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Specification development of robotic system for
pesticide spraying in greenhouse
Vitalijs Komasilovs*,**
, Egils Stalidzans*,**
, Vitalijs Osadcuks*,**
and Martins Mednis*
* Department of Computer Systems, Latvia University of Agriculture, Jelgava, Latvia
** Faculty of Computer Science and Information Technology, Riga Technical University, Riga, Latvia
e-mail: [email protected], [email protected], [email protected], [email protected]
Abstract – Robotic systems are replacing humans in
increasing number of dangerous tasks. Pesticide spraying in
greenhouses is one of tasks where a number of robotic systems
have been developed and tested. Therefore it is the right time
to raise the question about optimisation of the costs efficiency
of robot colony. In case of homogenous robotic system only the
number of universal robots has to be determined. The
drawback is that some subsystems (vision, spraying) might be
utilised at a very low rate and the investment may be
inefficient. To avoid that it is possible to design a
heterogeneous robotic system where the solution space of a
robotic system specification is large and optimisation task
becomes very complex. Still a good solution can reduce costs.
Optimisation of specification of heterogeneous robotic system
for pesticide spraying (plant inspection, spraying and pesticide
transport functions) in a greenhouse with rectangular layout is
performed using genetic algorithms and corresponding open
source GAMBot-Eva software.
Optimisation results demonstrate three groups of solutions
that are within 3% range of lowest possible costs: 1)
homogeneous system of universal robots, 2) heterogeneous
system with two types of robots (inspection-spraying robots
and spraying-transportation robots) and 3) heterogeneous
system with two types of robots without duplicating functions
(inspection-spraying robots and transportation robots).
Applied optimisation approach can be adapted for different
robot missions.
I. INTRODUCTION
Robots are taking over more and more functions from
humans where precision and repeatability in routine tasks
are needed and where human workers are exposed to a
danger. One such task is cultivation of crops in fields and
greenhouses where human operators still manually perform
most operations on the crop although they are often highly
repetitive and sometime even dangerous [1]. Autonomous
mobile robots and their systems are classified as a part of
the Precision Agriculture, which aims to optimize
agricultural field management focusing on the enhancement
of crop knowledge, environmental protection and
economics [2].
Researches on using autonomous mobile robots for
various tasks in greenhouse environment are being
performed for over several decades, but only recent
advances in embedded control systems, optical plant
recognition and localisation methods allowed develop
individual robots and cooperative robot systems (colonies
of robots) ready for in-field tasks. Wherewith topics such as
design of grippers, sprayers, weed control and other plant
harvesting and treatment tools [3], plant recognition [4],
logistics and adaptation of greenhouse environment [1] as
well as frameworks and fully operational prototypes of
individual robots [5] and colonies of robots [6] are widely
discussed.
One of cases where mobile robots can be successfully
used in greenhouses is the pesticide spraying task [7]. Good
progress can be observed in automation of particular
operations like recognition of places on a plant to be
sprayed [8], [9], spraying technologies[10–12], protection
of the robot itself from pesticides [13] and other operations.
Taking into account that in real application more than one
robot will be needed there is still a lack of studies to find
out optimal specification of robotic system to assure that a
functioning robotic system has been developed with
minimal costs trying to utilize efficiently all the
components of system.
The robotic system or colony of robots is defined as a
multi-robot system where some robots are in charge of
some responsibilities to work with others in a cooperative
way to do same tasks, in a collaborative way, to
communicate each other to be more efficient or to take
decisions in a collective way, etc. with a common colony-
wide goal [14]. Colonies of mobile robots could be
effectively used instead of plant cultivation equipment with
fixed position (fixed watering tubes, pesticide sprayers,
fertilizers, conveyors etc.) to allow easily migrate between
types of crops which as a rule results in reconfiguration of
the greenhouse interior.
The specification may be optimised assuming that a
homogenous robotic system is needed and the main task is
to determine the number of necessary robots. In this case all
the robots would be the same and it can happen that some
tools on robots are utilised very seldom thus leaving space
for improvement of return of investments (ROI).
Heterogeneous robotic systems allow more elastic approach
at the costs of increasing space of solutions [15]. Still the
reward of optimisation can be savings of costs which may
well cover the optimisation expenses.
