Unmanned Ground Vehicles for Smart Farms

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
Chapter
Unmanned Ground Vehicles for
Smart Farms
PabloGonzalez-De-Santos, RoemiFernández,
DeliaSepúlveda, EduardoNavas and ManuelArmada
Abstract
Forecasts of world population increases in the coming decades demand new
production processes that are more efficient, safer, and less destructive to the
environment. Industries are working to fulfill this mission by developing the
smart factory concept. The agriculture world should follow industry leadership
and develop approaches to implement the smart farm concept. One of the most
vital elements that must be configured to meet the requirements of the new smart
farms is the unmanned ground vehicles (UGV). Thus, this chapter focuses on the
characteristics that the UGVs must have to function efficiently in this type of future
farm. Two main approaches are discussed: automating conventional vehicles and
developing specifically designed mobile platforms. The latter includes both wheeled
and wheel-legged robots and an analysis of their adaptability to terrain and crops.
Keywords: smart farm, precision agriculture, agricultural robot,
unmanned ground vehicle, autonomous robot
. Introduction
The world’s human population increases by approximately 240,000 people every
day: it is expected to reach 8 billion by 2025 and approximately 9.6 billion by 2050.
Cultivated land is at a near-maximum, yet estimates predict that food production
must be increased by 70% for worldwide peace to persist circa 2050 [1]. Thus,
producing sufficient food to meet the ever-growing demand for this rising popula-
tion is an exceptional challenge to humanity. To succeed at this vital objective, we
must build more efficient—yet sustainable—food production devices, farms, and
infrastructures. To accomplish that objective, the precision farming concept—a set
of methods and techniques to accurately manage variations in the field to increase
crop productivity, business profitability, and ecosystem sustainability—has pro-
vided some remarkable solutions.
Figure  summarizes the cycle of precision agriculture and distinguishes the
activities based on analysis and planning (right) from those that rely on providing
motion (left). The solutions for activities illustrated in Figure  right are being based
on information and communication technologies (ICT), whereas the activities on
the left rely on tractors, essential devices in current agriculture, that are being auto-
mated and robotized and will be also critical in future agriculture (smart farms).
The activities indicated in Figure  left can be applied autonomously in an iso-
lated manner, i.e., a fertilization-spreading task, can be performed autonomously

Agronomy

if the appropriate implement tank has been filled with fertilizer and attached to a
fueled autonomous tractor (UGV); the same concept is applicable to planting and
spraying. In addition, harvesting systems must offload the yield every time their
collectors are full. However, tasks such as refilling, refueling/recharging, implement
attachment, and crop offloading are currently primarily performed manually. The
question that arises is: would it be possible to automate all these activities? And if
so, would it be possible to combine these activities with other already automated
farm management activities to configure a fully automated system resembling the
paradigm of the fully automated factory? Then, the combination becomes a fully
automated farm in which humans are relegated to mere supervisors. Furthermore,
exploiting this parallelism, can we push new developments for farms to mimic the
smart factory model? This is the smart farm concept that represents a step forward
from the automated farm into a fully connected and flexible system capable of (i)
optimizing system performances across a wider network, (ii) learning from new
conditions in real- or quasi-real time, (iii) adapting the system to new conditions,
and (iv) executing complete production processes in an autonomous way [2]. A
smart farm should rely on autonomous decision-making to (i) ensure asset effi-
ciency, (ii) obtain better product quality, (iii) reduce costs, (iv) improve product
safety and environmental sustainability, (v) reduce delivery time to consumers, and
(vi) increase market share and profitability and stabilize the labor force.
Achieving the smart farm is a long-term mission that will demand design
modifications and further improvements on systems and components of very dis-
similar natures that are currently being used in agriculture. Some of these systems
are outdoor autonomous vehicles or (more accurately) UGVs, which are essential
in future agriculture for moving sensors and implementing to cover crop fields
accurately and guarantee accurate perception and actuation (soil preparation,
crop treatments, harvest, etc.). Thus, this chapter is devoted to bringing forward
the features that UGVs should offer to achieve the smart farm concept. Solutions
are focused on incorporating the new paradigms defined for smart factories while
providing full mobility of the UGVs. These two activities will enable the definition
of UGV requirements for smart farm applications.
To this end, the next section addresses the needs of UGVs in smart farms. Then,
two main approaches to configure solutions for UGVs in agricultural tasks are
described: the automation of conventional vehicles and specifically designed mobile
Figure 1.
UGVs in the cycle of precision agriculture.

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platforms. Their advantages and shortcomings regarding their working features are
highlighted. This material enables the definition of other operating characteristics
of UGVs to meet the smart farm requirements. Finally, the last section presents
some conclusions.
. UGV for agriculture
Ground mobile robots, equipped with advanced technologies for positioning
and orientation, navigation, planning, and sensing, have already demonstrated
their advantages in outdoor applications in industries such as mining [3], farming,
and forestry [4, 5]. The commercial availability of GNSS has provided easy ways to
configure autonomous vehicles or navigation systems to assist drivers in outdoor
environments, especially in agriculture, where many highly accurate vehicle steer-
ing systems have become available [6, 7]. These systems aid operators in the precise
guidance of tractors using LIDAR (light/laser detection and ranging) or GNSS tech-
nology but do not endow a vehicle or tool with any level of autonomy. Nevertheless,
other critical technologies must also be incorporated to configure UGVs, such as the
safety systems responsible for detecting obstacles in the robots’ path and safeguard-
ing humans and animals in the robots’ surroundings as well as preventing collisions
with obstacles or other robots. Finally, robot communications with operators and
external servers (cloud technologies) through wireless communications that include
the use of cyber-physical systems (CPSs) [8] and Internet of things (IoT) [9]
techniques will be essential to incorporate decision-making systems based on big
data analysis. Such integration will enable the expansion of decision processes into
fields such as machine learning and artificial intelligence. Smart factories are based
on the strongly intertwined concepts of CPS, IoT, big data, and cloud computing,
and UGVs for smart farms should be based on the same principles to minimize the
traditional delays in applying the same technologies to industry and agriculture.
The technology required to deploy more robotic systems into agriculture is avail-
able today, as are the clear economic and environmental benefits of doing so. For
example, the global market for mobile robots, in which agricultural robots are a part, is
expected to increase at a compound annual growth rate of over 15% from 2017 to 2025,
according to recent forecast reports [10]. Nevertheless, manufacturers of agricultural
machinery seem to be reluctant to commercialize fully robotic systems, although they
have not missed the marketing potential of showing concepts [11, 12]. In any event,
according to the Standing Committee on Agricultural Research [13], further efforts
should be made by both researchers and private companies to invent new solutions.
Most of the robotics and automation systems that will be used in precision
agriculture—including systems for fertilizing, planting, spraying, scouting, and
harvesting (Figure )—will require the coordination of detection devices, agri-
cultural implements, farm managing systems, and UGVs. Thus, several research
groups and companies have been working on such systems. Specifically, two trends
can be identified in the development of UGVs: the automation of conventional
agricultural vehicles (tractors) and the development of specifically designed mobile
platforms. The following sections discuss these two types of vehicles.
. Automation of conventional vehicles
The tractor has been the central vehicle for executing most of the work required
in a crop field. Equipped with the proper accessories, this machine can till, plant,
fertilize, spray, haul, mow, and even harvest. Their adaptability to dissimilar tasks

