Ugens Harddisk: Robotbyggernes værktøjskasse

DR P1 Harddisken, dagen udgave af Harddisken som handler om robotter og om at programmere robotter

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Bælterobotten Armadillo gøres klar til første danske "Field Robot Event"

Down on the Farm, Will Robots Replace Immigrant Labor?

You´d think that the most challenging, the lowest-paid labor in the U.S. was safe from automation, but as robots become increasingly sophisticated that could change

Localization in orchards using Extended Kalman Filter for sensor-fusion - A FroboMind component

Making an automated vehicle navigate in rows of orchards is a feature, relevant for automating the plant nursing and cultivation of the trees. To be able to navigate accurate and reliably, the vehicle must know its position relative to the trees in the orchards

Robots Using ROS: ASuBot

Weeding in organic orchards is a tedious process done either mechanically or by weed burning. Researchers at University of Southern Denmark and Aarhus University created the ASuBot, a self-drive tractor, to handle navigate around trees in organic orchards

Hortibot: Feasibility study of a plant nursing robot performing weeding operations – part IV

Based on the development of a robotic tool carrier (Hortibot) equipped with weeding tools, a feasibility study was carried out to evaluate the viability of this innovative technology. The feasibility was demonstrated through a targeted evaluation adapted to the obtainable knowledge on the system performance in horticulture. A usage scenario was designed to set the implementation of the robotic system in a row crop of seeded bulb onions considering operational and functional constraints in organic crop, production. This usage scenario together with the technical specifications of the implemented system provided the basis for the feasibility analysis, including a comparison with a conventional weeding system. Preliminary results show that the automation of the weeding tasks within a row crop has the potential of significantly reducing the costs and still fulfill the operational requirements set forth. The potential benefits in terms of operational capabilities and economic viability have been quantified. Profitability gains ranging from 20 to 50% are achievable through targeted applications. In general, the analyses demonstrate the operational and economic feasibility of using small automated vehicles and targeted tools in specialized production settings

Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation

Korthals T, Kragh M, Christiansen P, Karstoft H, Jørgensen RN, Rückert U. Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation. Frontiers in Robotics and AI. 2018;5: 26.Today, agricultural vehicles are available that can automatically perform tasks such as weed detection and spraying, mowing, and sowing while being steered automatically. However, for such systems to be fully autonomous and self-driven, not only their specific agricultural tasks must be automated. An accurate and robust perception system automatically detecting and avoiding all obstacles must also be realized to ensure safety of humans, animals, and other surroundings. In this paper, we present a multi-modal obstacle and environment detection and recognition approach for process evaluation in agricultural fields. The proposed pipeline detects and maps static and dynamic obstacles globally, while providing process-relevant information along the traversed trajectory. Detection algorithms are introduced for a variety of sensor technologies, including range sensors (lidar and radar) and cameras (stereo and thermal). Detection information is mapped globally into semantical occupancy grid maps and fused across all sensors with late fusion, resulting in accurate traversability assessment and semantical mapping of process-relevant categories (e.g., crop, ground, and obstacles). Finally, a decoding step uses a Hidden Markov model to extract relevant process-specific parameters along the trajectory of the vehicle, thus informing a potential control system of unexpected structures in the planned path. The method is evaluated on a public dataset for multi-modal obstacle detection in agricultural fields. Results show that a combination of multiple sensor modalities increases detection performance and that different fusion strategies must be applied between algorithms detecting similar and dissimilar classes

Fluorescence spectroscopy as a potential metabonomic tool for early detection of colorectal cancer

Abstract Fluorescence spectroscopy Excitation Emission Matrix (EEM) measurements were applied on human blood plasma samples from a case control study on colorectal cancer. Samples were collected before large bowel endoscopy and included patients with colorectal cancer or with adenomas, and from individuals with other non malignant findings or no findings (N = 308). The objective of the study was to explore the possibilities for applying fluorescence spectroscopy as a tool for detection of colorectal cancer. Parallel Factor Analysis (PARAFAC) was applied to decompose the fluorescence EEMs into estimates of the underlying fluorophores in the sample. Both the pooled score matrix from PARAFAC, holding the relative concentrations of the derived components, and the raw unfolded spectra were used as basis for discrimination models between cancer and the various controls. Both methods gave test set validated sensitivity and specificity values around 0.75 between cancer and controls, and poor discriminations between the various controls. The PARA-FAC solution gave better options for analyzing the chemical mechanisms behind the discrimination, and revealed a blue shift in tryptophan emission in the cancer patients, a result that supports previous findings. The present findings show how fluorescence spectroscopy and chemometrics can help in cancer diagnostics, and with PARAFAC fluorescence spectroscopy can be a potential metabonomic tool