A dedicated procedure for optimisation of the
specification of robot systems has been developed earlier
[16]. That approach is demonstrated using grass mowing
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CINTI 2013 • 14th IEEE International Symposium on Computational Intelligence and Informatics • 19–21 November, 2013 • Budapest, Hungary
978-1-4799-0197-5/13/$31.00 ©2013 IEEE
and collecting task as a case study [17] indicating good
applicability of genetic algorithms [18]. Analysis of results
indicates that different composition strategies of robotic
system can be applied while some rules like proportion of
some implemented units remain constant.
In this research we aim to discover industrial factors in
case of pesticide processing of greenhouse plants that
influence the optimal specification of robotic system
assuming that humans should be eliminated from contact
with pesticides using the approach of Komasilovs [16].
The procedure is applied to the pesticide spraying task in
the greenhouse to find out and classify different
specification approaches with similar return of investments
(ROI) period. It is found that three groups of solutions (one
homogeneous and two heterogeneous) have very similar
ROI period.
II. LAYOUT OF THE GREENHOUSE
During modelling based optimization procedure we have
used rectangular layout of greenhouse (see fig. 1). The
greenhouse was composed of 80 rows with 150 plants on
each row. Interval between rows is 1.5 m, interval between
plants is 1 m. It is assumed that pesticide storage is 50 m
away from greenhouse entrance.
General mission for the robotic system was to inspect the
plants and spray the pesticides where it is needed. Relative
amount of diseased plants was assumed equal to 40%.
It was assumed that overall amount of work for the
system is to perform 1000 full cycles of greenhouse
inspection and spraying mission. One cycle includes one
full treatment of all diseased plants in the particular type of
greenhouse (see fig. 1).
III. FUNCTIONAL DECOMPOSITION OF PESTICIDE
SPRAYING TASK
According to concepts of specification optimization
procedure [15] the mission for the robotic system is defined
using components and tasks.
A. Defining components of robotic system
The components required for pesticide spraying mission
are shown in Table I.
Spending needed for purchasing each component
(investment cost) are expressed in abstract monetary units.
Operating power of each component is defined and is used
for estimating operating cost of the robotic system. We
define additional properties for the components needed for
detailed analysis of dynamic behaviour of the robotic
system.
Mobile robot platform is used to carry all other facilities
needed for the mission. We have considered two alternative
robot platforms: fast platform with moderate weight
carrying capacity (can include small tank of pesticides) and
slower platform with high capacity (can include large
pesticide tank and able to refill small tanks of pesticides).
Distance measurement sensor (range finder) is used for
precise positioning near the plant to enable exact plant
inspection and spraying. We have analysed three sensor
alternatives: laser range finder, distance measurement and
localization using radio beacon and receivers [19], and
distance measurement based on visual information
processing.
High definition camera is used for plant inspection and
disease location. Sprayer is utilized for pesticide infliction
onto the diseased plants and includes internal storage for
pesticides needed for spraying. External pesticide tank is
used for transporting pesticides from the storage to the
greenhouse. The volume of the tank depends on carrying
capacities of robot platform.
Standalone pesticide storage is equipped with automated
docking and refuelling system for approaching robots.
Fig. 1. Greenhouse layout
Table I. Robot components for greenhouse spraying missing
Component Inv.cost,
m.u.
Power,
W Properties
Light platform 4000 500 Max weight: 10 kg Max speed: 3 m/s
Length: 0.2 m
Heavy platform 7000 900 Max weight: 60 kg
Max speed: 1 m/s
Length: 0.4 m
Laser range
finder
800 2 Tolerance: 0.05 m
Sample rate: 100 Hz
Radio receiver
for beacon based
localization
10 2 Tolerance: 0.5 m
Sample rate: 100 Hz
Visual range finder
50 2 Tolerance: 0.2 m Sample rate: 3 Hz
Radio beacon 1000 100 -
HD Camera 70 2 -
Sprayer 50 5 -
Tank 100 5 -
Pesticide storage 500 25 -
V. Komasilovs et al. • Specification Development of Robotic System for Pesticide Spraying in Greenhouse
454
B. Defining mission tasks
We distinguish three tasks in global greenhouse spraying
mission: plant inspection task, when the diseased plants are
located; spraying task when the diseased plants are being
sprayed with pesticides; and transportation task, when
pesticides are transported from the storage to the worksite.
