Planning Algorithm for Route and Task Allocation of Plant Protection UAVs in Multiple Operating Areas

Li, Yibai; Cao, Guangqiao; Chen, Cong; Liu, Dong

doi:https://doi.org/10.1155/2022/7599021

Mathematical Problems in Engineering

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Abstract Introduction Discussion Conclusions Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2022 | Article ID 7599021 | https://doi.org/10.1155/2022/7599021

Planning Algorithm for Route and Task Allocation of Plant Protection UAVs in Multiple Operating Areas

Yibai Li,¹Guangqiao Cao,¹Cong Chen,¹and Dong Liu¹

Academic Editor: Francesco Franco

Received08 Mar 2022

Accepted05 May 2022

Published26 May 2022

Abstract

This paper takes the flying defense team as the operating unit and studies the route planning and tasks in the process of plant protection operations as system problems. In the route planning, the flight defense team’s operating time is used as the optimization goal of route planning. Taking the UAV battery and drug load as constraints, the flight mission is divided into subtasks that can be completed by a single UAV in a single flight. On this basis, the particle swarm algorithm is used to optimize the task allocation, so as to achieve the goal of UAV plant protection route and task division with minimum time loss. In order to verify the effectiveness of the method in this paper, 3 and 4 UAVs flying defense teams were used as the experimental objects, and experiments were carried out in large and small field groups. In terms of time consumption, the method in this paper is compared with the unit operation area method and the single size method. The energy consumption advantage is weaker than time consumption. In terms of time consumption, the method in this paper can greatly improve the efficiency of large-area operation areas. Compared with the unit operation area method, 3 drones can increase the efficiency by 58.98%, which is increased by 10.22% compared with the unit size method. The efficiency of 4 units can be increased by 58.08% compared with the unit operation area method, and the efficiency is increased by 10.22% compared with the unit size method.

1. Introduction

Low-altitude application by unmanned aerial vehicles (UAV) for plant protection is a new application technology suitable for the development of modern agriculture in China. UAV plant protection operation has the advantages of adjustable height, strong penetration of mist flow up and down, and high operation efficiency [1, 2]. In practical production, plant protection UAV operations are generally performed by the flight defense fleet, and their operational efficiency is affected by the route planning and task assignment in the operation area [2–5].

The route planning of plant protection UAV is a full-coverage route planning problem based on the boustrophedon method [6, 7], with optimization objectives such as minimum operation time, energy consumption, number of turns, and redundant coverage [8, 9]. A method of constructing Gaussian projection environment coordinates is used to convert the coordinate system. By changing the coordinate origin, the heading angle and heading distance of the plant protection UAVs are optimized, and the minimum redundant coverage rate can reach 2.8% [5]. In terms of heuristic route search, a vibration genetic algorithm is used to search the flight route of the convex-shaped UAV, which accelerated the search efficiency of the algorithm. [10] On this basis, a point-to-point minimum fuel consumption table and genetic algorithm are used to optimize the sequence of trajectory connections and construct a route planning model with minimum fuel consumption. [11] In order to minimize nonplant protection operation time, the grid and the gravitational search algorithm are used to optimize the flight route by adding movement weights.

In current studies, the relationship between the field size and the UAV battery, maximum drug load, and other factors are rarely considered comprehensively, which may lead to short UAV operation time in the field but long round-trip flight time and higher frequency for supply, thus reducing the overall operational efficiency. [12, 13] Moreover, field investigation suggests that in practical operation and production, battery loss is a significant part of the operational loss of the flight defense fleet; reducing the battery charging frequency can lower battery loss. [14, 15] Therefore, reducing the time consumption of plant protection UAVs in a single operation area and lowering battery consumption can improve the operation efficiency of the flight defense fleet and reduce operating costs.

