1. Introduction
In recent years, environmental protection and sustainability have become fundamental needs. Environmental sustainability is the conservation of natural resources and meeting the needs of future generations to avoid potential hazards, and for this purpose, it is vital to interact with the planet responsibly. In this situation, it is necessary to provide future generations with a lifestyle at least an equal in quality to the current generations, and in this direction, it is necessary to use existing natural resources efficiently [
1]. In recent times, one of the most popular areas of sustainability is agriculture. In the last few years, researchers have made traditional agriculture more efficient and functional with new technologies, concepts, and methods within the scope of smart agriculture. In this context, sustainable agriculture can be achieved, and resources such as human and natural resources will be used more efficiently. On the other hand, with the prediction that the world population will reach 9 billion people by 2050, agricultural products should be increased by 70% [
2]. Currently, the food industry is responsible for 30% of the world’s energy consumed and 22% of greenhouse gas emissions. In addition, if a product variety is not suitable for certain regional conditions and the planning in planting and harvesting is wrong, it causes the overconsumption of resources, crop culling, and consequent food shortages. These problems may even cause forced migration in some regions [
3]. Therefore, the agricultural sector has to address serious issues such as climate change issues, limited arable land, and increasing demand for freshwater. In this regard, it is essential that the development policies of states for agriculture are in a sustainable framework [
4].
The role of smart systems in sustainable agriculture is increasing day by day. In this direction, many technological methods are used and recommended. One of them is to use autonomous robots’ technology, but in an environment with many autonomous robots and obstacles, one of the most critical tasks is to transfer these robots safely between two points without them colliding with each other or with obstacles. For an autonomous robot, the problem of searching for a safe path from a source to a destination is called path planning [
5,
6]. This issue can be addressed using various new technologies (e.g., Wireless Sensor Networks (WSNs) and Internet of Things (IoT)) that have a wide range of applications [
7,
8,
9] since they can be designed with heterogeneous or homogeneous devices in distributed, central, or Peer-to-Peer (P2P) architectures. One of the application areas of these technologies which has become popular in recent years is agriculture [
10,
11,
12,
13]. This field has a wide range of smart applications and systems from the cultivation of agricultural products to their logistics [
14,
15,
16,
17]. Although there are many agricultural studies in the literature, the design of smart and autonomous devices and applications that use effective and efficient resources have not been developed. One of them is the proposal of efficient 3D path planning algorithms for mobile devices used in large-scale farmlands, which has many obstacles.
It is important to consider the environment in three dimensions in order for it to be applicable to real-world applications and projects in complex environments. Furthermore, when it comes to mobile robots, three-dimensional movements and areas seem more acceptable. In real application areas, considering the resources of mobile robots, such as energy, finding the optimal path is important. Optimal path planning means that the shortest path length, where the selected path is as far as possible from obstacles, must be smooth without sharp turns and must consider motion constraints. Finding an optimal 3D path planning is a Non-deterministic Polynomial-time (NP-hard) problem [
5,
6]. This makes metaheuristic algorithms a good choice for designing a solution to such a problem. Considering that large-scale environments in 3D environments increase the applicability of this study in real applications, as such, one of the fundamental problems related to robots from past to present is 3D path planning for aerial robots. This problem can become even more complex in large-scale agricultural areas with many obstacles.
In this study, we focused on Gray Wolf Optimizer (GWO)-based algorithms to solve the mentioned problem. In general, GWO-based algorithms have a balanced behavior transition between discovery and use phases because they use the hierarchical group working mechanism of wolves, and they also use a minimum number of control mathematical parameters. In this way, the chance of finding the optimal solution in a short time is high; in addition, the use of resources is also efficient. On the other hand, a GWO-based method was proposed in [
18] for solving the mentioned problem, and they proved it was better than other metaheuristic-based algorithms. In this study, two methods, inspired by Incremental Gray Wolf Optimization (I-GWO) [
19] and Expanded Gray Wolf Optimization (Ex-GWO) [
19], are proposed to address the above issue. The classical GWO algorithm can behave more stably in normal situations (for a somewhat standard environment without many obstacles). The Ex-GWO-based path planning method may be performed more successfully in larger and more crowded environments with larger populations and iterations, and the I-GWO-based path planning method may give good results in medium and smaller, less populated environments. However, the I-GWO is faster than other algorithms.