Specification optimization procedure requires two
features to be defined for each task: total amount of work
for the robotic system, and function used for calculating
performance of a particular agent. In order to express agent
performance we calculate the time needed to accomplish
single unit of work of the task.
For the inspection task total amount of work is a
number of plants within greenhouse (1).
(1)
where
– inspection task work amount (plant);
– number of rows in greenhouse;
– number of plants in row.
We distinguish three phases of inspection task and
calculate time required to perform it for particular agent.
First, robot is moving between plants (2), positions near the
plant (3) and then inspects the plant.
(
) ⁄ (2)
where
– time for moving between plants (s);
– distance between plants (m);
– platform approaching distance (m);
– platform moving speed (m/s).
⁄
(
)⁄ (3)
where
– positioning time (s);
– robot positioning speed (m/s);
– positioning tolerance for task (m);
– position sensor sampling rate (1/s).
Agent performance for inspection task is calculated from
a time needed for inspecting single plant (4).
(
)⁄ (4)
where
– agent performance for the task (1/s);
– inspection time (s).
Total amount of spraying task is a number of diseased
plants in the greenhouse (5).
(5)
where
– spraying task work amount (plant);
– relative amount of diseased plants.
We distinguish three phases of spraying task similarly to
inspection task. Robot moves between diseased plants (6),
positions near plant (7) and sprays pesticides.
[(
)
⁄ ] ( )⁄ (6)
where
– time for moving between plants (s);
– max. moving speed for the task (m/s).
⁄
(
)⁄ (7)
where
– positioning time (s);
– positioning tolerance for task (m).
Agent performance for spraying task is calculated from a
time needed for spraying single plant (8).
(
)⁄ (8)
where
– agent performance for the task (1/s);
– spraying time (s).
For the transportation task total amount of work is
expressed in transported litres of pesticides (9).
(9)
where
– transporting task work amount (l);
– pesticide volume per plant (l).
We define three phases of transportation task as follows:
robot is refuelling at pesticide storage (10), moves to the
greenhouse (11) and then moves to working site within the
greenhouse (12).
⁄ (10)
where
– pesticide refueling time (s);
– transport volume (l);
– pesticide flow speed (l/s).
(
)⁄ (11)
where
– time for moving to greenhouse (s);
– distance to storage (m);
– max. moving speed for the task (m/s).
( ) ⁄ (12)
where
– time for moving to worksite (s);
– distance between rows (m);
Agent performance for transportation task depends on a
transport volume and a time needed for transporting single
litre of pesticide (13).
(
)⁄ (13)
where
– agent performance for the task (1/s).
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CINTI 2013 • 14th IEEE International Symposium on Computational Intelligence and Informatics • 19–21 November, 2013 • Budapest, Hungary
Used task parameters and their values are summarized in
Table II.
IV. SET-UP OF GENETIC ALGORITHM FOR SPECIFICATION
OPTIMISATION
GAMBot-Eva software [20] was used for specification
optimization of robotic system which is based on genetic
algorithm and allows fast evaluation of large number of
solution candidates.
Genetic representation of the solution candidate is based
on a list of agents and a number of their instances used
within the solution [18].
A list of feasible agents is obtained by combining
recently defined components and applying constraints. The
list of constraints is determined by the greenhouse spraying
mission and is used to eliminate illogical component
combinations. The list includes following constraint
entities:
single type of robot platform is allowed for an agent;
single type of position sensor is allowed for an
agent;
position sensor, camera, sprayer and tank should be
mobile (placed on mobile platform);
pesticide storage should be stationary;
agent has to be able to perform at least one task
( ).
The total number of possible combinations of
components exceeds . Taking into account
defined constraints the feasible solution space is reduced
down to combinations, which are considered
during optimization.
GAMBot-Eva specific parameters used during
experiments are as follows:
maximum number of agent instances: 30;
GA generations limit: 2000;
processing batch size: 50;
population size: 100;
mutation rate: of genes;
crossover rate: 35% of chromosomes;
randomization rate: 5% of chromosomes.
V. OPTIMIZATION RESULTS AND DISCUSSION
During experiments we have repeated specification
optimization procedure for 20 times using aforementioned
setup of greenhouse spraying mission. Because of
stochastic nature of optimization approach used within the
procedure obtained results might change between runs.
The output of the optimization procedure is a formal
specification of robotic system, which lists required agents
(combinations of the components) and the number of their
instances, e.g. 3x ([ranger-visual][platform-light][sprayer]).
