22 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
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)
Understanding the use of current land cover, along with monitoring change over time, is vital for agronomists and agricultural agencies responsible for land management. The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring. Existing solutions dedicated to cropland mapping can be categorized based on per-pixel based and object-based. However, it is still challenging when more classes of agricultural crops are considered at a massive scale. In this paper, a novel and optimal deep learning model for pixel-based LC&CC is developed and implemented based on Recurrent Neural Networks (RNN) in combination with Convolutional Neural Networks (CNN) using multi-temporal sentinel-2 imagery of central north part of Italy, which has diverse agricultural system dominated by economic crop types. The proposed methodology is capable of automated feature extraction by learning time correlation of multiple images, which reduces manual feature engineering and modeling crop phenological stages. Fifteen classes, including major agricultural crops, were considered in this study. We also tested other widely used traditional machine learning algorithms for comparison such as support vector machine SVM, random forest (RF), Kernal SVM, and gradient boosting machine, also called XGBoost. The overall accuracy achieved by our proposed Pixel R-CNN was 96.5%, which showed considerable improvements in comparison with existing mainstream methods. This study showed that Pixel R-CNN based model offers a highly accurate way to assess and employ time-series data for multi-temporal classification tasks
Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks
Convolutional Neural Networks (CNNs) have been consistently proved
state-of-the-art results in image Super-Resolution (SR), representing an
exceptional opportunity for the remote sensing field to extract further
information and knowledge from captured data. However, most of the works
published in the literature have been focusing on the Single-Image
Super-Resolution problem so far. At present, satellite based remote sensing
platforms offer huge data availability with high temporal resolution and low
spatial resolution. In this context, the presented research proposes a novel
residual attention model (RAMS) that efficiently tackles the multi-image
super-resolution task, simultaneously exploiting spatial and temporal
correlations to combine multiple images. We introduce the mechanism of visual
feature attention with 3D convolutions in order to obtain an aware data fusion
and information extraction of the multiple low-resolution images, transcending
limitations of the local region of convolutional operations. Moreover, having
multiple inputs with the same scene, our representation learning network makes
extensive use of nestled residual connections to let flow redundant
low-frequency signals and focus the computation on more important
high-frequency components. Extensive experimentation and evaluations against
other available solutions, either for single or multi-image super-resolution,
have demonstrated that the proposed deep learning-based solution can be
considered state-of-the-art for Multi-Image Super-Resolution for remote sensing
applications
A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge
The vital statistics of the last century highlight a sharp increment of the
average age of the world population with a consequent growth of the number of
older people. Service robotics applications have the potentiality to provide
systems and tools to support the autonomous and self-sufficient older adults in
their houses in everyday life, thereby avoiding the task of monitoring them
with third parties. In this context, we propose a cost-effective modular
solution to detect and follow a person in an indoor, domestic environment. We
exploited the latest advancements in deep learning optimization techniques, and
we compared different neural network accelerators to provide a robust and
flexible person-following system at the edge. Our proposed cost-effective and
power-efficient solution is fully-integrable with pre-existing navigation
stacks and creates the foundations for the development of fully-autonomous and
self-contained service robotics applications
Action Transformer: A Self-Attention Model for Short-Time Human Action Recognition
Deep neural networks based purely on attention have been successful across
several domains, relying on minimal architectural priors from the designer. In
Human Action Recognition (HAR), attention mechanisms have been primarily
adopted on top of standard convolutional or recurrent layers, improving the
overall generalization capability. In this work, we introduce Action
Transformer (AcT), a simple, fully self-attentional architecture that
consistently outperforms more elaborated networks that mix convolutional,
recurrent, and attentive layers. In order to limit computational and energy
requests, building on previous human action recognition research, the proposed
approach exploits 2D pose representations over small temporal windows,
providing a low latency solution for accurate and effective real-time
performance. Moreover, we open-source MPOSE2021, a new large-scale dataset, as
an attempt to build a formal training and evaluation benchmark for real-time
short-time human action recognition. Extensive experimentation on MPOSE2021
with our proposed methodology and several previous architectural solutions
proves the effectiveness of the AcT model and poses the base for future work on
HAR
Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-wideband
Indoor autonomous navigation requires a precise and accurate localization
system able to guide robots through cluttered, unstructured and dynamic
environments. Ultra-wideband (UWB) technology, as an indoor positioning system,
offers precise localization and tracking, but moving obstacles and
non-line-of-sight occurrences can generate noisy and unreliable signals. That,
combined with sensors noise, unmodeled dynamics and environment changes can
result in a failure of the guidance algorithm of the robot. We demonstrate how
a power-efficient and low computational cost point-to-point local planner,
learnt with deep reinforcement learning (RL), combined with UWB localization
technology can constitute a robust and resilient to noise short-range guidance
system complete solution. We trained the RL agent on a simulated environment
that encapsulates the robot dynamics and task constraints and then, we tested
the learnt point-to-point navigation policies in a real setting with more than
two-hundred experimental evaluations using UWB localization. Our results show
that the computational efficient end-to-end policy learnt in plain simulation,
that directly maps low-range sensors signals to robot controls, deployed in
combination with ultra-wideband noisy localization in a real environment, can
provide a robust, scalable and at-the-edge low-cost navigation system solution.Comment: Accepted by ICAART 2021 - http://www.icaart.org
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
Local Planners with Deep Reinforcement Learning for Indoor Autonomous Navigation
Autonomous indoor navigation requires an elab- orated and accurate algorithmic stack, able to guide robots through cluttered, unstructured, and dynamic environments. Global and local path planning, mapping, localization, and decision making are only some of the required layers that undergo heavy research from the scientific community to achieve the requirements for fully functional autonomous navigation. In the last years, Deep Reinforcement Learning (DRL) has proven to be a competitive short-range guidance system solution for power-efficient and low computational cost point-to-point local planners. One of the main strengths of this approach is the possibility to train a DRL agent in a simulated environment that encapsulates robot dynamics and task constraints and then deploy its learned point-to-point navigation policy in a real setting. However, despite DRL easily integrates complex mechanical dynamics and multimodal signals into a single model, the effect of different sensor data on navigation performance has not been investigated yet. In this paper, we compare two different DRL navigation solutions that leverage LiDAR and depth camera information, respectively. The agents are trained in the same simulated environment and tested on a common benchmark to highlight the strengths and criticalities of each technique
UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture
Precision agriculture is considered to be a fundamental approach in pursuing
a low-input, high-efficiency, and sustainable kind of agriculture when
performing site-specific management practices. To achieve this objective, a
reliable and updated description of the local status of crops is required.
Remote sensing, and in particular satellite-based imagery, proved to be a
valuable tool in crop mapping, monitoring, and diseases assessment. However,
freely available satellite imagery with low or moderate resolutions showed some
limits in specific agricultural applications, e.g., where crops are grown by
rows. Indeed, in this framework, the satellite's output could be biased by
intra-row covering, giving inaccurate information about crop status. This paper
presents a novel satellite imagery refinement framework, based on a deep
learning technique which exploits information properly derived from high
resolution images acquired by unmanned aerial vehicle (UAV) airborne
multispectral sensors. To train the convolutional neural network, only a single
UAV-driven dataset is required, making the proposed approach simple and
cost-effective. A vineyard in Serralunga d'Alba (Northern Italy) was chosen as
a case study for validation purposes. Refined satellite-driven normalized
difference vegetation index (NDVI) maps, acquired in four different periods
during the vine growing season, were shown to better describe crop status with
respect to raw datasets by correlation analysis and ANOVA. In addition, using a
K-means based classifier, 3-class vineyard vigor maps were profitably derived
from the NDVI maps, which are a valuable tool for growers
Ultra-low-power Range Error Mitigation for Ultra-wideband Precise Localization
Precise and accurate localization in outdoor and indoor environments is a
challenging problem that currently constitutes a significant limitation for
several practical applications. Ultra-wideband (UWB) localization technology
represents a valuable low-cost solution to the problem. However,
non-line-of-sight (NLOS) conditions and complexity of the specific radio
environment can easily introduce a positive bias in the ranging measurement,
resulting in highly inaccurate and unsatisfactory position estimation. In the
light of this, we leverage the latest advancement in deep neural network
optimization techniques and their implementation on ultra-low-power
microcontrollers to introduce an effective range error mitigation solution that
provides corrections in either NLOS or LOS conditions with a few mW of power.
Our extensive experimentation endorses the advantages and improvements of our
low-cost and power-efficient methodology