These methods can be applied in different and diverse agricultural application areas and thus can be useful work for farming and smart agriculture. This paper presents optimized, reliable, and shortest pathfinding mechanisms for smart agricultural robots (e.g., autonomous tractors and agricultural drones) that track crops on large-scale farmlands without the need for the intervention of any human using distributed IoT [
20] and WSN technologies. Thanks to the algorithms proposed in this study, efficient resource consumption and product growth rate can be achieved with low risk and cost. On the other hand, avoiding obstacles in the path planning of agricultural areas is more complex than in other path planning areas because of a dense population of objects that can serve as obstacles such as trees, plants, and buildings. As mentioned above, the most critical problem these mobile robots face is the efficient use of resources such as energy, so this issue is given importance in this paper. In other words, the management of resources with minimal loss is the aim of the paper. In addition, a smooth and efficient pathfinding mechanism is very important for robots; because of this, the system must showcase a sustainable performance. Therefore, the method used with the mobile robots must deliver them to the destination point using the best path. To achieve all these purposes, two different algorithms based on metaheuristic algorithms are presented for each autonomous mobile robot. Indeed, the proposed methods find collision-free optimal paths in an acceptable time with the lowest process costs in different environments containing various obstacles. In this study, it is assumed that there are many obstacles in agricultural land in order to ensure that environmental conditions are realistic. Therefore, the proposed algorithms are simulated and evaluated in a similar environment. The mobile robots in this farmland try to find the optimal paths while bypassing possible obstacles in the farmland with our proposed methods. In addition, in a developed application by the authors for farmers, these employed robots can be monitored and controlled.
In
Section 2 of this paper, the literature studies are presented. The proposed algorithms and their related applications are explained in
Section 3. In
Section 4, the simulation results and performances of each method are evaluated. The last section of the paper includes the conclusions and possible further studies.
3. Materials and Methods
With the increase in the world population, the need for agricultural and food products has also increased. At the same time, the importance and need for smart agricultural systems and methods have also increased. Therefore, it is very important to plan optimal paths without harming objects (barriers) such as plants and trees in agricultural areas. Thanks to the methods proposed in this study, various tasks such as tracking crops in large farmlands can be performed efficiently by autonomous robots. Accordingly, it is necessary to find the optimal path between two points for robots without human intervention. Therefore, in this paper, two adaptive 3D path planning methods were presented for autonomous agricultural UAVs to find collision-free optimal paths in an acceptable time with the lowest process costs in different environments, containing various obstacles. These methods were developed, inspired by two metaheuristic algorithms (I-GWO and Ex-GWO). In addition, many obstacles were assumed to be present in the field in order to prove that the proposed methods are functional, and robots had to find their paths in relation to these obstacles. In addition, this study also used a mechanism for obstacle management. The studies in the literature either do not mention how to detect and prevent obstacles or they used the features of an existing device and did not suggest an algorithm or technique [
20,
64]. This mechanism can be embedded in various sensors and IoT devices.
3.1. Definitions
Before describing the proposed algorithms, the problem must be defined. The main purpose of 3D path planning is to find an optimum (or nearly optimal) path between the source (start) and the destination (target) stations. The path planning function is defined as outlined in Equation (1).
Source and destination denote the relative coordinates of the source and destination positions on the map. Each path has a cost during movement from source to destination. There are different parameters used to define a cost between two points. In most studies, the cost is considered as the consumption of energy, Euclidean distance, and velocity [
20,
21]. For example, the position matrix determines how many stations robots travel from the source to destination. This matrix is defined using Equation (2).
where pi represents the position coordinates of each station that our robot takes on the map. In order to find an optimized trajectory, the proposed algorithms try to minimize cost (length of trajectory in our experiment). The cost of the trajectories is calculated using Equation (3), where
i and
j denote the current and next stations.
