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NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.



Motion planning algorithms have seen considerable progress and expansion across various domains of science and technology during the last few decades, where rapid advancements in path planning and trajectory optimization approaches have been made possible by the conspicuous enhancements brought, among others, by sampling-based methods and convex optimization strategies. Although they have been investigated from various perspectives in the existing literature, recent developments aimed at integrating robots into social, healthcare, industrial, and educational contexts have attributed greater importance to additional concepts that would allow them to communicate, cooperate, and collaborate with each other, as well as with human beings, in a meaningful and efficient manner. Therefore, in this survey, in addition to a brief overview of some of the essential aspects of motion planning algorithms, a few vital considerations required for assimilating robots into real-world applications, including certain instances of social, urban, and industrial environments, are introduced, followed by a critical discussion of a set of outstanding issues worthy of further investigation and development in future scientific studies.

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The identification of individual tree logs along the wood procurement chain is a coveted goal within the forest industry. The tracing of logs from the sawmill back to the forest would support the legal and sustainable sourcing of wood, as well as increase the resource efficiency and value of harvested timber. In this work, using a dataset of thousands of Scots pine (Pinus sylvestris L.) log end images displaying varying perspectives, lighting, and aging effects, we develop and assess log identification methods based on deep convolutional neural networks. The estimated rank-1 accuracy of our final model on an independent test set of 99 logs is 84 and 91% when allowing for random rotations of the log ends and when keeping each log at approximately fixed orientation, respectively. We estimate the scaling of these methods up to a template pool size of 493 logs, which reveals a weak dependence of accuracy on pool size for logs at fixed orientation. The deep learning approach gives superior results to a classical local binary pattern method, and appears feasible in practice, assuming that pre-filtering of the log database can be leveraged depending on the use case and properties of the queried log image. We make our dataset publicly available.