According to results of 20 optimisation runs three
solutions have costs within 3% of the best value of
objective function found.
Most of the proposed solutions (55%) specify the
homogeneous robot system containing identical robots
which are able to perform all three tasks of the mission:
inspect plants, spray pesticides and transport them from
storage to worksite. The solution implies application of 5
identical robots based on light platform and equipped with
camera, sprayer, pesticide tank and visual range finder.
Estimated total cost of ownership (TCO) for such system is
172 358 monetary units. Another large group of proposed solutions (30%) stand
for semi-heterogeneous system with two types of robots.
The solution implies application of 2 robots which are able
to perform inspection and spraying tasks (build onto light
platform and equipped with camera, sprayer and laser range
finder) and 3 robots capable to perform spraying and
transportation task (build onto light platform and equipped
with sprayer, pesticide tank and visual range finder).
Estimated TCO for this type of system is 174 560 monetary
units.
Only 10% of proposed solutions defined robot system
specifications with no duplicating functions. Example
solution is composed of 5 agents capable to perform
inspection and spraying task (light platform, visual range
finder) and 1 agent dedicated for transportation task (heavy
platform, radio beacon and receiver based range finder).
Another example solution is composed of 1 agent capable
to perform inspection and transportation task (heavy
platform, laser range finder) and 4 agents dedicated for
spraying task (light platform, visual range finder).
Estimated TCO of these solutions is 177 244 monetary
units.
The rest of proposed solutions (5%) stand for custom
composition of agents and can be considered as experiment
exception.
Among robot platform alternatives preference is given to
the light platform (98% of agents). It could be explained by
faster moving time and lower power consumption. Heavy
platforms have lower benefit even taking into account much
higher workload capacity.
Among sensor alternatives preference is given to visual
distance measurement (71% of agents) and to laser range
finders (26% of agents). It is explained by sufficient
Table II. Task parameters for greenhouse spraying mission
Parameter Symbol Value
Number of rows in greenhouse 80
Number of plants per row 150
Distance between plants (m) 1.0
Distance between rows (m) 1.5
Positioning tolerance for inspection task (m) 0.5
Inspection time (s) 1.0
Relative amount of diseased plants 0.4
Max. moving speed for spraying task (m/s) 1.5
Positioning tolerance for spraying task (m) 0.2
Spraying time (s) 10.0
Pesticide volume per plant (l) 0.2
Pesticide flow speed at storage (l/s) 0.1
Distance between greenhouse and storage (m) 50.0
Max. moving speed for transporting task (m/s) 2.0
V. Komasilovs et al. • Specification Development of Robotic System for Pesticide Spraying in Greenhouse
456
tolerance of visual measurement system for all tasks and by
lower costs. The potential of laser range finders with high
tolerance stays unused during the tasks. While radio beacon
and receiver based distance measurement is suitable only
for inspection task and has relatively high costs.
CONCLUSIONS
Specification optimisation procedure for heterogeneous
robotic systems can be applied for pesticide spraying task in
a greenhouse to determine the groups of best solutions to
support the decision about the solution of robotic system to
be implemented. In case of necessity more exact evaluation
can be done using dynamic simulations on playgrounds like
Player/Stage or similar software. The main advantage of
proposed method is automatic screening of very large
solution space avoiding the risk to implement greenhouse
specialist’s intuition-based solution which might be far
from optimum in terms of costs.
According to the performed optimisations only three
types of solutions out of large solution space were
competitive taking into account the costs of purchase and
exploitation for given number of operation cycles.
It turned out that homogeneous robot system is
competitive compared with two best heterogeneous
systems: 1) heterogeneous system with two types of robots
(inspection-spraying robots and spraying-transportation
robots) and 2) heterogeneous system with two types of
robots without duplicating functions (inspection-spraying
robots and transportation robots). The total costs of best
solutions among all the three groups lay within 3%. Refined
search including additional criteria can be performed in this
case.
In case if the competitors of best solution are much worse
according to objective function (in this case it was costs)
more detailed comparison may be skipped.
ACKNOWLEDGMENTS
The research was supported by the European Regional
Development Fund within the project „Development of
technology for multiagent robotic intelligent system”.
Agreement No. 2010/0258/2DP/2.1.1.1.0/10/APIA/VIAA/
/005.
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