Plant protection UAV operations generally take the form of flying defense teams. A flying defense team can have multiple plant protection drones operating. Reasonable arrangement of the tasks of each drone can improve the operational efficiency of the flying defense team. Otherwise, it may lead to “high efficiency of a single UAV but low efficiency of the fleet”. [16, 17] Current studies of plant protection task assignments are mainly conducted based on the following two ideas: (1) Unit operation area method: each operation area is taken as one subtask [18]. (2) Unit size method: a unit operation size is determined empirically, and the operation area is divided into one or more subtasks by unit operation size. [19] The ranking and assignment of subtasks can be performed using the intelligent heuristic algorithm. [19, 20] In the research, on subtask partitioning at present, the lack of consideration of the battery capacity or maximum drug load of plant protection UAVs leads to unscientific partitioning of subtasks and inaccurate calculation of the time and energy consumption of subtasks. [21, 22] Hence, the efficiency advantages of plant protection UAV fleet can hardly be exerted based on the task assignment strategy. [23–25] The research on UAV task assignment has been researched in the fields of military, logistics, and so on. [26] In order to solve the task allocation problem, the research generally divides job tasks into subtasks that can be executed once and sorts jobs according to the needs of the job flow. On this basis, the heuristic algorithm is used to allocate tasks to achieve the goal of the shortest overall operating time or the least energy consumption. [27–29].

In summary, the current research on the route planning and task assignment of plant protection UAVs mainly has the following two problems: (1) the time and battery drain consumption of resupply are rarely considered with the frequency of replenishment times on field route planning of plant protection UAVs. (2) Plant protection task assignment lacks accurate partitioning of subtasks and the calculation of time and energy consumption for each subtask. The two problems result in the problem of low operation efficiency in practical operation. In order to solve the above two problems, this paper proposes (1) multiple operation areas route planning method that comprehensively considers plant protection operations, supply round-trip distances, and times. (2) By considering the subtask division method of UAV battery capacity and drug-carrying capacity, the particle swarm optimization algorithm is applied to subtask assignment to achieve the goal of minimum total operation time of the flight defense team.

2. Task Assignment Model of Plant Protection UAV

2.1. Model Assumptions

It is assumed that the operation task T includes N operation area blocks, which can be divided into V subtasks performed by a UAV in a single operation, subject to the constraints of battery capacity and drug load. There are K plant protection UAVs in the whole flight defense fleet, that is, up to K UAVs involved in plant protection operations (Table 1). The model in this paper meets the following basic assumptions:(1)UAVs take off from the supply point and return to the site after a single operation is completed(2)Each UAV has the same model, maximum pesticide load, battery capacity, and consumption rate(3)The location and shape of each operation area are known, and it is assumed that there are no obstacles in the field(4)The location of the supply point is fixed(5)All operation area blocks have the same operation priority(6)The coordination waiting time during the supply of multiple plant protection UAVs is not considered

2.2. Model Building

The energy consumption of plant protection UAVs is positively correlated with the flight time, and the operation time of the flight defense fleet is a more crucial factor in the practical operation. Hence, the operation time is selected as an optimization objective in this paper. The in (1) indicates the optimization objective.

Equation (2) indicates that the total operation time includes plant protection operation time, round-trip flight time for supply, and time consumption in the supply process. Empirically, 30 s is generally selected for .

Equation (3) indicates that each unit operation area is visited only once.

Equation (4) indicates that the energy consumption in UAV subtask operation is subject to its battery capacity limit.

Equation (5) shows the pesticide consumption of UAV subtasks, which shall meet the maximum drug load limit.

2.3. Route Planning and Task Assignment Algorithm of Plant Protection UAV Fleet for Multiple Operation Areas

The route and task assignment algorithm of plant protection UAV fleet applicable to multiple operation areas mainly includes the following three parts: route planning in the operation area, subtask partitioning method considering the battery capacity and maximum drug load of UAV, and plant protection task assignment.

2.3.1. Route Planning and Subtask Partitioning Method for Operation Area Based on Supply Consumption

In the current research on the route optimization method for convex polygonal plots, the minimum span method is generally used for optimization. This method is applicable to route planning for operation areas that can be covered by UAV in a single time. However, with relatively large operation areas, it may have a long transfer time between fields, leading to a longer operation time.

