Autonomous Aerial Robotic System for Smart Spraying Tasks: Potentials and Limitations

Abstract

Continuous demands for growth in agricultural productivity and modern demands for the sustainable agricultural approach are bringing farmers into a new technological era. With all the limitations and risks, precision agriculture and other related technologies show great potential in solving the challenges of sustainable and more efficient agricultural production. Nowadays, unmanned aerial vehicles (UAVs) are able to perform a wide range of agricultural tasks, from data collection to smart spraying. This chapter presents the concept of a modular autonomous robotic system that, based on available technologies, materials, and system components, can be produced and applied in precision agriculture. The primary purpose of such a system, which consists of a multirotor UAV and docking station, is to save the time required to perform the task and to reduce environmental and soil pollution. Several problems have been addressed, which affect performance and energy consumption, for example, of spraying a field crop.

1. Introduction

Agricultural production has continuously progressed from primitive techniques and tools to modern comprehensive digitized processes and systems. This evolutionary process can be presented in four main steps from Agriculture 1. To Agriculture 4.0. Agriculture 1.0 is based on simple tools, manpower, and animal forces and can be placed up to the nineteenth century. Agriculture 2.0 follows first industrial revolution and introduces various agricultural machinery operated by farmers and use of plenty chemicals. Agriculture 3.0 emerged in the twentieth century through the usage of automation and robotic techniques thanks to the rise of information and communication technologies (ICTs). Production became more efficient, and some environmental problems were reduced. In the present day, the main aims of Agriculture 4.0 are associated with the introduction of further automation and new digital technologies such as Internet of things (IoT), big data, artificial intelligence (AI), remote sensing, cloud computing, wireless sensor network in agriculture production, allowing a transition toward smart and sustainable farming. This advanced automation and process digitalization have resulted in emergence of the precision agriculture (PA), a farming management concept that utilizes the available technology with aims to improve productivity, efficiency and profitability, quality of the crops and product, along with sustainability and the protection of the environment. Although the principles of PA have been known for more than 25 years, they became interesting to farmers in the last decade due to technological advances and the adoption of new technologies. Thanks to intensive research and technological advances, unmanned aerial vehicles (UAVs) have also undergone through tremendous technical progress over the last decade, which is why they are used today to perform a variety of tasks in many industries. The global agriculture unmanned aerial vehicles (UAVs) market is expected to reach 5,7 billion of USD by 2025. One of the promising areas of application is also the use of UAVs in PA where they are used for a whole range of tasks, from data collection to smart spraying tasks. The utilization of various technologies in PA has been extensively researched and documented in several scientific papers. Nowadays, some of the key terms related to PA are remote sensing, automated hardware, control systems, software, global positioning system (GPS) guidance, robotics, unmanned ground vehicles (UGVs), UAV, and so on.

Information technologies (ITs) used in PA and criteria for their comparison and selection, to store, recover, transmit, and manipulate agricultural data are identified in [1]. The identified IT are GPS, multimedia devices (devices that allow capturing images or videos, such as smartphones or cameras), nano sensors, remote sensors, sensors in general, unmanned aerial systems (UASs), UAV, UGV, variable rate technology (VRT), and wireless sensor networks (WSNs). A survey given in [2] includes wireless communication technologies, sensors, and wireless nodes used to assess the environmental behavior, the platforms used to obtain spectral images of crops, the common vegetation indices used to analyze spectral images, and applications of WSN in agriculture. Authors have also proposed a smart solution for crop health monitoring based on the Internet of things (IoT) and comprising two modules, the wireless sensor network–based system to monitor real-time crop health status and a low-altitude remote sensing platform to obtain multispectral imagery. The work [3] deals with the influence of the fourth industrial revolution on PA. The revolution is expected to spur new technological innovation in six areas: artificial intelligence, robotics, IoT, unmanned vehicles, three-dimensional printing, and nanotechnology. Additionally, it will include a range of new technologies that use big data to incorporate the physical, biological, and digital worlds. Detailed analysis of UAV applications for PA is given [4], where all applications are divided into three categories: UAV-based monitoring applications, UAV-based spraying applications, and multi-UAV applications where multiple UAVs are used to accomplish a task. The application of small UAS for mapping and monitoring in PA is discussed in [5].

