37 research outputs found
Position-agnostic autonomous navigation in vineyards with Deep Reinforcement Learning
Precision agriculture is rapidly attracting research to efficiently introduce automation and robotics solutions to support agricultural activities. Robotic navigation in vineyards and orchards offers competitive advantages in autonomously monitoring and easily accessing crops for harvesting, spraying and performing time-consuming necessary tasks. Nowadays, autonomous navigation algorithms exploit expensive sensors which also require heavy computational cost for data processing. Nonetheless, vineyard rows represent a challenging outdoor scenario where GPS and Visual Odometry techniques often struggle to provide reliable positioning information. In this work, we combine Edge AI with Deep Reinforcement Learning to propose a cutting-edge lightweight solution to tackle the problem of autonomous vineyard navigation with-out exploiting precise localization data and overcoming task-tailored algorithms with a flexible learning-based approach. We train an end-to-end sensorimotor agent which directly maps noisy depth images and position-agnostic robot state information to velocity commands and guides the robot to the end of a row, continuously adjusting its heading for a collision-free central trajectory. Our extensive experimentation in realistic simulated vineyards demonstrates the effectiveness of our solution and the generalization capabilities of our agent
Domain Generalization for Crop Segmentation with Knowledge Distillation
In recent years, precision agriculture has gradually oriented farming closer
to automation processes to support all the activities related to field
management. Service robotics plays a predominant role in this evolution by
deploying autonomous agents that can navigate fields while performing tasks
without human intervention, such as monitoring, spraying, and harvesting. To
execute these precise actions, mobile robots need a real-time perception system
that understands their surroundings and identifies their targets in the wild.
Generalizing to new crops and environmental conditions is critical for
practical applications, as labeled samples are rarely available. In this paper,
we investigate the problem of crop segmentation and propose a novel approach to
enhance domain generalization using knowledge distillation. In the proposed
framework, we transfer knowledge from an ensemble of models individually
trained on source domains to a student model that can adapt to unseen target
domains. To evaluate the proposed method, we present a synthetic multi-domain
dataset for crop segmentation containing plants of variegate shapes and
covering different terrain styles, weather conditions, and light scenarios for
more than 50,000 samples. We demonstrate significant improvements in
performance over state-of-the-art methods and superior sim-to-real
generalization. Our approach provides a promising solution for domain
generalization in crop segmentation and has the potential to enhance a wide
variety of precision agriculture applications
Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation
Single-Image Super-Resolution can support robotic tasks in environments where
a reliable visual stream is required to monitor the mission, handle
teleoperation or study relevant visual details. In this work, we propose an
efficient Generative Adversarial Network model for real-time Super-Resolution.
We adopt a tailored architecture of the original SRGAN and model quantization
to boost the execution on CPU and Edge TPU devices, achieving up to 200 fps
inference. We further optimize our model by distilling its knowledge to a
smaller version of the network and obtain remarkable improvements compared to
the standard training approach. Our experiments show that our fast and
lightweight model preserves considerably satisfying image quality compared to
heavier state-of-the-art models. Finally, we conduct experiments on image
transmission with bandwidth degradation to highlight the advantages of the
proposed system for mobile robotic applications
Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network
Modern robotic platforms need a reliable localization system to operate daily
beside humans. Simple pose estimation algorithms based on filtered wheel and
inertial odometry often fail in the presence of abrupt kinematic changes and
wheel slips. Moreover, despite the recent success of visual odometry, service
and assistive robotic tasks often present challenging environmental conditions
where visual-based solutions fail due to poor lighting or repetitive feature
patterns. In this work, we propose an innovative online learning approach for
wheel odometry correction, paving the way for a robust multi-source
localization system. An efficient attention-based neural network architecture
has been studied to combine precise performances with real-time inference. The
proposed solution shows remarkable results compared to a standard neural
network and filter-based odometry correction algorithms. Nonetheless, the
online learning paradigm avoids the time-consuming data collection procedure
and can be adopted on a generic robotic platform on-the-fly
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
Autonomous Navigation in Rows of Trees and High Crops with Deep Semantic Segmentation
Segmentation-based autonomous navigation has recently been proposed as a
promising methodology to guide robotic platforms through crop rows without
requiring precise GPS localization. However, existing methods are limited to
scenarios where the centre of the row can be identified thanks to the sharp
distinction between the plants and the sky. However, GPS signal obstruction
mainly occurs in the case of tall, dense vegetation, such as high tree rows and
orchards. In this work, we extend the segmentation-based robotic guidance to
those scenarios where canopies and branches occlude the sky and hinder the
usage of GPS and previous methods, increasing the overall robustness and
adaptability of the control algorithm. Extensive experimentation on several
realistic simulated tree fields and vineyards demonstrates the competitive
advantages of the proposed solution
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
Lavender Autonomous Navigation with Semantic Segmentation at the Edge
Achieving success in agricultural activities heavily relies on precise
navigation in row crop fields. Recently, segmentation-based navigation has
emerged as a reliable technique when GPS-based localization is unavailable or
higher accuracy is needed due to vegetation or unfavorable weather conditions.
It also comes in handy when plants are growing rapidly and require an online
adaptation of the navigation algorithm. This work applies a segmentation-based
visual agnostic navigation algorithm to lavender fields, considering both
simulation and real-world scenarios. The effectiveness of this approach is
validated through a wide set of experimental tests, which show the capability
of the proposed solution to generalize over different scenarios and provide
highly-reliable results
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