Agronomy

makes tractors a prime target for automation, which would enable productivity
increases, improve safety, and reduce operational costs. Figure  shows an example
of the technologies and equipment for automating agricultural tractors.
Numerous worldwide approaches to automating diverse types of tractors have
been researched and developed since 1995 when the first GNSS was made available
to the international civilian community of users, which opened the door for GPS-
guided agricultural vehicles (auto-steering) and controlled-traffic farming.
The first evaluations of GPS systems for vehicle guidance in agriculture were
also published in 1995 [14] demonstrating its potential and encouraging many
research groups around the world to automate diverse types of tractors. The earliest
attempts were made at Stanford University in 1996, where an automatic control
system for an agricultural tractor was developed and tested on a large farm [15].
The system used a location system with four GPS antennas. Around the same time,
researchers at the University of Illinois, USA, developed a guidance system for an
autonomous tractor based on sensor fusion that included machine vision, real-time
kinematics GPS (RTK-GPS), and a geometric direction sensor (GDS). The fusion
integration methodology was based on an extended Kalman filter (EKF) and a two-
dimensional probability-density-function statistical method. This system achieved
a lateral average error of approximately 0.084m at approximately 2.3ms−1 [16].
A few years later, researchers at Carnegie Mellon University, USA, developed
some projects that made significant contributions. The Demeter project was
conceived as a next-generation self-propelled hay harvester for agricultural opera-
tions, and it became the most representative example of such activity [17]. The
positional data was fused from a differential GPS, a wheel encoder (dead reckon-
ing), and gyroscopic system sensors. The project resulted in a system that allowed
an expert harvesting operator to harvest a field once, thus programming the field.
Subsequently, an operator with lesser skill could “playback” the programmed field
at a later date. The semi-autonomous agricultural spraying project, developed by
the same research group, was devoted to making pesticide spraying significantly
cheaper, safer, and more environmentally friendly [18]. This system enabled a
remote operator to oversee the nighttime operation of up to four spraying vehicles.
Another example is research conducted at the University of Florida, USA, [19], in
Figure 2.
An example of agricultural tractor automation-distribution of sensorial and actuation systems for
transforming an agricultural tractor into a UGV (Gonzalez-de-Santos etal., 2017).

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which two individual autonomous guidance systems for use in a citrus grove were
developed and tested along curved paths at a speed of approximately 3.1ms−1. One
system, based on machine vision, achieved an average guidance error of approxi-
mately 0.028m. The other system, based on LIDAR guidance, achieved an average
error of approximately 0.025m.
Similar activities started in Europe in the 2000s. One example is the work
performed at LASMEA-CEMAGREF, France, in 2001, which evaluated the pos-
sibilities of achieving recording-path tracking using a carrier phase differential
GPS (CP-DGPS), as the only sensor. The vehicle heading was derived according to
a Kalman state reconstructor and a nonlinear velocity independent control law was
designed that relied on chained systems properties [20].
A relevant example of integrating UGVs with automated tools is the work con-
ducted at the University of Aarhus and the University of Copenhagen, Denmark [21].
The system comprised an autonomous ground vehicle and a side shifting arrangement
affixed to a weeding implement. Both the vehicle and the implement were equipped
with RTK-GPS; thus, the two subsystems provided their own positions, allowing the
vehicle to follow predefined GPS paths and enabling the implement to act on each
individual plant, whose positions were automatically obtained during seeding.
Lately, some similar automations of agricultural tractors have been conducted
using more modern equipment [22, 23], and some tractor manufacturers have
already presented noncommercial autonomous tractors [11, 12]. This tendency to
automate existing tractors has been applied to other types of lightweight vehicles
for specific tasks in orchards such as tree pruning and training, blossom and fruit
thinning, fruit harvesting, mowing, spraying, and sensing [24]. Table  summarizes
the UGVs based on commercial vehicles for agricultural tasks.
Institution Year Description
Stanford University (USA) [15]1996 Automatic large-farm tractor using 4 GPS antennas
University of Illinois (USA) [16]1998 A guidance system using a sensor based on machine
vision, an RTK-GPS, and a GDS
Carnegie Mellon University
(USA)—Demeter project [17]
1999 A self-propelled hay harvester for agricultural
operations
Carnegie Mellon University
(USA)—Autonomous Agricultural
Spraying project [18]
2002 A ground-based vehicles for pesticide spraying
LASMEA-CEMAGREF
(France) [20]
2001 This study investigated the possibility of achieving
vehicle guiding using a CP-DGPS as the only sensor
University of Florida (USA) [19]2006 An autonomous guidance system for citrus groves
based on machine vision and LADAR
University of Aarhus and the
University of Copenhagen
(Denmark) [21]
2008 An automatic intra-row weed control system
connected to an unmanned tractor
RHEA consortium (EU) [22]2014 A fleet (3units) of tractors that cooperated and
collaborated in physical/chemical weed control and
pesticide applications for trees
Carnegie Mellon University
(USA) [24]
2015 Self-driving orchard vehicles for orchard tasks
University of Leuven (Belgium) [23]2015 Tractor guidance using model predictive control for
yaw dynamics
Table 1.
UGVs based on commercial vehicles.