Based on Equation (3), the cost of each founded path is obtained by the sum of distances between tuples from source to destination. Drones can be blended with metaheuristics so they can carry out their mission efficiently. In this regard, not only the distance parameters of the drones but also the remaining power amounts of drones are taken into account in the fitness function, which is defined to be more realistic. Therefore, the result from the metaheuristic algorithm can be used in real environments when applied to mobile robots. Random and optimized trajectories are used for UAV movement from source to destination, as shown in
Figure 2. Here, the UAV moved through different stations. In path planning methods, usually, either the robots randomly move or costly processes are undertaken in finding an optimized path, but in this study, we tried to find the most efficient optimized path. This process is performed gradually between both stations. In this way, the UAV tries to find a path between two points. To optimize randomly created paths and to find the best possible trajectory, a method is proposed in this section with a minimum computational cost. Thanks to this method, robots can also actively avoid obstacles. In the final phase, the sum of all tuples’ costs is calculated, and the cost of the path is obtained. The purpose of this study was to find the best path between the start and target stations of each UAV.
Typically, the first step in path planning is to represent the workspace as a map. In the maps, many obstacles were used to make the mobile robots’ tasks of finding the path realistic and complex. The challenge was to avoid various obstacles and to reach the position of the destination. In this study, a large-scale map was prepared to evaluate the proposed algorithms. The boundary of this map is shown in
Figure 3a. In addition, three mobile robots with different start and destination stations were used, and their three-dimension points are given in
Figure 3b. In this paper, it was assumed that the number of obstacles was quite high in order to make our proposed methods applicable in real areas. The number of obstacles was considered to be 150. Therefore, the coordination of some of the obstacles is presented in
Figure 3c, and the full list is presented in
Supplementary File 1. The problem of avoiding and managing obstacles is one of the most important aspects of path planning. The used mechanism includes two main steps and algorithms that take place sequentially, which were inspired by [
47].
3.2. GWO-Based Path Planning
In this paper, the path planning for autonomous agricultural robots was realized using the proposed method, inspired by Incremental Gray Wolf Optimization (I-GWO) and Expanded Gray Wolf Optimization (Ex-GWO) algorithms. These algorithms are inspired by gray wolves in nature. The natural behaviors of gray wolves such as encircling, hunting, and attacking prey have been modeled mathematically. Encircling in the I-GWO and Ex-GWO are calculated based on Equations (4) and (5). The hunting and attacking mechanism in the I-GWO can be obtained by Equations (9)–(11), and in the Ex-GWO, this is based on Equations (12)–(14). There are four types of wolves in each pack; alpha, beta, delta, and omega wolves. Each wolf has different responsibilities in the pack. Alpha, beta, and delta wolves are involved in encircling the prey, and omega wolves update their own positions based on them to attack the prey. The I-GWO algorithm is based on leader wolf’s behavior. Other wolves in the pack update their own positions based on all the wolves selected before themselves. This may result in these wolves being present in similar regions. Thus, they only search for prey (solution) in a particular and similar area, which may be a missing point. The nth wolf in the pack updates its own position based on the
n−1 wolf before it. This algorithm is completely dependent on the alpha wolf. In the I-GWO algorithm, all relative operations are addressed according to Equations (4)–(11), where
t indicates the current iteration,
T demonstrates maximum iteration number,
indicates the position vector of a wolf, and
is the position vector of the prey. Additionally,
D is a vector that depends on the location of the target.
Another metaheuristic algorithm (Ex-GWO) is based on the first three hierarchies of the wolves (alpha, beta, and delta) in a pack. The fourth level of the wolves in a pack update their positions based on the leading three wolves. Generally, the nth wolf updates its own position relative to the prey according to the previous and the first three wolves (Equations (12)–(14)). In the Ex-GWO algorithm, the attacking mechanism is used to avoid the prey from escaping.