PA must quantify variations in soil and crop within agricultural fields, hence the following works also discuss various remote sensing technologies [6], sensor fusion [7], and deep learning techniques [8] to be able to automate processes and make decisions based on the sensor readings. Some research papers also deal with specific types of corps, such as orchard management [9], monitoring of nitrogen status of potatoes [10], detecting green weeds in preharvest cereals [11], and rice [12]. The main driver of PA was tractor GPS technology, which enabled accurate terrain mapping and meeting individual crop needs with different dosages of pesticides for different areas, depending on the reading from different sensors that can be fixed or mobile. Nowadays, ground vehicles of various types, sizes, and power sources are used to accomplish various tasks for PA purposes. Design and field evaluation of a ground robot as a new phenotyping platform that can measure individual plant architecture traits accurately over large areas at a subdaily frequency is demonstrated in [13]. Autonomous mobile robot based on a commercial agricultural vehicle chassis as a robotized patch sprayer is presented in [14], while in [15], the development of a small electrical robot intended to use for autonomous spraying is shown. In work [16] solar-powered UGV is presented that has multiple degrees of freedom positioning mechanism, and it is equipped with a robotic arm and vision sensors, which allow to challenge irregular terrains and to perform precision field operations with perception. There are many applications of solar systems used in agricultural production, and some are listed in the paper [17]. Numerous studies have been conducted, which consider heterogeneous robotic systems, mainly combinations of UGV and UAV. Ground and aerial measurements used for estimating nitrogen levels on-demand across a farm are presented in [18]. The main tasks of UGV in the context of UAV-UGV cooperation are considered in research [19]. The capability of images acquired from UAVs with multispectral cameras to detect weed patches and to support herbicide patch spraying is presented in [20]. Furthermore, the research [21] described a fleet of heterogeneous ground and aerial robots, developed, and equipped with innovative sensors, enhanced end effectors, and improved decision control algorithms to cover a large variety of agricultural situations.

UAVs have been used in a wide range of applications to support digital agriculture, including field scouting [22], precision management of oil palm plantation [23, 24], estimating plant's parameters such as leaf area index and height [25], health assessment [26], and variable rate spraying [27, 28]. The technologies of aerial electrostatic spraying using UAVs are being investigated [29], as well as the development of automatic aerial spraying systems based on UAVs [30, 31]. The design of an embedded real-time UAV spraying control system, based on low-cost hardware, which supports onboard image processing, is proposed in [32]. The use of computer-controlled swarms of UAVs for crop spraying enables nonuniform coverage of high precision and time efficiency, therefore an algorithmic control method for autonomous UAV swarm spraying is proposed in [33]. The static configuration usually adopted in the literature deals with the development of spraying processes have shortcomings in terms of changing weather conditions (e.g., sudden changes of wind speed and direction). To overcome this deficiency, in paper [34], an adaptive approach for UAV-based pesticide spraying in dynamic environments is presented. Also, in the paper [35], an algorithm for adjustment of the UAV route with respect to changes in wind intensity and direction is described, input of which is the feedback obtained from the WSN deployed in the crop field. Furthermore, the influence of windward airflow and droplet size on the movement of droplet groups is investigated. In [36], a numerical simulation and computational fluid dynamics analysis on spray drift movement are conducted for multirotor UAVs. Since the different spray requirements are possible, the variable spray system, which can rapidly adjust the flow range of the nozzle, is presented in [37]. The key problem in the task of smart spraying using drones is the distribution of droplets, so many scientific papers have been published on this topic [38, 39, 40].