Agronomy

Nevertheless, UGVs suitable for agriculture remain far from commercialization,
although many intermediate results have been incorporated into agricultural equip-
ment—from harvesting to precise herbicide application. Essentially, these systems
are installed on tractors owned by farmers and generally consist of a computer (the
controller), a device for steering control, a localization system (mostly based on
RTK-GPS), and a safety system (mostly based on LIDAR). Many of these systems
are compatible only with advanced tractors that feature ISOBUS control technology
[25], through which controllers connected to the ISOBUS can access other subsys-
tems of the tractor (throttle, brakes, auxiliary valves, power takeoff, linkage, lights,
etc.). Examples of these commercial systems are AutoDrive [26] and X-PERT [27].
An important shortcoming of these solutions is their lack of intelligence in solv-
ing problems, especially when obstacles are detected because they are not equipped
with technology suitable for characterizing and identifying the obstacle type. This
information is essential when defining any behavior other than simply stopping
and waiting for the situation to be resolved. Another limitation of this approach is
that the conventional configuration of a standard tractor driven by an operator is
designed to maximize the productivity per hour; thus, the general architecture of
the system (tractor plus equipment) is only roughly optimized.
. Specifically designed mobile platforms
The second approach to the configuration of mobile robots for agriculture is the
development of autonomous ground vehicles with specific morphologies, where
researchers develop ground mobile platforms inspired more by robotic principles
than by tractor technologies. These platforms can be classified based on their loco-
motion system. Ground robots can be based on wheels, tracks, or legs. Although
legged robots have high ground adaptability (that enables the vehicles to work on
irregular and sloped terrain) and intrinsic omnidirectionality (which minimizes
the headlands and, thus, maximizes croplands) and offer soil protection (discrete
points in contact with the ground that minimize ground damage and ground
compaction, an important issue in agriculture), they are uncommon in agriculture;
however, legged robots provide extraordinary features when combined with wheels
that can configure a disruptive locomotion system for smart farms. Such a structure
(which consists of legs with wheels as feet) is known as a wheel-legged robot. The
following sections present the characteristics, advantages, and disadvantages of
these specifically designed types of robots.
. Wheeled mobile robots
.. Structures of wheeled robots
The structure of a wheeled mobile platform depends on the following features:
Number of wheels: Three nonaligned wheels are the minimum to ensure platform
static stability. However, most field robots are based on four wheels, an approach
that increases the static and dynamic stability margins [28].
Wheel orientation type: An ordinary wheel can be installed on a platform in
different ways that strongly determine the platform characteristics. Several wheel
types can be considered:
a. Fixed wheel: This wheel is connected to the platform in such a way that the
plane of the wheel is perpendicular to the platform and its angle (orientation)
cannot change.

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Unmanned Ground Vehicles for Smart Farms
DOI: hp://dx.doi.org/10.5772/intechopen.90683
b. Orienting wheel: The wheel plane can change its orientation angle using an
orientation actuator.
c. Castor wheel: The wheel can rotate freely around an offset steering joint. Thus,
its orientation can change freely.
Wheel power type: Depending on whether wheels are powered, they can also be
classified as follows:
a. Passive wheel: The wheel rotates freely around its shaft and does not provide
power.
b. Active wheel: An actuator rotates the wheel to provide power.
Wheel arrangement: Different combinations of wheel types produce mobile
platforms with substantially different steering schemes and characteristics.
a. Coordinated steering scheme: Two fixed active wheels at the rear of the platform
coupled with two passive orienting wheels at the front of the platform are the
most common wheel arrangement for vehicles. To maintain all wheels in a pure
rolling condition during a turn, the wheels need to follow curved paths with
different radii originating from a common center [29]. A special steering mecha-
nism, the Ackermann steering system, which consists of a 4-bar trapezoidal
mechanism (Figure a), can mechanically manage the angles of the two steering
wheels. This system is used in all the vehicles presented in Table  . It features
medium mechanical complexity and medium control complexity. One advantage
of this system is that a single actuator can steer both wheels. However, independ-
ent steering requires at least three actuators for steering and power (Figure b).
b. Skid steering scheme: Perhaps the simplest structure for a mobile robot consists
of four fixed, active wheels, one on each corner of the mobile platform. Skid
steering is accomplished by producing a differential thrust between the left
Figure 3.
Steering driving systems: (a) Ackermann steering system; (b) independent steering; (c) skid steering system and
(d) independent steering and traction system.

Agronomy

and right sides of the vehicle, causing a heading change (Figure c). The two
wheels on one side can be powered independently or by a single actuator. Thus,
the motion of the wheels in the same direction produces backward/forward
platform motion; and the motion of the wheels on one side in the opposite
direction to the motion of wheels on the other side produces platform rotation.
c. Independent steering scheme: An independent steering scheme controls
each wheel, moving it to the desired orientation angle and rotation speed
(Figure d). This steering scheme makes wheel coordination and wheel
Steering
scheme
Characteristics
Coordinated Advantages:
• Simplicity.
• Few actuators (2) if based on the Ackermann device.
• Good turning accuracy if the front wheels are steered independently.
Disadvantages:
• Large turning radii.
• Ideal rotation in only three steering angles if based on the Ackermann device.
• Requires three actuators and more complex control algorithms if based on front
wheels steered independently.
• Steering control on loose grounds, e.g., after plowing, is difficult.
Use in smart farms:
• New mobile robotic designs are abandoning this scheme, which only offers simplicity.
Hence, such steering control is not expected to be used in smart farms.
Skid Advantages:
• Compact size, robustness, few parts.
• Agility (motion with heading control and zero-radius turns).
• Few actuators (2).
Disadvantages:
• The maximum forward thrust is not maintained during turns.
• Terrain irregularities and tire-soil effects demand unpredictable power supply.
• Vehicle rotations erode the ground and wore the tires.
Use in smart farms:
• This steering scheme is simple and robust, but not very precise in loose terrain;
hence, it could be used in smart farms, e.g., for indoor tasks, but not for infield tasks.
Independent Advantages:
• Full mobility (including crab motion).
Disadvantages:
• Many actuators and parts (eight for a four-wheel robot).
• Complex control algorithms.
Use in smart farms:
• This steering scheme is the more versatile of the schemes, but it is also more complex
and expensive. However, most of the engineering systems evolve by increasing their
sophistication and robustness while decreasing their cost; hence, this scheme will be
intensively used in smart farms.
Table 2.
Characteristics of wheeled structures.

