96 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
Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
[No abstract available
Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives
Phenotyping plants is an essential component of any effort to develop new crop varieties. As plant breeders seek to increase crop productivity and produce more food for the future, the amount of phenotype information they require will also increase. Traditional plant phenotyping relying on manual measurement is laborious, time-consuming, error-prone, and costly. Plant phenotyping robots have emerged as a high-throughput technology to measure morphological, chemical and physiological properties of large number of plants. Several robotic systems have been developed to fulfill different phenotyping missions. In particular, robotic phenotyping has the potential to enable efficient monitoring of changes in plant traits over time in both controlled environments and in the field. The operation of these robots can be challenging as a result of the dynamic nature of plants and the agricultural environments. Here we discuss developments in phenotyping robots, and the challenges which have been overcome and others which remain outstanding. In addition, some perspective applications of the phenotyping robots are also presented. We optimistically anticipate that autonomous and robotic systems will make great leaps forward in the next 10 years to advance the plant phenotyping research into a new era
Robotic 3D Plant Perception and Leaf Probing with Collision-Free Motion Planning for Automated Indoor Plant Phenotyping
Various instrumentation devices for plant physiology study such as chlorophyll fluorimeter and Raman spectrometer require leaf probing with accurate probe positioning and orientation with respect to leaf surface. In this work, we aimed to automate this process with a Kinect V2 sensor, a high-precision 2D laser profilometer, and a 6-axis robotic manipulator in a high-throughput manner. The relatively wide field of view and high resolution of Kinect V2 allowed rapid capture of the full 3D environment in front of the robot. Given the number of plants, the location and size of each plant were estimated by K-means clustering. A real-time collision-free motion planning framework based on Probabilistic Roadmap was adopted to maneuver the robotic manipulator without colliding with the plants. Each plant was scanned from top with the short-range profilometer to obtain a high-precision point cloud where potential leaf clusters were extracted by region growing segmentation. Each leaf segment was further partitioned into small patches by Voxel Cloud Connectivity Segmentation. Only the small patches with low root mean square values of plane fitting were used to compute probing poses. To evaluate probing accuracy, a square surface was scanned at various angles and its centroid was probed perpendicularly with a probing position error of 1.5 mm and a probing angle error of 0.84 degrees on average. Our growth chamber leaf probing experiment showed that the average motion planning time was 0.4 seconds and the average traveled distance of tool center point was 1 meter
Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution
The application of autonomous robots in agriculture is gaining increasing
popularity thanks to the high impact it may have on food security,
sustainability, resource use efficiency, reduction of chemical treatments, and
the optimization of human effort and yield. With this vision, the Flourish
research project aimed to develop an adaptable robotic solution for precision
farming that combines the aerial survey capabilities of small autonomous
unmanned aerial vehicles (UAVs) with targeted intervention performed by
multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview
of the scientific and technological advances and outcomes obtained in the
project. We introduce multi-spectral perception algorithms and aerial and
ground-based systems developed for monitoring crop density, weed pressure, crop
nitrogen nutrition status, and to accurately classify and locate weeds. We then
introduce the navigation and mapping systems tailored to our robots in the
agricultural environment, as well as the modules for collaborative mapping. We
finally present the ground intervention hardware, software solutions, and
interfaces we implemented and tested in different field conditions and with
different crops. We describe a real use case in which a UAV collaborates with a
UGV to monitor the field and to perform selective spraying without human
intervention.Comment: Published in IEEE Robotics & Automation Magazine, vol. 28, no. 3, pp.