In this chapter, a concept of an autonomous aerial robotic system intended for smart spraying tasks is presented. The presented system consists of a mobile base station and a multirotor UAV armed with spray equipment and a spraying tank. The main purpose of the concept is autonomous execution of spraying tasks on parcels of different surface ranges. The advantages and current problems related to the use of UAVs in smart spraying tasks are stated, and guidelines for the design of the base station are given. Since multirotor UAVs are characterized by high energy consumption, special emphasis is placed on the characterization and adequate selection of components in order to obtain satisfactory flight performance and necessary flight duration. Furthermore, the aircraft system is divided into four subsystems (equipment and payload, electric energy, electric propulsion, and control subsystem), thus achieving a certain degree of modularity. In the last part of the paper, guidelines for designing a real system through the phases of characterization, analysis, and simulation are presented.

2. Precision agriculture: UAV integration

UAVs are found in a wide range of applications in PA due to their advantages over the use of current agricultural machinery. Their flexibility and a high degree of autonomy, along with low labor needs and avoidance of crops and soil damage, significantly increase agricultural productivity and sustainability. The efficient use of chemicals in agricultural production is crucial in order to reduce harm to human health and also to reduce costs. UAVs can be an effective and inexpensive alternative to conventional spraying, and applications can be extended to crop fertilization, seed sowing, and similar activities. The equipment in charge of spraying can be relatively easily retrofitted to this type of aircraft, which further reduces the cost of the system. In terms of system autonomy, a multirotor type of UAV is able to perform precision pesticide spraying missions given the specifics of the crop, the severity of the disease or pest, the location, and other requirements. The key thing in carrying out the mission is precisely controlled droplets deposition on the target and reducing the environmental pollution. Several UAV system parameters need to be considered, including flight route (path pattern), spraying height, flight speed, nozzle flow rate, number and orientation of nozzles, and others. There are several commercial smart spraying systems, and one of the most used all-in-one solutions is DJI Agras (Figure 1) [41].

Figure 1.

DJI Agras MG-1 commercial aircraft [42].

Multirotor unmanned aerial vehicles intended for plant protection can be used on flat plots but also hilly and extremely uneven terrain. The application of an aerial robotic system for smart spraying missions in the rural area of Hrvatsko Zagorje, which is characterized by hilly terrain (relief), was considered, where typical landscape is shown in Figure 2. Apart from the demanding terrain, the problem is the fragmentation of plots and an uneven distribution of crops (by square footage and shape). Besides, some plots are very difficult to access with the machinery currently in use because there are very narrow roads between plots that are often unorganized, and some plots do not have any access roads. The abovementioned implies the need to design a flexible robotic system that can be used on parcels of wider square footage. In this chapter, the concept of an aerial robotic system consisting of a mobile base station and a multirotor UAV armed with spray equipment and a tank is considered. The possibility of performing vertical take-off and landing of a multirotor type of UAV allows easy docking of the aircraft with the base station.

Figure 2.

Presentation of a typical landscape in Hrvatsko Zagorje characterized by small and irregular plots.

A base station is a mobile multifunctional docking facility that has several functions. From the aspect of system planning and control, the essential component is a computer with associated modules that send and receive wireless signals from the aircraft online and also serve as an interface between the user and the aircraft. The mission parameters can be set via the base station, i.e., the flight can be planned based on the tasks that the aircraft needs to perform. The base station will determine flight parameters (path, speed, height) based on the required pesticide amount for specific area and the volume of spraying tank. Mission parameters determine the course of execution since this type of system can be used for different dimensions of plots and can also be used to perform a task on several plots. The base station should be able to change the batteries as needed for the mission and recharge the tank. After the aircraft completes the first part of the task and consumes the chemical, it returns vertically to the base station to fill up the tank and replace the battery. After the change, the aircraft performs a vertical take-off and continues to perform the task of spraying at the place where it stopped before loading. It follows from the above mentioned that the base station must be designed in such a way as to enable aircraft take-off and landing, two-way communication, easy and safe replacement of batteries, and pump to fill up the tank. In addition to the listed basic functions, the base station can also have a module (generator) for charging batteries. Figure 3 schematically shows the concept of an autonomous aerial robotic system consisting of a multifunctional mobile base station and a multirotor aircraft for smart spraying tasks.

Figure 3.

Schematic representation of the concept of an aerial robotic system.

