=Paper=
{{Paper
|id=Vol-2030/HAICTA_2017_paper19
|storemode=property
|title=Scheduling and Control of Unmanned Ground Vehicles for Precision Farming: A Real-time Navigation Tool
|pdfUrl=https://ceur-ws.org/Vol-2030/HAICTA_2017_paper19.pdf
|volume=Vol-2030
|authors=Dimitrios Bechtsis,Vasileios Moisiadis,Naoum Tsolakis,Dimitrios Vlachos,Dionysis Bochtis
|dblpUrl=https://dblp.org/rec/conf/haicta/BechtsisMTVB17
}}
==Scheduling and Control of Unmanned Ground Vehicles for Precision Farming: A Real-time Navigation Tool==
https://ceur-ws.org/Vol-2030/HAICTA_2017_paper19.pdf
Scheduling and Control of Unmanned Ground Vehicles
for Precision Farming: A Real-time Navigation Tool
Dimitrios Bechtsis1, Vasileios Moisiadis2, Naoum Tsolakis3, Dimitrios Vlachos4
Dionysis Bochtis2
1
Department of Automation Engineering, Alexander Technological Educational Institute of
Thessaloniki, Greece, e-mail: dimbec@autom.teithe.gr
2
Institute for Bio-economy and Agri-technology (IBO), Centre for Research & Technology-
Hellas (CERTH), 6th km Charilaou-Thermi Rd, GR 57001 Thermi, Thessaloniki, Greece
3
Centre for International Manufacturing, Institute for Manufacturing, Department of
Engineering, University of Cambridge, United Kingdom
4
Aristotle University of Thessaloniki, Department of Mechanical Engineering, Greece
Abstract. Autonomous systems are a promising alternative for effectively
executing agricultural field management strategies. Unmanned Ground
Vehicles perform farming activities on custom agricultural fields, using real-
time navigation. The aim of this study is to provide a software tool for
optimizing accuracy and efficiency in precision farming activities, hence
leading to improved farming output, while dynamically addressing operational
and tactical level uncertainties. This paper contributes to the operations
research field by allowing the application of simulated results direct to the
guidance of a physical vehicle. Unlike existing sophisticated tools, the
developed navigation mechanism is user-friendly and highly customizable at
outdoor navigation.
Keywords: Unmanned Ground Vehicles, Precision farming, Robot Operating
System, Real-time navigation.
1 Introduction
The paper focuses on the real-time scheduling and control of Unmanned Ground
Vehicles (UGVs) designed to perform precision farming activities on custom
agricultural fields, under the occurrence of any geomorphological and environmental
uncertainties. More specifically, the aim of this study is to provide a software tool for
the real-time navigation of UGVs in agricultural fields to optimize accuracy and
efficiency in precision farming activities, hence leading to improved farming output,
while dynamically addressing operational and tactical level uncertainties.
Research that motivates the integration of the UGV's ramifications onto the SC
ecosystem in general is not sufficient (Bechtsis et al., 2017) and this is also projected
to the agricultural sector. In traditional agriculture, farmers’ lack of accuracy and
lack of proper feedback leads to: (i) loss of situation awareness (Walker et al., 2008),
(ii) vigilance decrement (Finomore et al., 2009), (iii) complacency (Kaber and
Endsley, 2004), and (iv) skill degradation and human errors (Billings, 1996). In this
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regard, semi- or fully-autonomous vehicles that are controlled by computers (Ho et
al., 2012) are being used to address such difficulties and increase agricultural
operations efficiency (Zheng et al., 2016). More specifically, autonomous systems
are a promising alternative for effectively executing agricultural field management
strategies and agricultural operations considering that UGVs: (i) provide business
intelligence with real-time feedback on field’s parameters, (ii) can better handle
throughput volatility, (iii) can optimally operate autonomously on a 24/7 shift with
reliable performance, (iv) save energy compared to conventional man-driven farming
vehicles, (v) promote the smart agriculture vision, and (vi) increase safety at the field
level.
Despite the evident benefits of UGVs in agriculture, the application of
autonomous mechanization in the sector is challenged by inherent factors including
both field geometry and morphology particularities (Bochtis and Sørensen, 2009),
and environmental uncertainties like volatile weather conditions and encounters with
random objects and obstacles (Bochtis et al., 2014). This calls for automated systems
with scheduling and control intelligence capabilities that evaluate alternative
navigation decisions based on real-time feedback including field’s and crops’ states
assessed through real-time data (Wulfsohn et al., 2012). However, existing systems
have not been able to overcome problems such as low resolution maps, low
positioning accuracy, low grade process automation and incorrect or incomplete
measurements.
