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Publications

Publications by CRIIS

2019

Collaborative Welding System using BIM for Robotic Reprogramming and Spatial Augmented Reality

Authors
Tavares, P; Costa, CM; Rocha, L; Malaca, P; Costa, P; Moreira, AP; Sousa, A; Veiga, G;

Publication
Automation in Construction

Abstract
The optimization of the information flow from the initial design and through the several production stages plays a critical role in ensuring product quality while also reducing the manufacturing costs. As such, in this article we present a cooperative welding cell for structural steel fabrication that is capable of leveraging the Building Information Modeling (BIM) standards to automatically orchestrate the necessary tasks to be allocated to a human operator and a welding robot moving on a linear track. We propose a spatial augmented reality system that projects alignment information into the environment for helping the operator tack weld the beam attachments that will be later on seam welded by the industrial robot. This way we ensure maximum flexibility during the beam assembly stage while also improving the overall productivity and product quality since the operator no longer needs to rely on error prone measurement procedures and he receives his tasks through an immersive interface, relieving him from the burden of analyzing complex manufacturing design specifications. Moreover, no expert robotics knowledge is required to operate our welding cell because all the necessary information is extracted from the Industry Foundation Classes (IFC), namely the CAD models and welding sections, allowing our 3D beam perception systems to correct placement errors or beam bending, which coupled with our motion planning and welding pose optimization system ensures that the robot performs its tasks without collisions and as efficiently as possible while maximizing the welding quality. © 2019 Elsevier B.V.

2019

Modeling of video projectors in OpenGL for implementing a spatial augmented reality teaching system for assembly operations

Authors
Costal, CM; Veiga, G; Sousa, A; Rocha, L; Sousa, AA; Rodrigues, R; Thomas, U;

Publication
19th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2019

Abstract
Teaching complex assembly and maintenance skills to human operators usually requires extensive reading and the help of tutors. In order to reduce the training period and avoid the need for human supervision, an immersive teaching system using spatial augmented reality was developed for guiding inexperienced operators. The system provides textual and video instructions for each task while also allowing the operator to navigate between the teaching steps and control the video playback using a bare hands natural interaction interface that is projected into the workspace. Moreover, for helping the operator during the final validation and inspection phase, the system projects the expected 3D outline of the final product. The proposed teaching system was tested with the assembly of a starter motor and proved to be more intuitive than reading the traditional user manuals. This proof of concept use case served to validate the fundamental technologies and approaches that were proposed to achieve an intuitive and accurate augmented reality teaching application. Among the main challenges were the proper modeling and calibration of the sensing and projection hardware along with the 6 DoF pose estimation of objects for achieving precise overlap between the 3D rendered content and the physical world. On the other hand, the conceptualization of the information flow and how it can be conveyed on-demand to the operator was also of critical importance for ensuring a smooth and intuitive experience for the operator. © 2019 IEEE.

2019

Prototyping and Programming a Multipurpose Educational Mobile Robot - NaSSIE

Authors
Pinto, VH; Monteiro, JM; Gonçalves, J; Costa, P;

Publication
Robotics in Education - Advances in Intelligent Systems and Computing

Abstract

2019

Introduction to the Special Issue “Robotica 2016”

Authors
Cunha, B; Lima, J; Silva, M; Leitao, P;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract

2019

Map-Matching Algorithms for Robot Self-Localization: A Comparison Between Perfect Match, Iterative Closest Point and Normal Distributions Transform

Authors
Sobreira, H; Costa, CM; Sousa, I; Rocha, L; Lima, J; Farias, PCMA; Costa, P; Paulo Moreira, AP;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract
The self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics navigation field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to the algorithms accuracy, robustness and computational efficiency. In this paper, we present a comparison of three of the most used map-matching algorithms applied in localization based on natural landmarks: our implementation of the Perfect Match (PM) and the Point Cloud Library (PCL) implementation of the Iterative Closest Point (ICP) and the Normal Distribution Transform (NDT). For the purpose of this comparison we have considered a set of representative metrics, such as pose estimation accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset, containing several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article and is of paramount importance for real-time embedded systems with limited computing power that require accurate pose estimation and fast reaction times for high speed navigation. Moreover, we added to PCL a new algorithm for performing correspondence estimation using lookup tables that was inspired by the PM approach to solve this problem. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach in PCL and allowed the Iterative Closest Point algorithm to perform point cloud registration 5 to 9 times faster. © 2018 Springer Science+Business Media B.V., part of Springer Nature

2019

Optimal Perception Planning with Informed Heuristics Constructed from Visibility Maps

Authors
Pereira, T; Moreira, A; Veloso, M;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract
In this paper we consider the problem of motion planning for perception of a target position. A robot has to move to a position from where it can sense the target, while minimizing both motion and perception costs. The problem of finding paths for robots executing perception tasks can be solved optimally using informed search. In perception path planning, the solution when considering a straight line without obstacles is used as heuristic. In this work, we propose a heuristic that can improve the search efficiency. In order to reduce the node expansion using a more informed search, we use the robot Approximate Visibility Map (A-VM), which is used as a representation of the observability capability of a robot in a given environment. We show how the critical points used in A-VM provide information on the geometry of the environment, which can be used to improve the heuristic, increasing the search efficiency. The critical points allow a better estimation of the minimum motion and perception cost for targets in non-traversable regions that can only be sensed from further away. Finally, we show the contributed heuristic with improvements dominates the base PA* heuristic built on the euclidean distance, and then present the results of the performance increase in terms of node expansion and computation time. © 2018 Springer Science+Business Media B.V., part of Springer Nature

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