29-49, Sept. 202
Viewpoint Planning based on Shape Completion for Fruit Mapping and Reconstruction
Robotic systems in agriculture do not only enable increasing automation of
farming activities but also represent new challenges for robotics due to the
unstructured environment and the non-rigid structures of crops. Especially,
active perception for fruit mapping and harvesting is a difficult task since
occlusions frequently occur and image segmentation provides only limited
accuracy on the actual shape of the fruits. In this paper, we present a
viewpoint planning approach that explictly uses the shape prediction from
collected data to guide the sensor to view as yet unobserved parts of the
fruits. We developed a novel pipeline for continuous interaction between
prediction and observation to maximize the information gain about sweet pepper
fruits. We adapted two different shape prediction approaches, namely parametric
superellipsoid fitting and model based non-rigid latent space registration, and
integrated them into our Region of Interest (RoI) viewpoint planner.
Additionally, we used a new concept of viewpoint dissimilarity to aid the
planner to select good viewpoints and for shortening the planning times. Our
simulation experiments with a UR5e arm equipped with a Realsense L515 sensor
provide a quantitative demonstration of the efficacy of our iterative shape
completion based viewpoint planning. In comparative experiments with a
state-of-the-art viewpoint planner, we demonstrate improvement not only in the
estimation of the fruit sizes, but also in their reconstruction. Finally, we
show the viability of our approach for mapping sweet peppers with a real
robotic system in a commercial glasshouse.Comment: Agricultural Automation, Viewpoint Planning, Active Perceptio
MAP-NBV: Multi-agent Prediction-guided Next-Best-View Planning for Active 3D Object Reconstruction
We propose MAP-NBV, a prediction-guided active algorithm for 3D
reconstruction with multi-agent systems. Prediction-based approaches have shown
great improvement in active perception tasks by learning the cues about
structures in the environment from data. But these methods primarily focus on
single-agent systems. We design a next-best-view approach that utilizes
geometric measures over the predictions and jointly optimizes the information
gain and control effort for efficient collaborative 3D reconstruction of the
object. Our method achieves 22.75% improvement over the prediction-based
single-agent approach and 15.63% improvement over the non-predictive
multi-agent approach. We make our code publicly available through our project
website: http://raaslab.org/projects/MAPNBV/Comment: 7 pages, 7 figures, 2 tables. Submitted to MRS 202
A Hybrid Cable-Driven Robot for Non-Destructive Leafy Plant Monitoring and Mass Estimation using Structure from Motion
We propose a novel hybrid cable-based robot with manipulator and camera for
high-accuracy, medium-throughput plant monitoring in a vertical hydroponic farm
and, as an example application, demonstrate non-destructive plant mass
estimation. Plant monitoring with high temporal and spatial resolution is
important to both farmers and researchers to detect anomalies and develop
predictive models for plant growth. The availability of high-quality,
off-the-shelf structure-from-motion (SfM) and photogrammetry packages has
enabled a vibrant community of roboticists to apply computer vision for
non-destructive plant monitoring. While existing approaches tend to focus on
either high-throughput (e.g. satellite, unmanned aerial vehicle (UAV),
vehicle-mounted, conveyor-belt imagery) or high-accuracy/robustness to
occlusions (e.g. turn-table scanner or robot arm), we propose a middle-ground
that achieves high accuracy with a medium-throughput, highly automated robot.
Our design pairs the workspace scalability of a cable-driven parallel robot
(CDPR) with the dexterity of a 4 degree-of-freedom (DoF) robot arm to
autonomously image many plants from a variety of viewpoints. We describe our
robot design and demonstrate it experimentally by collecting daily photographs
of 54 plants from 64 viewpoints each. We show that our approach can produce
scientifically useful measurements, operate fully autonomously after initial
calibration, and produce better reconstructions and plant property estimates
than those of over-canopy methods (e.g. UAV). As example applications, we show
that our system can successfully estimate plant mass with a Mean Absolute Error
(MAE) of 0.586g and, when used to perform hypothesis testing on the
relationship between mass and age, produces p-values comparable to ground-truth
data (p=0.0020 and p=0.0016, respectively).Comment: 8 pages (6-content, 2-citations), 10 figures, 4 tables, submitted to
ICRA 202
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