The aim of this paper is to present an engineering-driven system for the
scheduling and control of agricultural UGVs with the objective of executing
precision farming activities in an optimal manner, form an intra-logistics field
operations perspective. The research principle relies on the basic hypothesis that an
agricultural field presents obstacles (both random and static) that a UGV should
detect and recalculate an optimal pathway to perform its farming tasks. A key
theoretical contribution in the existing body of literature is the proposition of a UGV
software tool for the real-time field recognition that guides the vehicle to execute its
precision farming activities whilst allowing for a better usage of resources and
potentially a better harvest.
The remainder of the manuscript is organized as follows. In Section 2 we provide
the system description, along with the required input and operational data, and the
field monitoring process. In Section 3, we discuss the key findings of the simulation
tool in a conceptual field. Finally, in Section 4 we wrap-up with conclusions and
limitations, while we further outline beyond state-of-the-art applications of the
proposed system in the agricultural sector.
2 System Description
The proposed simulation system aims to navigate an UGV in custom agricultural
fields to perform related precision farming activities (e.g. seeding, spraying, and
fertilizing). The proposed system basically comprises of an information system that
consists of the following: (i) field layer representing the landscape and the static
objects (obstacles, trees), (ii) simulation layer which includes the action agents –
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representing the UGV whose task is to perform precision farming activities, (iii)
message layer – representing the continuous transactions between the system nodes
to produce and manage raw data, and (iv) application layer which handles all the
vehicles activities including the action scheduler and the data acquisition block –
determining, prioritizing and optimally scheduling the optimal navigation pathway of
the UGV in the field based on encounters with obstacles and any particular
geomorphological characteristics.
From a farm’s geometrical area perspective, the devised system regards the most
common area coverage practice which involves a set of parallel field-work tracks, or
trips, which starts at one boundary of the field and terminates at the opposite
boundary (Bochtis et al., 2012). In the exemplar demonstrator case under study, we
consider a set of five -equidistant- parallel tracks. An overview of the system
components as well as their inter-connections and related processes is presented in
Fig. 1.
ROS Tools Objects/Actions
Rvis
• Nodes
• Topics
rqt_graph
• Action Server/Publish
• Action Client/Subscribe
• Robot Joints
Rvis • Robot Links
Gazebo • Robot Sensors
• Robot Actuators
• Landscape
Gazebo • Static Objects
Fig. 1. Overview of the system components and related inter-connections.
2.1 Simulation Model Development
At the proposed research paper the Robot Operating System (ROS) is used to
simulate an autonomous vehicle’s farming activities. The model’s layout is created at
the Gazebo simulation tool; for optimized path tracking, the inherent ROS
Simultaneous Localization And Mapping (SLAM) procedure informs the UGV's
movement.
ROS is powerful and robust software with numerous third-party compatible tools
and relies on an extensive community of users and researchers that support it. A
significant number of ready to use outdoor robotic systems are compatible with ROS.
Indicatively, the Husky research robot developed by Clearpath
(https://www.clearpathrobotics.com/) and the Thorvald multipurpose agricultural
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vehicle by SAGA robotics (https://sagarobotics.com/) are two well-established
examples. Simulation, on the other hand, offers a test bed for developing algorithms
and testing different UGV properties on various fields (Farinelly et al., 2016). ROS
allows researchers to elaborate their ideas and safely simulate them at a lab level,
before testing them on the field.
2.2 Field and Operational Level Parameters
2.2.1 Field Representation
Initially, a 3-dimensional (3-D) coordinate system is assigned to the field. The 3D
world model is the simulation environment of the robot. Gazebo offers the tools for
building the facility layout of the robot's world and the user can add texture, color
and even objects. Fig. 2 depicts the experimental five-row crop cultivation field. The
model’s layout was created at the Gazebo simulation tool using the extensible
markup language and the Digital Elevation Model (DEM) for a 3-D representation of
the terrain. By convention Gazebo platform uses the right-handed coordinate system,
with X- and Y-axes in the plane, and Z-axis increasing with the altitude.
Fig. 2. The reference geomorphology of the experimental agricultural field.
2.2.2 Field-Specific Data
Field-specific data are associated with the direct field attributes, including:
• Field boundaries – The vehicle navigates in a pre-specific region in order to map
the field's layout. Field boundaries should be introduced either directly by passing
the absolute coordinates of the field's boundaries to the system or indirectly by
means of specific physical landscape morphology (hills with intense slope around
the field).