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DOI: hp://dx.doi.org/10.5772/intechopen.90683
position accuracy more complex but provides some advantages in maneuver-
ability. In addition, this scheme provides crab steering (sideways motion at
any angle α; 0≤α≤2π) by aligning all wheels at an angle α with respect to the
longitudinal axis of the mobile platform. Finally, the coordination of driving
and steering results in more efficient maneuverability and reduces internal
power losses caused by actuator fighting. The independent steering scheme
requires eight actuators for a four-wheel vehicle.
Table  summarizes the advantages and drawbacks of these schemes. Note that
the number of actuators increases the total mass of a robot as well as its mechanical
and control complexity (more motors, more drivers, more elaborate coordinating
algorithms, etc.).
.. Examples of wheeled robots
Some examples of wheeled mobile platforms for agriculture are the conventional
tractor using the Ackermann steering system (Figure ) with two front passive and
steerable wheels and two rear fixed and active wheels.
Skid steering platforms can be found in many versions. For example,
• Four fixed wheels placed in pairs on both sides of the robot
• Two fixed tracks, each one placed longitudinally at each side of the robot,
• Two fixed wheels placed at the front of the robot and two castor wheels placed
at the rear (Figure c), etc.
Regarding the independent steering scheme, the robot developed by Bak and
Jakobsen [30] is one of the first representative examples (Figure a). This platform
was designed specifically for agricultural tasks in wide-row crops and featured good
ground clearance (approximately 0.5m) and 1-m wheel separation. The platform is
based on four-identical wheel modules. Each one includes a brushless electric motor
that provides direct-drive power, and steering is achieved by a separate motor.
An example of a mobile platform under development that focuses on performing
precision agricultural tasks is AgBot II (Figure c). This is a platform that follows
the skid steering scheme with two front fixed wheels (working in skid or differen-
tial mode) and two rear caster wheels. It is intended to work autonomously on both
large-scale and horticultural crops, applying fertilizer, detecting and classifying
weeds, and killing weeds either mechanically or chemically [31, 32]). Another robot
is Robot for Intelligent Perception and Precision Application (RIPPA), which is a
light, rugged, and easy-to-operate prototype for the vegetable growing industry.
It is used for autonomous high-speed, spot spraying of weeds using a directed
micro-dose of liquid when equipped with a variable injection intelligent precision
applicator [33]. Another example is Ladybird (Figure b), an omnidirectional
robot powered with batteries and solar panels that follows the independent steer-
ing scheme. The robot includes many sensors (i.e., hyperspectral cameras, thermal
and infrared detecting systems, panoramic and stereovision cameras, LIDAR, and
GPS) that enable assessing crop properties [34]. One more prototype, very close
to commercialization, is Kongskilde Vibro Crop Robotti, which is a self-contained
track-based platform that uses the skid steering scheme. It can be equipped with
implements for precision seeding and mechanical row crop cleaning units. This
robot can work for 2–4hours at a 2–5kmh−1 rate and is supplied by captured
electric energy [35].

Agronomy

Figure 4.
Pictures of several specifically-designed agricultural platforms. (a) Robot for weed detection, courtesy of
T.Bak, Department of Agricultural Engineering, Danish Institute of Agricultural Sciences; (b) ladybird,
courtesy of J.P. Underwood, Australian Centre for Field Robotics at the University of Sydney [34]; (c) AgBot
II, courtesy of O.Bawden, strategic Investment in Farm Robotics, Queensland University of Technology [31].
These robots are targeted toward fertilizing, seeding, weed control, and gathering
information, and they have similar characteristics in terms of weight, load capacity,
operational speed, and morphology. Tools, instrumentation equipment, and agricul-
tural implements are connected under the robot, and tasks are performed in the area
just below the robot, which optimizes implement weight distribution. These robots
have limitations for use on farmland with substantial (medium to high) slopes or gully
erosion. Nevertheless, some mobile platforms are already commercially available. Two
examples of these vehicles are the fruit robots Cäsar [36] and Greenbot [37].
Cäsar is a remote-controlled special-purpose vehicle that can perform temporar-
ily autonomous operations in orchards and vineyards such as pest management,
soil management, fertilization, harvesting, and transport. Similarly, Greenbot is a
self-driving machine specially developed for professionals in the agricultural and
horticultural sectors who perform regular, repetitious tasks. This vehicle can be
used not only for fruit farming, horticulture, and arable farming but also in the
urban sector and even at waterfronts or on roadsides.
Despite their current features, the existing robots lack flexibility and terrain adapt-
ability to cope with diverse scenarios, and their safety features are limited. For example:

