18 research outputs found
Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
With the advent of agriculture 3.0 and 4.0, researchers are increasingly
focusing on the development of innovative smart farming and precision
agriculture technologies by introducing automation and robotics into the
agricultural processes. Autonomous agricultural field machines have been
gaining significant attention from farmers and industries to reduce costs,
human workload, and required resources. Nevertheless, achieving sufficient
autonomous navigation capabilities requires the simultaneous cooperation of
different processes; localization, mapping, and path planning are just some of
the steps that aim at providing to the machine the right set of skills to
operate in semi-structured and unstructured environments. In this context, this
study presents a low-cost local motion planner for autonomous navigation in
vineyards based only on an RGB-D camera, low range hardware, and a dual layer
control algorithm. The first algorithm exploits the disparity map and its depth
representation to generate a proportional control for the robotic platform.
Concurrently, a second back-up algorithm, based on representations learning and
resilient to illumination variations, can take control of the machine in case
of a momentaneous failure of the first block. Moreover, due to the double
nature of the system, after initial training of the deep learning model with an
initial dataset, the strict synergy between the two algorithms opens the
possibility of exploiting new automatically labeled data, coming from the
field, to extend the existing model knowledge. The machine learning algorithm
has been trained and tested, using transfer learning, with acquired images
during different field surveys in the North region of Italy and then optimized
for on-device inference with model pruning and quantization. Finally, the
overall system has been validated with a customized robot platform in the
relevant environment
An Adaptive Row Crops Path Generator with Deep Learning Synergy
The autonomous navigation of agricultural field machines strongly depends on the global path generation system. Indeed, a correct and effective path construction heavily influences the overall navigation stack compromising the successfulness of the robot mission. However, the most commonly used search algorithms struggle to adapt to environments where a significant prior knowledge of the domain is not negligible. Despite this crucial factor, path generation for row-based crops has received little attention from the research community so far. The proposed research introduces a novel global path planning system that works in synergy with a deep learning model to provide an accurate and centered path with respect to the rows of the analyzed crop. It guarantees the full coverage of the given occupancy grid with less processing time compared to other available literature solutions. Moreover, the presented methodology can detect an anomaly in the path generation and provide the hypothetical user feedback of the missing full coverage of the given crop. Indeed, especially in a practical application, the correct coverage and centrality of the path are essential for effective autonomous navigation. Experimentation with synthetic and real-world satellite occupancy grid maps clearly show the advantages of the proposed methodology and its intrinsic robustness
Deep Semantic Segmentation at the Edge for Autonomous Navigation in Vineyard Rows
Precision agriculture is a fast-growing field that aims at introducing
affordable and effective automation into agricultural processes. Nowadays,
algorithmic solutions for navigation in vineyards require expensive sensors and
high computational workloads that preclude large-scale applicability of
autonomous robotic platforms in real business case scenarios. From this
perspective, our novel proposed control leverages the latest advancement in
machine perception and edge AI techniques to achieve highly affordable and
reliable navigation inside vineyard rows with low computational and power
consumption. Indeed, using a custom-trained segmentation network and a
low-range RGB-D camera, we are able to take advantage of the semantic
information of the environment to produce smooth trajectories and stable
control in different vineyards scenarios. Moreover, the segmentation maps
generated by the control algorithm itself could be directly exploited as
filters for a vegetative assessment of the crop status. Extensive
experimentations and evaluations against real-world data and simulated
environments demonstrated the effectiveness and intrinsic robustness of our
methodology.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS 2021
DeepWay: A Deep Learning waypoint estimator for global path generation