• Field coordinates – The field includes the crop lines and several physical
obstacles (rocks, trees and hills). The map creation process identifies the field's
layout and the vehicle can navigate to specific coordinates using a global
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optimum path. The start and end points of each field-work track can be passed as
arguments to the system.
• Potential obstacles – The vehicle identifies potential obstacles as a result of the
field area scanning procedure, after sending the original signal the vehicle
interprets the signals’ feedback of any spatially distributed obstacles to relative
map coordinates.
• Driving direction – This regards the direction of the parallel field-work tracks and
is obtained using the field's map.
2.2.3 Operation-Specific Data
Operation-specific data include the width between the field-work tracks and the
width of the action agent. The conceptual UGV is programmed to be equipped with a
light detection and ranging (LiDAR) sensor that is further developed, programmed
and tested at the 3-D agricultural field. The two-dimensional LiDAR sensor, with a
180-degree scanning angle, uses a laser meter which identifies distributed points in
the field that may represent trees or obstacles, and calculates the relative points’
distance thus informing the ROS about the corresponding X- and Y-axes coordinates.
In case a linear distribution of points is recognized, then the ROS recognizes a field-
work track of planted trees. As the UGV navigates in the field (Fig. 3), the scanning
angle changes resulting in a vast amount of the information generated via the LiDAR
sensor resulting in the full representation of the field and obstacles. A local pathway
is constantly updated as the UGV tries to contend with the global prescheduled path.
In order to maintain a safety distance from the identified trees and obstacles, the
global cost-map takes into account the UGV's size and the identified points are
inflated by the inscribed radius of the UGV. The global path creation is performed by
the ROS and an inter-row global optimal path is followed for the scheduled activities
at the working shift. The final mapping of the field’s layout is used by ROS to
calculate a special matrix for finding the optimized path from a single point to the
other (global cost-map) and create the global UGV’s navigation pathway in the field.
Fig. 3. Inflated layout with ROS global and local path generation.
The developed algorithm reads sensor’s input from the UGV to detect the
landscape in real-time and gradually creates the mapping of the overall field’s layout
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(Fig. 4). The created map is periodically saved as a snapshot of the working process
in the file system structure. Each file is continuously compared using image
processing techniques with previews file sequences in order to determine the end of
mapping procedure. The optimized map is identified and used as an input for the
creation of the global cost map and the robots movement. More specifically, Fig. 4
provides the starting image of the mapping procedure where the UGV first activates
the LiDAR sensor to transmit signals into the field’s layout until the entire
agricultural field is mapped. At the starting point, the LiDAR signals are represented
by gray that are used to identify the uncharted area, gradually forming the visible
areas. The linearly distributed black points indicate the field tracks and the obstacles’
contour. The boundary of the scanned area is produced from the 3-D terrain altitude
differences.
Fig. 4. Inflated layout with ROS global and local path generation.
3 Results
The simulation results demonstrate that with the proposed software system
farming tasks can be scheduled step-by-step, with extreme accuracy, as the vehicle
passes through the trees’ field tracks. For crop line planting, irrigation and weeding
the X-, Y-, Z-axes coordinates of the landscape are pre-programmed and are
imported to the UGV’s schedule. Fig. 5 indicates the real-time SLAM of the UGV at
the field level as the LiDAR sensor constantly scans for possible real-time collisions.
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Fig. 5. UGV’s navigation with real-time SLAM.
4 Conclusions
The paper contributes to sustainable precision farming operations by providing a
software tool for the real-time scheduling and control of UGV vehicles’ navigation in
custom agricultural fields under possible uncertainties. Sustainability parameters are
addressed at economic (optimize performance and increase production),
environmental (optimize energy consumption on UGVs and equipment at operational
level), and social (increase safety levels at field level, improve ergonomics for human
workers) levels. Overall, this research makes contextual/business environmental
elements of operational management into more dominant elements of an operational
system. More specifically, the low computational time requirements of the
underlying process allow for the implementation of the proposed system as a real-
time tool in agricultural operations. The developed system can be extended to capture
multi-criteria optimization aspects to promote agricultural supply chain sustainability
from a cradle-to-grave perspective.
Finally, the system is prone to be tested in real field conditions with the use of a
custom or a commercial UGV robot. In order to make the transfer to the real world
the vehicle should be equipped with the proper sensors (lidar sensors, depth cameras,
inertial sensors etc) and could either be a simple ROS node that exchanges
information with the ROS core platform or a complete ROS enabled platform (with
an embedded pc) that performs all the necessary computations.
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