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Unmanned Ground Vehicles for Smart Farms
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• They focus only on orchard and vineyard activities.
• They have ground clearance limitations.
• They are unsuitable for rough terrain or slopes.
• They must be manually guided to the working area rather than freely and
autonomously moving to different working areas around the farm.
• They possess no advanced detection systems for weed or soil identifica-
tion, which limits their use to previously planned tasks related to selective
treatment.
• They lack dynamic safety systems capable of recognizing or interpreting
safety issues; thus, they are incapable of rescheduling or solving problems by
themselves.
In addition, existing UGVs for agriculture lack communication mechanisms for
providing services through cloud technologies, CPS, and IoT techniques, crucial
instruments to integrate decision-making systems based on big data analysis, as is
being done in the smart factory concept.
Table  summarizes the diverse robotic platforms, and Figure  depicts some of
these platforms.
. Wheel-legged robots
.. Structures of wheel-legged robots
The structure of a wheel-legged mobile platform depends on (i) the number
of legs, (ii) the leg type, and (iii) the leg arrangement. The feet consist of 2-DOF
steerable powered wheels as illustrated in Figure .
Number of legs: The minimum number of legs required for statically stable
walking is four-three legs providing support in the form of a stable tripod while
the other leg performs the transference phase [38]. Combining sequences of leg
Ve h i c l e Ty pe* Year Description
AgBot II [32] P2014 A platform that follows the skid steering scheme with two front
fixed wheels (working in skid or differential mode) and two
rear caster wheels
Ladybird [34] P 2015 An omnidirectional robot powered with batteries and solar
panels that uses the independent steering scheme
Greenbot [37] C 2015 A self-driving robot for tasks in agriculture and horticulture
Cäsar [36] P 2016 A remotely controlled platform for temporary, autonomous use
in fruit plantations and vineyards
RIPPA [33] P 2016 A light, rugged, and easy-to-operate prototype for the vegetable
growing industry
Vibro Crop
Robotti [35]
C2017 A self-contained track-based platform that uses the skid steering
scheme
*) P-prototype; C-commercial.
Table 3.
Robots designed specifically for agriculture.

Agronomy

transferences with stable tripods produce a walking motion. A wheel-legged robot
requires only three legs for translational motion, which provides additional terrain
adaptation.
Leg type: Legs are based on the typical configurations of manipulators; thus,
articulated, cylindrical, Cartesian, and pantographic configurations are the types
used most often.
Leg arrangement: The normal arrangement for a 2n-legged robot is to distribute
n legs uniformly on the longitudinal sides. Four-legged structures present some
advantages regarding terrain adaptability, ground clearance, and track width
control (crop adaptability) but also have some drawbacks, such as additional
mechanical complexity (complex joints designs, including actuators and brakes)
and control of redundant actuated systems, which exhibit complex interactions
with the environment and make motion control more difficult than that of con-
ventional wheeled platforms. Table  illustrates different theoretical wheel-legged
structures.
.. Examples of wheel-legged robots
Figure a illustrates the structure scheme of a wheel-legged robot based on the
3-DOF SCARA leg (See Figure b) with full terrain adaptability, ground clear-
ance control, crop adaptability, and capability of walking, and Figure b shows
the structure of a wheel-legged robot exhibiting full terrain adaptability, ground
clearance control, and crop adaptability; however, it cannot walk under static
stability.
Figure 5.
Wheel-legged structures. (a) 4-DOF articulated leg; (b) 3-DOF SCARA leg; (c) 2-DOF SCARA leg;
(d) 1-DOF leg.

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Unmanned Ground Vehicles for Smart Farms
DOI: hp://dx.doi.org/10.5772/intechopen.90683
Another interesting example is the structure of BoniRob [39], a real wheel-legged
platform for multipurpose agriculture applications, which consists of four indepen-
dently steerable powered wheeled legs with the structure illustrated in Figure d
(1-DOF legs with a 2-DOF wheeled foot). This robot can adjust the distance between
its wheel sets, making it adaptable to many agricultural scenarios. The platform
can be equipped with common sensorial systems used in robotic agricultural
applications, such as LIDAR, inertial sensors, wheel odometry, and GPS.Moreover,
Structure Characteristics
A 4-DOF
articulated leg
with a 2-DOF
wheeled foot
(Figure a)
Advantages:
• Full terrain adaptability and ground clearance control.
• Crop control.
• Full capability for walking.
Disadvantages:
• A huge number of actuators (24) that jeopardize the robot’s reliability.
Use in smart farms:
• This structure is the most complex structure that exhibits complete wheel
positioning and orientation in its working volume. However, the orientation
of the wheel does not provide additional characteristics regarding stability or
traction. Thus, this structure provides the same advantages as other structures
(see Figure c) but with extra complexity, which will jeopardize its application in
smart farms. This structure is presented here as the most complex platform.
A 3-DOF
motion-
decoupled leg*
with a 2-DOF
wheeled foot
(Figure b)
Advantages:
• Full terrain adaptability and ground clearance control.
• Crop adaptability.
• Full capability for walking.
Disadvantages:
• A large number of actuators (20).
Use in smart farms:
• This structure provides full positioning of the wheel in its working volume and can
control the robot’s body leveling, which allows for the wheel plane to be aligned
with gravity, which provides an excellent robot’s stability using fewer motors than
the structure illustrated in Figure a. In addition, this structure can walk under
static stability, an interesting feature when the robot works in very irregular, soft,
or muddy terrain. Its terrain adaptability, ground clearance control, and crop
adaptability, along with its medium complexity, make this structure the most
promising for use in smart farms in the long term.
A 2-DOF
motion-
decoupled leg*
with a 2-DOF
wheeled foot
(Figure c)
Advantages:
• A medium number of actuators (16).
• Full terrain adaptability and ground clearance control.
• Crop adaptability.
Disadvantages:
• Limitations for walking.
Use in smart farms:
• This structure can control the ground clearance, leveling, and distance between
wheels; the latter determines the adaptation to different crops (distance between
crop rows). Nevertheless, the wheel moves on a vertical-cylindrical surface rather
than in a working volume. This fact impedes the robot from walking and, thus,
exhibits worse characteristics than the structure illustrated in Figure b. In any
case, it can be a proper structure to introduce wheel-legged vehicles and could be
used in the short term.