3. Aerial robot system description

Multirotor aircraft are mechanical systems that exist in 3D space with six degrees of freedom (DOF) consisting of N rotors. From the aspect of dynamics, they are considered as symmetrical rigid bodies, where the only moving parts are the rotors of the propulsion assembly on whose axes are mounted propellers with a fixed pitch angle. Propellers create aerodynamic forces and moments by their rotation, so it follows that the angular velocities of the rotor are the only variables that have a direct impact on flight dynamics. The development and design of multirotor UAVs depend on constraints in size and energy consumption, and a key parameter in system design is aircraft weight. Given that the multirotor type of UAV is characterized by high energy consumption, it is extremely important to correctly select the components and parameters of the system in order to reduce energy consumption and extend the flight duration. To ensure overall flight performance, it is necessary to determine the thrust-to-weight ratio (TWR), and as a rule, aircraft are designed with approximately twice the thrust of the weight.

3.1 Equipment and payload subsystem

The equipment of a multirotor aircraft depends primarily on the mission to be performed, which affects the selection of components and parameters of other subsystems. In addition to standard applications where multirotor UAVs are used in data collection missions, mainly using different types of cameras, they can also be used in special applications. Since the paper considers the application in precision agriculture in smart spraying tasks, the payload of the aircraft is divided into two segments. The first segment consists of the equipment in charge of distributing and spraying the chemical under pressure. The essential parts are a set of hoses and manifolds, sprinklers, nozzles, and pump assembly. It is mounted on the existing aircraft frame, mainly on the landing gear or propulsion arms. The second segment consists of a tank containing a chemical that has a variable mass since it is deployed during the mission.

One of the most widely used commercial aircraft for agricultural purposes is the DJI Agras MG-1, an electric motor multirotor UAV with protection against dust and water. It is designed for applications in a variety of environments and terrains and can be used in fields, terraces, orchards, or other areas. It uses a microwave radar located on the underside of the aircraft that in combination with an altitude stabilization system maintains the aircraft at the desired height above the plants in order to ensure optimal spraying. The volume of the tank is 10 liters, and according to the manufacturer's specifications, it can cover an area of 7–10 acres per hour. The spray mechanism consists of four sprinklers located on two sides of the aircraft. The diameter of the aircraft is 1520 mm, and the configuration consists of eight rotors (octorotor) placed in one plane as shown in Figure 4 [41].

Figure 4.

DJI Agras representation [41].

3.2 Electric energy subsystem

As already mentioned, multirotor UAVs are characterized by high energy consumption as they use rotating wings (propellers) to move in 3D space. The energy subsystem must provide sufficient energy to the aircraft to perform the intended missions and must be compatible with the components of the propulsion subsystem. When selecting the components and parameters of the energy subsystem, the energy requirements of the propulsion subsystem must be taken into account, which in turn depends on the mass and size of the aircraft and the number of propulsion units. The energy subsystem consists of one or more lithium polymer (LiPo) batteries and energy distribution elements. LiPo batteries consist of one or more electrochemical cells in which lithium ions transfer charge between electrodes. They are characterized by high energy density and high discharge rate, which allows higher power and consistent energy flow to the propulsion subsystem. The main parameters of LiPo batteries are their mass, capacity, discharge rate (C), and the number of cells that determine the operating voltage (S).

Batteries are the heaviest elements of the aircraft system and have the greatest impact on aircraft dynamics, so it is advisable to place them as close as possible to the aircraft center of gravity. Battery capacity also plays an important role as the flight time of the aircraft depends on it. Hence, the ratio of mass and capacity of the battery is one of the key data when designing a multirotor UAV system. The parameters of the considered Gens ace commercial high-voltage (12S) batteries are listed in Table 1. In addition to batteries, the energy subsystem consists of sophisticated circuits for energy distribution and measurement of electrical parameters of the battery.

Battery	Capacity (mAh)	Discharge rate	Mass (g)	Dimension (mm)
Tattu 10000	10000	30 C	2741	182*118*68
Tattu Plus 1.0 16000	16000	15 C	4700	224*163*90
Tattu Plus 1.0 22000	22000	25 C	6058	237*173*116
DJI MG-12000S	12000	20 C	3800	195*151*70

Table 1.

