From GitHub to GitHub with XProc: An approach to automate documentation for an open source project with XProc and the GitHub Web API

Kraetke, Martin

doi:10.4242/balisagevol17.kraetke01

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Due to the path-planning architecture of our approach, the main trajectory-planning process for deploying the robot in the real world relies on the specific software platform, parametric settings, and the architecture of the particular robot in use. Hence, to demonstrate the practical applicability of our method, and to underscore its potential for realworld robotic operations, we made use of the Kinova Kortex API [49] for our Kinova Gen3 robot to perform bin-picking operations. This facilitates the conversion of our established paths into actionable trajectories.…”

Section: Real-world Implementationmentioning

confidence: 99%

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

Ghafarian Tamizi,

Honari,

Nozdryn-Plotnicki

et al. 2024

Robotica

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Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path-planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths.

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Section: Real-world Implementationmentioning

confidence: 99%

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

Ghafarian Tamizi,

Honari,

Nozdryn-Plotnicki

et al. 2024

Robotica

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“…Al abrir esta carpeta con el navegador de la biblioteca de GitHub, el archivo aparecerá en la parte inferior de la página. Desde que nos mudamos a GitHub, a menudo usamos este enfoque para brindar documentación concisa de las versiones a los desarrolladores [9].…”

Section: Sistema De Control De Versionesunclassified

Influencia del código abierto y su simplicidad en el desarrollo de sistemas web académicos

Adriano,

Guapi Cuji

2023

RCTU-UPSE

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Las nuevas tecnologías Open Source han influido en el desarrollo de sistemas informáticos aplicados a los procesos de los institutos de educación superior. La presente investigación muestra la viabilidad de automatizar la gestión de matrículas y notas del Instituto Superior Tecnológico “San Gabriel”. La investigación aplicada fue la cualitativa y se desarrolló aplicando métodos empíricos de investigación, con lo que se determinó los requerimientos y necesidades, de igual forma se realizó una búsqueda bibliográfica en revistas científicas y base de datos como Scielo, Latindex 2.0, entre otros. El análisis, desarrollo y la implementación se efectuó a través de la metodología de desarrollo ágil Scrum que permite dividir en pequeños bloques de funcionalidades para cada módulo del sistema para posteriormente ir mejorando y ajustando los resultados, además la colaboración constante del cliente con el equipo Scrum garantiza un software de calidad. Para la construcción del sistema se utilizó la programación orientada a objetos, arquitectura N-Capas y Laravel como framework de desarrollo. Los resultados permitieron automatizar la información del control de matrículas y notas de la institución, a través de una interfaz amigable y fácil para los usuarios, que se evidenció en la facilidad de manejo por parte de los estudiantes, docentes y personal administrativo.

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“…Section 6 explains our experiments and numerical results, and finally, we provide a summary of our conclusions in Section 7. As described in [8], and represented in Fig. 1, one sample in the dataset consists of a tuple containing the following: a topology object, which holds data on the physical components (nodes and links) of the network, a routing matrix, which holds the unique path linking each node pair in the topology, and a traf ic matrix, which contains information on the lows between each pair of nodes.…”

Section: Introductionmentioning

confidence: 99%

Designing graph neural networks training data with limited samples and small network sizes

Junior Momo Ziazet,

Charles Boudreau,

Oscar Delgado

et al. 2023

ITU J-FET

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Machine learning is a data-driven domain, which means a learning model's performance depends on the availability of large volumes of data to train it. However, by improving data quality, we can train effective machine learning models with little data. This paper demonstrates this possibility by proposing a methodology to generate high-quality data in the networking domain. We designed a dataset to train a given Graph Neural Network (GNN) that not only contains a small number of samples, but whose samples also feature network graphs of a reduced size (10-node networks). Our evaluations indicate that the dataset generated by the proposed pipeline can train a GNN model that scales well to larger networks of 50 to 300 nodes. The trained model compares favorably to the baseline, achieving a mean absolute percentage error of 5-6%, while being significantly smaller at 90 samples total (vs. thousands of samples for the baseline).

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From GitHub to GitHub with XProc: An approach to automate documentation for an open source project with XProc and the GitHub Web API

Cited by 5 publications

References 11 publications

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

End-to-end deep learning-based framework for path planning and collision checking: bin-picking application

Influencia del código abierto y su simplicidad en el desarrollo de sistemas web académicos

Designing graph neural networks training data with limited samples and small network sizes

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