In this paper, the routes at all heading angles are traversed based on the scan-line filling method. The total operation time, energy, and pesticide consumption are calculated, respectively. The return flight is required when the energy consumption exceeds 80% of the battery capacity in the plant protection operation to ensure that the UAV can return to the supply point smoothly. At this time, two situations may occur: (1) at one or several heading angles, the plant protection UAV can accomplish the plant protection operation task in a single trip. If there is only one heading angle, it is the route planning angle of the operation area. If there is more than one heading angle, the heading angle with the smallest sum of field operation and return time is selected as the route planning angle for the operation area. (2) The plant protection UAV cannot accomplish the plant protection operation task in one trip at any angle. At this time, the battery capacity and maximum drug load of the plant protection UAV should be considered comprehensively to identify the point of return. In this paper, the point of return is searched by a single route. The starting point and return point of each subtask are at the edge of the operation area [15]. The method for determining the point of return is described as follows.(1)The energy consumption of the plant protection UAV from take-off at the supply point to the start point of the route is calculated. The flight distance of the UAV is , the energy consumption is , and the flight route length is .(2)From the first route, , , the energy consumption , pesticide consumption , and the flight distance of plant protection operation in the route are calculated, respectively; , pesticide spraying dose , , .(3)If exceeds 80% of the battery capacity or is greater than the maximum drug loading capacity of the UAV, then , , . The route from to is taken as a subtask, and is taken as the energy consumption of the subtask. is the pesticide spraying dose for the subtask, and the time consumption is .(4), . Loop this process until all routes are traversed and return to the supply point.

The flow chart of the algorithm is shown in Figure 1. Based on this method, the start point and the energy, time, pesticide consumption sets , , and of plant protection UAV subtasks at each heading angle can be obtained. The heading angle with the least time consumption among all heading angles is selected as the route planning angle for the plot, and the route for the operation area is planned based on the scan-line filling method.

2.3.2. Task Partitioning and Assignment Method Based on Particle Swarm Optimization

After the time, energy, and pesticide consumption of each subtask are calculated, as all plots have the same operational priority, plot operations are sorted based on the proximity principle. Subject to the joint influence of the shape of the operation area and the spraying width of the plant protection UAV, the time and energy consumption of each subtask are not necessarily the same. Hence, the task partitioning problem is transformed into an array partitioning problem; that is, given an array with m elements (m is the number of subtasks), the array is partitioned into n subarrays (n is the maximum number of UAV in the flight defense fleet). The content of the array is the time consumption of the subtask. Each subarray is summed to form a new array sum and make it the smallest partitioning method in max (sum). The particle swarm algorithm is used in this paper to solve the above array partitioning problem, as shown in the following equations:

where is current particle velocity, is current particle position, is the best position of a single particle, and is the best position of particles in a swarm.

The basic operation steps of the particle swarm algorithm are described as follows:(1)Initialize the population. Randomly initialize the velocity and position of particles, where and are the learning factors, and constants are generally selected.(2)Calculate the fitness of each particle according to the fitness function (1) and perform velocity optimization of particles according to equation (6).(3)Update the position update of particles according to (7).(4)Iterate (2) and (3) repeatedly until the number of iterations is reached, or the minimum value is met between generations, and then end the loop.

The overall modeling process of the task assignment model of the plant protection UAV model is shown in Figure 2.

3. Experimental Analysis

3.1. UAV and Terrain Parameter Description

In this paper, according to the operation mode of the Nanjing Essence Plant Protection Flight Defense fleet, the duration of plant protection UAV used in the fleet is 20 min. When the battery power is less than 20%, the UAV shall return to the fixed supply point. The velocity of UAV operation and supply flight is 4 m/s, and the operation width is 2 m. The maximum drug load of UAV is 10 L, and the spraying dose is 1 L/mu. The time to replace the battery and replenish the pesticide liquid at the supply point is 30 seconds. According to the operation experience of the flight team, the operation area of the plant protection UAV in a single trip is about 10 mu when the operation area is regular in shape and relatively large in length-width ratio.