Agronomy

the robotic platform can be retrofitted and upgraded with swappable application
modules or tools for crop and weed identification, plant breeding applications, and
weed control. This robotic platform is completely powered by electricity, which is
more environmentally friendly but reduces its operational working time compared
to conventional combustion-engine systems. Nevertheless, this robot configuration
requires custom-built implements, which prevent the reuse of existing implements
and, thus, jeopardize the introduction of this robot to the agricultural market.
. Smart farm UGV characteristics
In addition to their needed characteristics for infield operations, the robots
fulfilling the demands of a smart farm will require the operating requirements sum-
marized in the following paragraphs and Table  .
Small size: The idea that using small robots provides many advantages over the
use of conventional large vehicles has been widely discussed over the past decade
[22, 40]. It is broadly accepted that although several small robots can cost the same
as a large machine and accomplish the same amount of work, using small robots
allows a multi-robot system to continue a task even if a number of robots fail (re-
planning the task). Moreover, the reduced weight of the small robots reduces terrain
compaction and allows farmers to acquire robots incrementally.
Structure Characteristics
A 1-DOF leg
with a 2-DOF
wheeled foot
(Figure d)
Advantages:
• A small number of actuators (12).
• Crop adaptability.
Disadvantages:
• No terrain adaptability or ground clearance control.
• Limitations for walking.
Use in smart farms:
• This structure has no capabilities for walking or controlling the ground clearance
of the vehicle or its leveling. However, the structure is simple and could be used as
an introductory robot structure for smart farms in the short term.
*Cylindrical, Selective Compliant Articulated Robot Arm (SCARA) or Cartesian.
Table 4.
Wheel-legged structures.
Figure 6.
Model of wheel-legs: (a) full terrain-crop adaptability, (b) full terrain and partial crop adaptability.

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DOI: hp://dx.doi.org/10.5772/intechopen.90683
Flexibility: Agricultural robots must be capable of adapting to many different
scenarios (e.g., crops, row types, etc.) and tasks (e.g., plow, sow, fumigate, etc.).
Thus, the robots must also be able to accommodate different agricultural imple-
ments, which should attach to or connect to (respectively, detach or disconnect
from) the robots automatically.
Although conventional tractors are proven and highly reliable machines, they
lack some adaptability features. Tractors have normally fixed distances between
wheels, which makes them unsuitable for working on crops with different distances
between rows. Using mobile platforms capable of controlling the distance between
wheels could alleviate this problem, allowing the machines to adapt to different
crops under different situations.
Characteristics Val u e
Dimensions Length: ~3.0m;
width: ~1.50m;
height:~1.00m
Weight 1200–1700kg
Payload 500–1000kg
Comments: These characteristics are estimations based on the current medium-sized vehicles reported in
this chapter that are capable of carrying agricultural implements. Robots for carrying sensing systems can
be truly small (low payloads), but vehicles for treatments need to carry medium to heavy loads (pesticides,
fertilizes, etc.). For example, existing sprayers [45] weigh approximately 600–700kg including 200–300L of
active ingredient.
Speed 3–25kmh−1
Comments: Treatment speed is limited by the treatment process that depends on physical laws. However,
robots need to move among working fields minimizing moving time; therefore, they must feature a
reasonably high top speed.
Position accuracy ±0.02m
Comments: The current DGPS accuracy seems to be sufficient for real applications. However, specific
real-time localization systems, RTLS, can be used in small areas where GNSS is unavailable (radio frequency
identification tags (RFID), ultra-wide band tags (UWB), etc.). These technologies will be essential in smart
farms to ensure positioning precision in GNSS occluded areas.
Clearance 0.35–1m
Comments: Weed control is performed at an early crop-growth stage; therefore, the minimum ground
clearance of the robot must be approximately 0.35m. A ground clearance of approximately 1m will facilitate
application of treatments at later crop-growth stages. The ideal approach would be to control the ground
clearance to optimize the working height of the implements based on the crop. Existing robots cannot control
their ground clearance, but some wheel-legged configurations can meet this specification (Figure a, b, and c).
Track width 1.50–2.25m
Comments: To preserve crops in narrow-row situations, a tramline control is required; however, in wide-row
crops, the tramlines must be located in the inter-row spacing. Taking maize as an example, which is planted
at an inter-row spacing of approximately 0.75m in some areas in Europe, a robot track width of 1.50 to 2.25m
is required to enable 2 or 3 rows to pass under the robot’s body. Controlling robot track width is imperative
in a smart farm world. This characteristic is exhibited by wheeled-legged robots, which makes them a good
candidate for UGVs in smart farms.
Energetic autonomy ~10h
Comments: Robots based on combustion engines (e.g., tractors) can operate autonomously for
approximately 10hours, at minimum. The duration of autonomous operation for electrically driven systems
should be similar. Some existing prototypes already meet this expectation [31]. In any case, the increasing
improvement in battery technology will enlarge the energetic autonomy of future vehicles and robots.
Table 5.
Prospective characteristics for UGVs in smart farms.

