, and the system moves to that location, where a new exploration step begins. The exploration session is terminated when there are no more unknown regions in the scene or when those that exist cannot be observed by the system. The experimental setup consisted of a robotic manipulator with an RGB-D sensor assembled on its end-effector, all managed by a Robot Operating System (ROS) based architecture. The manipulator provides movement, while the sensor collects information about the scene. Experimental results span over three test scenarios designed to evaluate the performance of the proposed system. In particular, the exploration performance of the proposed system is compared against that of human subjects. Results show that the proposed approach is able to carry out the exploration of a scene, even when it starts from scratch, building up knowledge as the exploration progresses. Furthermore, in these experiments, the system was able to complete the exploration of the scene in less time when compared to human subjects.

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Section: Resultsmentioning

confidence: 99%

Section: Test Scenario 3: Industrial Applicationmentioning

confidence: 99%