Agriculture 3.0 and 4.0 have gradually introduced service robotics and automation into several agricultural processes, mostly improving crops quality and seasonal yield. Row-based crops are the perfect settings to test and deploy smart machines capable of monitoring and manage the harvest. In this context, global path generation is essential either for ground or aerial vehicles, and it is the starting point for every type of mission plan. Nevertheless, little attention has been currently given to this problem by the research community and global path generation automation is still far to be solved. In order to generate a viable path for an autonomous machine, the presented research proposes a feature learning fully convolutional model capable of estimating waypoints given an occupancy grid map. In particular, we apply the proposed data-driven methodology to the specific case of row-based crops with the general objective to generate a global path able to cover the extension of the crop completely. Extensive experimentation with a custom made synthetic dataset and real satellite-derived images of different scenarios have proved the effectiveness of our methodology and demonstrated the feasibility of an end-to-end and completely autonomous global path planner
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Single-cell profiling of human gliomas reveals macrophage ontogeny as a basis for regional differences in macrophage activation in the tumor microenvironment
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Single-cell profiling of human gliomas reveals macrophage ontogeny as a basis for regional differences in macrophage activation in the tumor microenvironment
Single-cell profiling of human gliomas reveals macrophage ontogeny as a basis for regional differences in macrophage activation in the tumor microenvironment
Abstract Background Tumor-associated macrophages (TAMs) are abundant in gliomas and immunosuppressive TAMs are a barrier to emerging immunotherapies. It is unknown to what extent macrophages derived from peripheral blood adopt the phenotype of brain-resident microglia in pre-treatment gliomas. The relative proportions of blood-derived macrophages and microglia have been poorly quantified in clinical samples due to a paucity of markers that distinguish these cell types in malignant tissue. Results We perform single-cell RNA-sequencing of human gliomas and identify phenotypic differences in TAMs of distinct lineages. We isolate TAMs from patient biopsies and compare them with macrophages from non-malignant human tissue, glioma atlases, and murine glioma models. We present a novel signature that distinguishes TAMs by ontogeny in human gliomas. Blood-derived TAMs upregulate immunosuppressive cytokines and show an altered metabolism compared to microglial TAMs. They are also enriched in perivascular and necrotic regions. The gene signature of blood-derived TAMs, but not microglial TAMs, correlates with significantly inferior survival in low-grade glioma. Surprisingly, TAMs frequently co-express canonical pro-inflammatory (M1) and alternatively activated (M2) genes in individual cells. Conclusions We conclude that blood-derived TAMs significantly infiltrate pre-treatment gliomas, to a degree that varies by glioma subtype and tumor compartment. Blood-derived TAMs do not universally conform to the phenotype of microglia, but preferentially express immunosuppressive cytokines and show an altered metabolism. Our results argue against status quo therapeutic strategies that target TAMs indiscriminately and in favor of strategies that specifically target immunosuppressive blood-derived TAMs
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Single-cell profiling of human gliomas reveals macrophage ontogeny as a basis for regional differences in macrophage activation in the tumor microenvironment.
BACKGROUND:Tumor-associated macrophages (TAMs) are abundant in gliomas and immunosuppressive TAMs are a barrier to emerging immunotherapies. It is unknown to what extent macrophages derived from peripheral blood adopt the phenotype of brain-resident microglia in pre-treatment gliomas. The relative proportions of blood-derived macrophages and microglia have been poorly quantified in clinical samples due to a paucity of markers that distinguish these cell types in malignant tissue. RESULTS:We perform single-cell RNA-sequencing of human gliomas and identify phenotypic differences in TAMs of distinct lineages. We isolate TAMs from patient biopsies and compare them with macrophages from non-malignant human tissue, glioma atlases, and murine glioma models. We present a novel signature that distinguishes TAMs by ontogeny in human gliomas. Blood-derived TAMs upregulate immunosuppressive cytokines and show an altered metabolism compared to microglial TAMs. They are also enriched in perivascular and necrotic regions. The gene signature of blood-derived TAMs, but not microglial TAMs, correlates with significantly inferior survival in low-grade glioma. Surprisingly, TAMs frequently co-express canonical pro-inflammatory (M1) and alternatively activated (M2) genes in individual cells. CONCLUSIONS:We conclude that blood-derived TAMs significantly infiltrate pre-treatment gliomas, to a degree that varies by glioma subtype and tumor compartment. Blood-derived TAMs do not universally conform to the phenotype of microglia, but preferentially express immunosuppressive cytokines and show an altered metabolism. Our results argue against status quo therapeutic strategies that target TAMs indiscriminately and in favor of strategies that specifically target immunosuppressive blood-derived TAMs