Agronomy

Maneuverability: Robots must be capable of performing small radius turns while
adapting to different terrain. This last feature requires independent vertical control
of wheels with respect to the robot’s body.
A steering system capable of zero-radius turns would be a proper solution,
and this feature can be implemented by different structures as discussed in the
previous section. Thus, minimization of headlands and wheel distance control
can be achieved using either conventional or new articulated structures. Among
the conventional structures, the skid steering scheme based on wheels or tracks is
capable of zero-radius turns without additional steering mechanism, which helps
in minimizing the headlands. However, separating and controlling the distance
between contralateral wheels/tracks requires an active system (which already exists
for some tracked vehicles used in the building industry).
Mobile platform structures based on coordinated or independent steering
schemes can achieve zero-radius turns, but they still lack intrinsic track width
control and require additional mechanisms. Another structure is the wheel-
legged mechanism. Legged robots exhibit high terrain adaptability on irregular
ground, but wheeled robots have speed advantages on smooth terrain; that is, they
complement each other. Therefore, the most complete wheel-legged mechanism
(Figure a) is a leg with three degrees of freedom [38] with an active wheel as a
foot, where the wheel is steered and driven separately. This is a disruptive design
not verified yet that will provide extraordinary characteristics to robots for smart
farm applications. Thus, the wheels drive and steer, while the legs provide track-
width control and terrain adaptation, i.e., they control the robot’s body leveling
and ground clearance. This is the most capable system regarding ground clearance
and body pose control, but it comes at the cost of higher mechanical complexity.
Nevertheless, intermediate solutions can be developed to reduce the number of
actuators while maintaining appropriate robot characteristics. Table  summarizes
different wheel-legged theoretical solutions indicating advantages and shortcom-
ings, and Figure  shows some sketches of practical solutions.
Resilience: Resilience is the ability to recover from malfunctions or errors.
Initializing complex robots is a time-consuming procedure, especially when several
robots are collaborating on the same task. Agricultural mobile robots must be resil-
ient enough to ensure profitability. Thus, they must be easily shut down and started
up (essential for error recovery); moreover, they must facilitate changing between
manual operation mode and autonomous operation mode and vice versa.
Efficiency: UGV should be more efficient than conventional, manned solutions.
This can be accomplished by systems that:
• Minimize energy consumption by optimizing the robot trajectories during the
mission
• Drastically reduce the use of herbicides and fertilizers by using intelligent
detection systems, tools, and decision-making algorithms
• Eliminate the need for a driver and minimize operator risk
• Minimize unnecessary crop damage and soil compaction
Friendly human-machine interfaces (HMI): A friendly interface is required to
facilitate the introduction of robots into agriculture and to achieve profitability.
Intuitive, reliable, comfortable, and safe HMIs are essential for farmers to accept
robotic systems. The HMIs should be implementable on devices such as smart-
phones and tablets.

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Unmanned Ground Vehicles for Smart Farms
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Communications: Communications in the smart farm must capitalize on CPS and
IoT to collect sufficient data to take advantage of the big data techniques and enable
communication with the cloud for use via different services (software as a service,
platform as a service, and infrastructure as a service) offered by cloud providers [41].
Wireless communications with the operator and/or a central controller for con-
trol commands and data exchanges, including images and real-time video, will be
required. Wireless communication among robots will also be required for coordina-
tion and collaboration.
Standardization of mechanical and electrical/electronic interfaces: Commercial
equipment must comply with well-defined standards and homologous procedures
before adoption by industry. Subsystems such as LIDAR units, computers, and
wireless or Internet communication (4G/5G) devices and GNNS receivers and
antennas are already off-the-shelf components, but mobile platforms must also
cope with some standards related to agricultural machinery [25, 42].
Safety: Safety systems for agricultural robots must focus on three stages: (i)
safety to humans, (ii) safety to crops, and (iii) safety to the robots themselves.
Safety for humans and robots can usually be accomplished through a combina-
tion of computer vision, LIDAR, and proximity sensors to infer dangerous situa-
tions and halt robot motion, whereas safety to crops is achieved through precise
steering that guides the robot to follow the crop rows accurately using the crop
position acquired at seeding time or real-time crop-detection systems. Following
these three stages, a step forward in safety for agricultural robots would be the
integration of a two-level safety system relying on the following:
• A low-level safety system that detects short-range obstacles with the purpose
of avoiding imminent collisions. This level should be implemented within the
robot controller and based on commercial components.
• A high-level safety system that detects and discriminates obstacles at an
adequate distance to allow the robotic system to make decisions (i.e., re-plan-
ning a trajectory). This level should include vision, infrared, and hyperspectral
cameras that provide information about the surroundings. Optical flow methods
should be applied to detect obstacles in motion and compute their speed and
direction to predict potential collisions [43]. Hence, optical sensors should track
obstacles and their movements, dynamically compute safe zones, and adjust a
robot’s speed and direction of movement according to the given situation.
Regardless of the exact approach, standards on safety machinery must be taken
into consideration [42] to ensure that systems will meet regulations and will be able
to achieve certification.
Environmentally friendly impact: Both intervention mechanisms (implements)
and mobile robots must be environmentally friendly (e.g., use fewer chemicals
and cause less soil compaction) while improving the efficiency of the agricultural
processes (i.e., reduce chemical costs while equaling or improving production). In
addition, current agricultural vehicles use fossil fuels that emit large amounts of
pollutants into the air such as carbon dioxide (CO2), nitrogen oxide (NOX), carbon
monoxide (CO), and hydrocarbon (HC) [44]. Furthermore, fuel can be spilled onto
the ground, which is a long-term pollutant. These elements alter the environment
and damage the ecosystem. One possible solution—envisaged as the likely future
solution—is the use of electric vehicles.
Implements: The use of the conventional three-point hitch to attach implements
to tractors should be changed as robots are introduced into agriculture. Instead,
implements should be aligned with the robot’s center of gravity to optimize the

Agronomy

payload distribution and minimize compaction. Mechanical attachment and
electrical connection to the implement should be automated. The definition of
these types of interfaces is a pending issue; nevertheless, an intermediate solution
allowing the use of both new and conventional attachment devices (three-point
hitch) will facilitate the gradual introduction of robotic systems into the agricul-
tural sector. Obviously, developing new robots and adapting existing implements to
a new attachment/connection system is the only way to introduce the robots to real
applications.
HMI: An HMI for operators to communicate with robots should be imple-
mentable on portable equipment (smartphones, tablets, etc.). Operators will use
such devices to send commands and receive responses and data. Moreover, an
additional device—an emergency button that works using radio signals—must
be provided to stop the robots from malfunctioning or unsafe situations. These
interfaces must be true user-friendly devices to be operated by farmers rather than
by engineers, which is a vital aspect for the introduction of robotics into agriculture,
as it is for industry and services.
Autonomy: Two basic types of autonomies will be needed in smart farms:
behavioral autonomy and operational autonomy. Behavioral autonomy is primarily
associated with autonomous robots and relies on artificial intelligence techniques.
It refers to the robot’s ability to deal with uncertainty in its environment to accom-
plish a mission. Operational autonomy is associated with the tasks the robot has to
accomplish autonomously to become a UGV, i.e., the tasks required for the robot
to work continuously without human intervention: refueling or recharging (ener-
getic autonomy, see Table ), herbicide/pesticide refilling, implement attaching,
and crop offloading. These tasks, which can be solved using current automatic
techniques, are currently being done with human intervention and should be fully
automated in the smart farms.
Based on the existing agricultural vehicles and robot prototypes, robots to be
deployed in smart farms should meet also the characteristics presented in Table .
. Conclusions
The world population is increasing rapidly, causing a demand for more efficient
production processes that must be both safe and respect the ecosystem. Industry
has already planned to meet production challenges in the coming decades by
defining the concept of the smart factory; the agriculture sector should follow a
similar path to design the concept of the smart farm: a system capable of optimizing
its performance across a wide network, learning from new conditions in real time
and adapting the system to them and executing the complete production process in
an autonomous manner. Smart factory and smart farm concepts have many com-
monalities and include some common solutions, but some specific aspects of smart
farms should be studied separately. For example, the design of UGVs for outdoor
tasks in agriculture (field robots) presents specific characteristics worthy of explicit
efforts.
This chapter focused on reviewing the past and present developments of UGVs
for agriculture and anticipated some characteristics that these robots should
feature for fulfilling the requirements of smart farms. To this end, this chapter
presented and criticized two trends in building UGVs for smart farms based on (i)
commercial vehicles and (ii) mobile platforms designed on purpose. The former
has been useful for evaluating the advantages of UGV in agriculture, but the latter
offers additional benefits such as increased maneuverability, better adaptability
to crops, and improved adaptability to the terrain. Clearly, independent-steering