It is assumed that the coefficient vectors
and
lead to encircle the prey. The parameter
decreases from 2 to 0 relative to the iteration number. It is used to improve the convergence speed of the algorithm. These parameters control the tradeoff between exploration and exploitation phases. It is used to get closer to the solution range.
and
are the random vectors in a range of [0, 1]. In every algorithm, the leader encircles the prey, then hunts the prey, and finally attacks the prey based on the
value. If
< 1, the wolf is attacking the prey; otherwise, it is busy trying to find prey (solution).
Figure 4 depicts the working of the proposed algorithms, considering exploration and exploitation phases. Thanks to these features, the proposed 3D path planning methods were able to act in a balance between the two phases and try to find the most appropriate path without falling into any local optima trap.
3.3. Working Mechanism of the Method
One of the most commonly applied methods of 3D path planning is to provide a robot with a defined number of static stations and to allow an algorithm to discover the most appropriate path. These types of algorithms are easier to apply mathematically, but generally, their time and space complexity is relatively higher. Here, a pool of stations is assumed so that these stations can be created randomly. Since the station selection in our methods is based on metaheuristic algorithms, it works appropriately with fewer parameters, and therefore, it can work efficiently by consuming resources in an acceptable time. Mathematically, this pool has been described in the structure of the 3xn matrix. The elements of search space represent distances between stations. Each station in the pool is a possible position that a mobile robot can choose as the next station. This pool is used to control the mobile robot’s movement in the area. In addition, by using the information of this pool, it may be possible to help to avoid obstacles. The station selection process used in our methods is presented in Algorithm 1. In this study, the number and positions of stations (mobile robot stopovers) and obstacles are predefined similar to other studies in the literature [
5,
6,
15,
20,
53]. On the other hand, obstacle avoidance is one of the many challenges that exist in the path planning problem. In this study, a method was used to avoid the collision of the UAVs with obstacles (objects or other robots), which benefits from geometric and calculus-based formulae. It was inspired by [
47].
Algorithm 1. Pseudocode of station selection |
State is array of candidate stations w = distance obtained from metaheuristics //Equations (10) and (13) d = The list of distances For each station (i) in pool di = distance between current and next stations + distance between next and destination stations End For MinDist = Min(d) //Min function indicates minimum distance in the list if (MinDist < w) Select station with minimum distance as next station Else Select station by metaheuristics as next station End if
|
Primarily, the proposed methods initialize the random position matrix. Each row of the position matrix defines the path, and the columns represent the number of steps in the path to the destination. These number of stations are denoted as
p. The
presents a coordinate of each station, where m is the aforementioned index of stations and n is the number of search agents in each method (
Figure 5a). The search agents are the configuration parameter of the metaheuristic algorithms. Then, for each metaheuristic algorithm, a search space, based on the position matrix, is initialized. The search space is shown in
Figure 5b, which represents the distance between tuples. In this table, each row represents a path length. Each element of the row shows the distance between two points as
, where i is the current state and j is the previous state. Furthermore, n is in the number of search agents. In addition, in the proposed methods, the path cost based on a fitness function that was presented in Equation (3) is calculated.
In the next step, the proposed methods calculate the distance between each tuple for each station in the pool. In this case, we have a distance cost (
d) between the current station and candidate next stations. The d includes two values: first is the distance between the current and next states, and the second is the distance between next and destination states. However, the metaheuristic algorithms find the best solution for the next station of each current station. If the distance of the possible next stations is smaller than the obtained value from metaheuristic algorithms (
w), the relevant station with the minimum value is selected as the elected next station. Otherwise, the UAV chooses the achieved solution of the metaheuristic algorithms as the next station (Algorithm 1). The proposed method’s aim is to reduce the cost of each path and try to find the optimal path with minimum cost for multi-UAVs. In this study, three UAVs were used that had dissimilar start (source) and final (destination) stations. The results obtained from this method are explained in the analysis and results section. The pseudocode and flowchart of the proposed path planning can be found in Algorithm 2 and
Figure 6.