Typical characteristics of high voltage (12S) LiPo batteries [43].

3.3 Electric propulsion subsystem

The propulsion subsystem of a multirotor UAV is determined by the parameters of the geometric arrangement of the configuration and the characteristics of the propulsion units that make it up. All designs of the propulsion subsystem (configurations) have in common that they consist of N propulsion units (rotors) that generate the necessary forces and moments for the movement of the aircraft in 3D space. Conventional multirotor configurations generally consist of an even number of equal rotors symmetrically arranged in one or more parallel planes. Each pair consists of CW and CCW rotors for the purpose of canceling the reactive moment about the vertical axis of the aircraft. The required performance of the aircraft depends on the type and profile of the mission such as payload, flight duration, power consumption, or other specific requirements. The choice of the propulsion configuration and the type of propulsion units is the key step in the design of the multirotor type of UAV because the flight performance depends on it. Figure 5 shows the configurations on the same scale of the six-rotor configuration considered in this paper and the eight-rotor configuration that makes up the propulsion subsystem of the DJI Agras commercial aircraft.

Figure 5.

Conventional multirotor UAV configurations.

The considered electric propulsion units (EPUs) enable precise and fast regulation of control forces and moments that directly affect the position and orientation of the aircraft. The EPU consists of an electronic unit (driver) and a mechanical motor assembly on whose rotor a fixed-pitch propeller is mounted. The brushless DC (BLDC) motor is the central part of the EPU for which there are mostly detailed manufacturer specifications with relevant collocation of driver and propeller. There are EPU components on the market with a very wide choice of motor power, so they can be used in a wide range of multirotor applications, including precision agriculture missions such as smart spraying tasks where carrying a heavier payload is required. The motor speed is controlled by an integrated power inverter, the so-called electronic speed controller (ESC), which generates the switching sequence of the motor phases for the desired RPM specified by the control unit. The rotor of the propulsion unit on which the propeller with fixed pitch is mounted creates aerodynamic forces and moments necessary for the movement of the aircraft. BLDC motor is defined with motor velocity constant (back EMF constant) Kv. Motors of low power, small dimensions, and large motor constants are used mainly to power micro and small aircraft intended for entertainment or sports (drone racing). On the other hand, high-power and large-dimensions motors with small motor constants are intended for heavy equipment and loads (heavy lift).

In this study, for the needs of the aerial robotic system concept, five combinations of EPUs are considered, which are combined with a high-voltage (12S) energy subsystem setup. Based on the specification of the propulsion components manufacturer, the characterization of EPUs intended for heavy payloads was performed. Selected BLDC motors have a low motor velocity constant (Kv <200), which means that they have lower speeds, so in combination with larger-diameter propellers, they achieve higher torques. Figure 6 shows the thrust force and efficiency of EPUs as a function of electrical power for the five considered setups. Propeller designations indicate geometry where the first two numbers indicate the propeller diameter in inches, e.g., a propeller marked G32x11 has a diameter of 32″. The next two numbers indicate the pitch of the propeller, also in inches, as the distance that propeller advances during one revolution.

Figure 6.

Considered EPU characteristics [44].

3.4 Control subsystem

The basic task of the control subsystem is to guide the multirotor UAV in 3D space according to the given input variables. In addition, it takes care of the functioning of the entire system and is a kind of interface between the multirotor and the docking facility. The control subsystem primarily consists of a flight controller (FC), state estimation sensors, telemetry, and a remote control receiver. Since the multirotor type of UAVs is characterized by inherent instability, the key component of the aircraft is FC, and it can be freely said that it represents the brain of the aircraft. To control the aircraft concept that would be used in precision agriculture, Pixhawk open-source FC is being considered. The control algorithm generates control signals that it sends to the propulsion units in order to achieve the desired movement in 3D space, i.e., to perform the mission. Orientation sensors are integrated into the Pixhawk FC, and as for the position of the aircraft, it is obtained using a peripheral compatible GPS.