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Unmanned Ground Vehicles for Smart Farms
DOI: hp://dx.doi.org/10.5772/intechopen.90683
and skid-steering systems provide the best maneuverability, but depending on
their complexity, wheel-legged structures can provide similar maneuverability and
improved adaptability to crops and terrain as well as increased stability on sloped
terrain. For example, the 4-DOF articulated wheeled leg (Figure a) and the 3-DOF
SCARA leg (Figure b and a) exhibit the best features at the cost of being the
most complex. Note that although both structures have the same maneuverability
features and adaptability to crops and terrain (ground clearance, body leveling,
etc.), the 3-DOF SCARA leg involves one fewer motor per leg, which decreases the
price and weight and improves the reliability of the robot. However, the 2-DOF
SCARA leg also exhibits useful features regarding maneuverability, adaptability
to crops, and adaptability to terrain (ground clearance control and body leveling)
while using fewer actuators (Figure c and b). For agricultural tasks carried
out on flat terrain, the 1-DOF leg with a 2-DOF wheeled foot provides sufficient
maneuverability and adaptability to crops with very few actuators (leg structure as
in Figure d).
However, these robots also require some additional features to meet the needs of
the smart farm concept, such as the following:
i. Flexibility to work on very dissimilar scenarios and tasks.
ii. Maneuverability to perform zero-radius turns, crab motion, etc.
iii. Resilience to recover itself from malfunctions.
i v. Efficiency in the minimization of pesticide and energy usage.
v. Intuitive, reliable, comfortable, and safe HMIs attractive to nonrobotic
experts to ease the introduction of robotic systems in agriculture.
vi. Wireless communications to communicate commands and data among the
robots, the operator, and external servers for enabling CPSs, IoT, and cloud
computing techniques to support services through the Internet.
vii. Safety systems to ensure safe operations to humans, crops, and robots.
viii. Environmental impact by reducing chemicals in the ground and pollutants
into the air.
ix. Standards: operational robots have to meet the requirements and specifica-
tions of the standards in force for agricultural vehicles.
x. Implement usage: although specific onboard implements for UGV are
appearing, the capability of also using conventional implements will help in
the acceptation of new technologies by farmers and, hence, the introduction
of new-generation robotic systems.
xi. Autonomy: both behavioral autonomy and operation autonomy. Regarding
power supplies, automobiles worldwide will likely be electric vehicles
powered by batteries within the next few decades; thus, agricultural vehicles
should embrace the same solution.
Regardless of these characteristics, UGVs for smart farms have to fulfill the
requirements of multi-robot systems, which is a fast-growing trend [22, 40, 46].

Agronomy

Author details
PabloGonzalez-De-Santos*, RoemiFernández, DeliaSepúlveda, EduardoNavas
and ManuelArmada
Center for Automation and Robotics (UPM-CSIC), Madrid, Spain
*Address all correspondence to: pablo.gonzalez@car.upm-csic.es
Multi-robot systems based on small-/medium-sized robots can accomplish the
same work as a large machine, but with better positioning accuracy, greater fault
tolerance, and lighter weights, thus reducing soil compaction and improving safety.
Moreover, they can support mission coordination and reconfiguration. These
capabilities position small/medium multi-robot systems as prime future candidates
for outdoor UGVs in agriculture. Additionally, UGVs for smart farms should exhibit
some quantitative physical characteristics founded on past developments and cur-
rent studies that are summarized in Table .
Finally, autonomous robots of any type, working in fleets or alone, are essential
for the precision application of herbicides and fertilizers. These activities reduce
the use of chemicals generating important benefits: (i) a decrease in the cost of
chemical usage, which impacts in the system productivity; (ii) an improvement in
safety for operators, who are moved far from the vehicles; (iii) better health for the
people around the fields, who are not exposed to the effects of chemical; and (iii)
improved quality of foods that will reduce the content of toxic products.
Acknowledgements
The research leading to these results has received funding from (i)
RoboCity2030-DIH-CM Madrid Robotics Digital Innovation Hub (“Robótica
aplicada a la mejora de la calidad de vida de los ciudadanos. fase IV”; S2018/NMT-
4331), funded by “Programas de Actividades I+D en la Comunidad de Madrid” and
cofunded by Structural Funds of the EU; (ii) the Agencia Estatal Consejo Superior
de Investigaciones Científicas (CSIC) under the BMCrop project, Ref. 201750E089;
and (iii) the Spanish Ministry of Economy, Industry and Competitiveness under
Grant DPI2017-84253-C2-1-R.
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms
of the Creative Commons Attribution License (http://creativecommons.org/licenses/
by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.

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DOI: hp://dx.doi.org/10.5772/intechopen.90683
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