The simulation runs on a personal computer, the processor uses Intel Core i5 and Windows 10, the environment uses python 3.6, and the geometry module is mainly used for the field shape description. The operation field in this paper is a simulated field. Its size and shape have a great influence on the operation efficiency of the plant protection UAV fleet. Hence, it is determined that the small operation area is about 1 mu, and the large one is about 15–100 mu with reference to the stipulations on grid and strip fields in the general rules for high-standard farmland construction. The operation area is abstracted as a two-dimensional plane, the coordinates of its vertices are known, and the coordinates at the position of the supply point are assumed to be [0, 0]. Among them, three operation areas with a relatively large size are taken as scenario 1, including an irregular operation area (vertex coordinates ([0, 0], [100, 0], [200, 100], [0, 200]), a trapezoidal operation area ([0, 220], [180, 220], [180, 500], [75, 520], [0, 300]), and a rectangular operation area ([200, 220], [400, 220], [400, 320], [200, 320])). Four operation areas with a smaller size are taken as scenario 2, including a parallelogram operation area (vertex coordinates ([0, 0], [50, 0], [60, 60], [10, 60]), a triangular operation area ([100, 0], [180, 0], [150, 60]), a trapezoidal operation area ([30, 100], [50, 100], [100, 100], [90 140], [40, 140])), and a rectangular operation area ([120, 100], [170, 100], [160, 140], [110, 140]). The shapes of the operation areas in scenarios 1 and 2 are shown in Figures 3 and 4.

(a)

(b)

(c)

(a)

(b)

(c)

3.2. Route Planning and Task Assignment of Plant Protection UAV Fleet

In terms of route planning for UAV operation areas, the method proposed in this paper and the shortest operation time method are used to plan the route for the operation areas in scenarios 1 and 2, respectively. Assuming that each operation area is covered by a single UAV, Table 2 summarizes the differences in the total time consumption (sum of plant protection operation time, return time, and supply time) in each plot for the routes planned based on the method proposed in this paper and the shortest field operation time method.

Based on the path planning method, the task assignment method proposed in this paper is compared with two traditional methods: (1) Unit operation area method: each operation area is covered by a single UAV responsible for the operation. (2) Unit size method: each UAV is responsible for plant protection tasks in an operation size of 7, 000 m². In both methods, when the battery power of the UAV is less than 20%, or the pesticide liquid carried is used up, the UAV shall return to the supply point.

The method proposed in this paper and traditional methods are discussed using different sorties of plant protection UAV flight defense fleets. The hardware configuration of the flight defense fleet is as follows: (1) assuming that there are 3 UAVs and 2 supply vehicles in the team, and each supply vehicle can serve 2 UAVs, the time and energy consumption for task assignment based on the method proposed in this paper, and the traditional methods are shown in Table 3. (2) Assuming that there are 4 UAVs and 2 supply vehicles in the flight defense fleet, and each supply vehicle can serve 2 UAVs, the time and energy consumption based on the method proposed in this paper, and the traditional methods are shown in Table 4.

The operation area block in scenario 1 is relatively large, with a single operation area of about 10,000–66,666 m², and that in scenario 2 is smaller, with a single operation area of about 667 m²; multiple convex polygon-shaped operation areas are included in both scenarios. In terms of task partitioning frequency, as reducing the frequency of returns to the supply station is considered in the path planning within the operation area based on the method proposed in this paper, the task partitioning frequency based on the multiarea route planning and task assignment method in both scenarios is reduced compared with the two traditional methods, thus decreasing the operation time in a single field.

In terms of subtask partitioning, the battery and maximum drug loading capacity are considered in the method proposed in this paper to determine the return point of the plant protection UAV. The time and energy consumption based on the subtask partitioning and task assignment method proposed in this paper are compared, as shown in Figures 3 and 4. Since the shape of the operation area and the actual battery capacity and maximum drug load of the plant protection UAV are combined in the method proposed in this paper, it avoids the redundant return due to the empirical determination of the unit operation area. The figure indicates that the method proposed in this paper is superior in scenarios with about 3 or 4 plant protection UAVs in large/small operation areas and more advantageous in scenarios with a large number of UAVs in large operation areas. When 3 UAVs are used for operations in scenario 1, the time consumption is 58.98% lower based on the unit operation area method and 10.22% lower based on the unit size method. In scenario 2, the time consumption is 52.73% lower based on the unit operation area method and 6.29% lower based on the unit size method. When 4 UAVs are used for operations in scenario 1, the time consumption is 58.08% lower based on the unit operation area method and 10.22% lower based on the unit size method. In scenario 2, the time consumption is 60.22% lower based on the unit operation area method and 42.29% lower based on the unit size method. It can be seen from the results that the method in this paper has advantages in time and energy consumption in large and small farmland operations.