From the aspect of system design, the control subsystem is very demanding because, in addition to the choice of hardware, it is necessary to design a software solution. The considered control unit has already been used in the research so far, and certain segments of code have been tested. Figure 7 schematically shows the custom firmware that is planned to be used in the future to control the aircraft in precision agriculture.

Figure 7.

Schematic representation of custom firmware main subsystems.

A series of experiments were conducted to primarily verify the motor mixer subsystem for different aircraft configurations. This will be extremely important for implementation on a prototype aircraft as configurations with different geometric arrangement parameters and with different propulsion unit characteristics have been tested. The first series of experiments was done with a small custom-made quadrotor with x-arrangement. Figure 8 shows the experimental results of reference attitude tracking. In the next series of experiments, a configuration consisting of eight rotors in a + arrangement, so-called octorotor, was tested (Figure 9).

Figure 8.

Attitude control experiment for custom quadrotor.

Figure 9.

Attitude control experiment for custom octorotor.

4. Toward heavy-payload multirotor UAV prototype

This chapter will present the results of individual design phases of a multirotor aircraft that is planned to be used as an integral part of the presented concept of an air robotic system for applications in precision agriculture. Experimental measurements of the considered propulsion units were conducted, on the basis of which payload analysis was performed for several configurations. Based on obtained physical parameters, a model was set up and preliminary simulations were performed, with the help of which it is possible to estimate the energy consumption of a real system.

4.1 Electric propulsion unit characterization

In order to adequately select aircraft components to ensure the performance of aircraft required for certain tasks (maximum cargo weight, flight speed, flight time, others), it is important to determine the thrust generated by a specific combination of motor and propeller and to determine power consumption. Based on a certain thrust, the maximum load capacity of the aircraft is determined with regard to the defined thrust-to-weight ratio. Based on electricity consumption, more specifically through the relationship between electric current and thrust, it is possible to estimate the maximum flight time depending on the mission. Manufacturers of propulsion elements generally also provide specifications, as previously shown in Figure 6; however, these data are not in all cases consistent with actual characteristics. Therefore, for a more precise analysis of the propulsion, it is necessary to perform characterization, and in this paper, the method described in the previous research was used [45] utilizing the experimental test stand RCbenchmark 1780 [46]. Figure 10 shows the thrust force as a function of the angular velocity of the rotor for the considered propulsion units where the measured experimental characteristics and the characteristics according to the manufacturer's specifications are shown. Furthermore, Figure 11 shows the electric current as a function of the thrust force for the purpose of estimating the flight time.

Figure 10.

Thrust force with respect to rotor angular velocity.

Figure 11.

Electric current with respect to the thrust force.

4.2 System mass distribution analysis

As mentioned in the previous sections, the weight (mass) of the aircraft plays an important role as it will directly affect the maximum payload of the aircraft. In order to be able to accurately determine the payload of an aircraft, the weight of all aircraft components/subsystems has to be known. Taking into account the choice of propulsion components, and the configuration of the aircraft, the choice of the energy subsystem will greatly affect the carrying capacity of the aircraft. Figure 12 graphically shows the dependence of the mass distribution of the aircraft subsystems in the case of three conventional aircraft configurations and various battery capacities. It can be seen that the mass of the avionics (control) subsystem can be considered fixed since the components that make up the control subsystem do not change in relation to the changes of other subsystems. The mass of the propulsion subsystem varies with the number of EPUs required to perform certain missions and significantly affects the total mass of the system. In terms of energy consumption, more units will require more energy, which means that more batteries will be needed, and the mass of the batteries, i.e., the mass of the energy subsystem, has the greatest impact on the total mass. All this affects the maximum payload of the aircraft. A larger number of EPUs will generally provide higher thrust and a higher payload mass, although they will also require a heavier energy subsystem with the ability to deliver more energy. The process of designing a multirotor aircraft is extremely demanding, especially given the limitations that exist in the size of the aircraft, but also energy consumption (Figure 12).

Figure 12.

System mass distribution for three conventional configurations.

Although a change in battery capacity will not change the overall thrust generated by the propulsion subsystem, it will affect the overall mass of the system and thus the payload of the aircraft and the flight time. The higher-capacity batteries have an expected higher mass, thus leaving less space for payload mass and requiring higher energy consumption to compensate for heavier aircraft. Thus, a higher-capacity battery does not always result in a longer flight time.

Since the system is divided into four key subsystems, as mentioned earlier, a certain degree of modularity is allowed. In the further work, special attention will be paid in the design phase to the construction of modular elements, which would allow easy assembly of aircraft configurations with different numbers of rotor arms, thus further expanding the diversity of the system and potentially reducing energy consumption. In this sense, the guidelines presented in the previous work [47] regarding the small educational aircraft will be used.

4.3 Simulation results

In the use of UAV for spraying or similar tasks such as fertilization or even seed sowing, the payload capacity is specific. As the aircraft tank is filled with the required chemicals (either fertilizer or seed) and depleted during usage, the weight (mass) of the aircraft will also continuously decrease. In order to efficiently conduct the spraying task with low energy and time losses, the flight path needs to be planned with regard to the tank size and the chemical consumption rate. The rate of chemical consumption is also not fixed for the whole parcel but depends on the crop health condition estimated based on sensor readings. Flight planning is an extremely complex process that includes many parameters, which will be the subject of future research.

To determine the energy consumption of the aircraft during the spraying mission and to approximately determine the required flight time, it is necessary to conduct computer simulations in the development phase of the prototype. In this way, the development time and the price of the product can be significantly shortened, as the possibility of incorrect selection of system components and parameters is reduced. Preliminary simulations are presented in this paper, where typical spraying parameters are taken: nozzle spraying rate of 0.375 L/min, spray width of 5 m, and flying speed of 2 m/s. The aircraft is equipped with a spraying tank of 25 L volume, and four spraying nozzles, which gives a total spraying rate of 1.5 L/min. Based on those specifications, a minimum flight time of 16.5 min is required to deplete the whole tank, and in that time area of approximately 10000 m² can be covered. The aircraft parameters (mass and inertia) were obtained based on a simplified 3D CAD model. Figure 13 shows the most elementary case when the mission consists of uniform spraying of the crop. Air resistance or any disturbances are not included in the simulations, this is planned in the next phases of the research.

Figure 13.

An example of the aircraft trajectory in a spraying mission.

Based on the planned flight consisting of take-off, horizontal flight in the pattern, and landing, the angular velocities of individual EPUs or direct control signals (PWM) can be extracted from the model, as shown in Figure 14. As mentioned, with the consumption of the chemical, the mass of the aircraft is reduced, which results in fewer forces and moments of the propulsion subsystem required for motion in 3D space, which can be seen in the figure where the control signals are continuously reduced. The main goal of the simulation is to determine the energy consumption of the aircraft by approximating the individual energy consumption of each EPU, which can be determined if the flight pattern and the change in aircraft mass are known. Since there are characteristics of propulsion units, it is easy to connect electrical quantities (electric current, voltage, and electric power) with the control signal or the angular velocity of the rotor. This can further allow the selection of optimal system components and parameters, which is extremely important in the system design phase.

Figure 14.

Motor control signals related to given spraying mission.

5. Conclusion

This paper discusses the current state of the art regarding the use of multirotor UAVs for spraying tasks in precise agriculture. The possibilities of application of the proposed autonomous aerial robotic system consisting of a mobile base station and a multirotor type of UAV were demonstrated. The purpose of the presented system was to autonomously perform spraying tasks on different ranges of surfaces, including large crops parcels. In such a system, special emphasis was placed on the functions of the mobile base station, which had to provide support for autonomous spraying and be a user interface. By selecting the correct components and parameters of the aircraft system, satisfactory spraying coverage, flight performance, and flight duration were achieved. In the future work, it is planned in the first phase to prototype the aircraft and then extensive testing of the control module. In the second phase, it is planned to design custom aircraft equipment and a mobile base station.

Acknowledgments

This research was funded by European Regional Development Fund, Operational programme competitiveness and cohesion 2014–2020, as part of the call for proposals entitled “Investing in science and innovation—first call,” grant number KK.01.1.1.04.0092.