4. Discussion

This paper discussed the route planning and subtask partitioning and assignment issues based on the minimum return frequency. In terms of route planning, field investigation indicates that the battery charging frequency is a crucial factor affecting the service life of UAVs, and battery loss is also an essential part of the cost of flight defense fleet operations. The traditional route planning method generally takes the shortest field operation time as the optimization goal, ignoring the UAV replenishment round-trip time and battery loss. Using this method for field route planning will result in short-field operation time and long replenishment process time, resulting in longer overall operation time and increased battery loss. [7, 9] In terms of UAV route planning, it is optimized by minimizing the operation and return time based on reducing return trips according to the scan-line filling method in this paper. In the previous chapter, this paper discussed both large and small fields as well as regular and irregular fields and compared the time and energy consumption of the route planning method in this paper and the traditional method. The results show that the route planning method that considers reducing the number of UAV’s return flight has a better effect in the irregular field. This method can reduce the number of return voyages for irregular fields, thereby reducing the time for nonplant protection operations. In terms of area, the larger the area of the operating field, the higher the proportion of time saved.

In terms of subtask assignment, this paper and papers [18, 30] discussed the task assignment issue for small and large agricultural fields in the operation scenario of flight defense fleets. The results of this paper and papers [18, 30] indicate that considering the route planning and task assignment in the field will reduce the overall time and energy consumption of flight defense fleets. Based on the field and fixed area in papers [18, 30], this paper used a computational approach to break down the task assignment method for subtask partitioning according to the route planning results and the duration and drug load of the plant protection UAV. The time and energy consumption of each subtask were accurately calculated, and subtasks were assigned to each UAV in the flight defense fleet based on the heuristic algorithm. The method proposed in this paper has lower time and energy consumption than the subtask assignment method in papers [18, 30].

5. Conclusions

For the operation mode of flight defense fleet with multiple plant protection UAVs in multiple operation areas, a route planning method that takes the time consumption of plant protection UAV operation, round trip for supply, and supply process into comprehensive consideration was proposed. With the route as the basic unit, subtasks were partitioned subject to the constraints of battery capacity and maximum drug load of plant protection UAV. In terms of task assignment, tasks were assigned based on the particle swarm method minimizing the total operation time as the optimization objective.

Simulation experiments were conducted in large and small operation areas using the method proposed in this paper, the unit operation area method, and the unit size method. Flight defense fleets with 3 and 4 UAVs were taken as the arithmetic example to perform statistics on the operation time and energy consumption. The energy consumption advantage is weaker than time consumption. In terms of time consumption, the method in this paper can greatly improve the efficiency of large-area operation areas. Compared with the unit plot method, 3 drones can increase the efficiency by 58.98%, which is higher than the unit area. The method increases the efficiency by 10.22%, and the efficiency of 4 units can be increased by 58.08% compared with the unit plot method, and the efficiency is increased by 10.22% compared with the unit area method.

This paper mainly studies the task allocation model of plant protection drones from a static perspective, and the operation authority of each field is the same. In actual operation scenarios, unexpected situations such as drone failures and weather changes may occur, and due to different crop growth conditions or planting agronomy, the operational priorities of the plots are not exactly the same. Therefore, in the future, the task allocation model of plant protection drones will be studied from a dynamic perspective, focusing on the impact of sudden factors such as drone failures and weather changes on task allocation. At the same time, considering that different plant protection orders are given different weights due to the different urgency of the operation, an operation task allocation model is established from the perspective of balancing operation quality and operation time.

Data Availability

The data used in this paper are only the coordinate points of the parcel edge in 3.1. Therefore, the data statement of this article selects the simulation data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by a grant of the special funding for basic scientific research business expenses of central public welfare scientific research institutes (S20201); Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences (Academy of Agricultural Sciences Office (2014) No. 216); and special funding for basic scientific research business expenses of central public welfare scientific research institutes (15-2001-32).

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Copyright © 2022 Yibai Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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