Algorithm 2. Pseudocode of proposed path planning |
Initialize the grey wolf population Xi (i = 1, 2, ….., n) Initialize A, C and a //Equations(6)–(8) Initialize positions matrix and search space Calculate fitness of each agent //Equation (3) While (t < Max number of iterations) For each search agent Update the position of current search agent //Algorithm 1. End For Update a, A and C Calculate the fitness of all search agents Update position //Equation (11) or Equation (14) Update the search space matrix t = t +1 End While
|
3.4. Other Possible Features: Applicability in Farmlands
Based on the functionality of farmland, farmers can analyze the data to increase productivity before the agricultural year begins. Most farmers fertilize their farmland based on the experimental information. Modern agriculture tries to use the source efficiently and encourages farmers to use new technologies in cultivation to increase productivity along with their income. While data that have been collected are stored in a light server to serve the clients, peer-to-peer communication can be held between monitoring devices with the robots via the Global System for Mobile communications (GSM). In precision agriculture, farmers are able to increase productivity by using the previous data analysis. As the connections are bidirectional, exchanging urgent commands such as changing tasks, terminating current tasks, and more can be performed. A tiny unit of computers of robots provides a mid-layer infrastructure to receive commands and to respond to requests. Thanks to the proposed algorithms, the farmer is able to lunch a UAV with predetermined states to monitor and control their land. Farmers can track the whole of their agricultural land and their crops remotely, and they can also meet their needs such as irrigation and harvesting using the related autonomous UAV and agricultural robot robots on an optimal path and minimum costs. In addition, the proposed methods can be used to find optimal routes for multiple UAVs at the same time in parallel or concurrently. In this case, each UAV perceives the other UAV as an obstacle andso, the relevant UAV can continue its mission without colliding with our obstacle management mechanism. In addition, thanks to this mechanism, it will be possible for the proposed methods to work successfully in dynamic or uncertain environments.
4. Results and Discussion
This section presents the performance of the proposed methods, which is analyzed and compared with the GWO-based 3D path planning method [
20]. The authors used the Gray Wolf optimizer (GWO) algorithm to find an optimal path with minimal cost in 3D environments. According to the results of the study, in path planning, the GWO-based method is better than Dijkstra, A*, D*, and several famous metaheuristic-based methods. They proved that the GWO-based method presents a more balanced and better performance in similar problems. In addition, GWO-based algorithms are sought after in many research and application areas due to their balanced behavior amongst various metaheuristic algorithms [
19,
20]. Therefore, we selected this method for the comparison of results and performance. The implantation and analysis presentation was performed in Java and MATLAB. The algorithms proposed in this study were performed on a Core i7-5500 U 2.4 processor with 8GB of RAM.
4.1. Simulation Setting
In the simulation, large-scale, agricultural land was considered. The size of the environment was 1000 m ∗ 1000 m ∗ 1000 m. In addition, 150 obstacles were also placed in this area. Three UAVs with different start and endpoints were considered in each simulation of the used methods. The map boundaries, UAVs, and obstacle positions were assumed based on
Figure 3. Each used algorithm was run 15 times. Furthermore, simulation parameters are presented in
Table 1. The best, worst, and average costs (distance traveled in meters), execution times and complexity, and finally, convergence curve analysis for each UAV in each algorithm was applied by different population sizes and iteration numbers.
4.2. Analysis and Evaluation (Cost of Paths)
In this section, the proposed path planning methods are analyzed based on the cost function, introduced in Equation (3). The results obtained are presented in
Table 2. The starting and ending points of UAVs are assumed to be different from each other. In this table, the costs for these autonomous robots were obtained from a set of various populations and iterations. This process was evaluated for all algorithms used. According to the results obtained, the Ex-GWO-based method achieved the best result compared to other used path planning methods. The Ex-GWO-based method gave the best solution in five of the assumed nine scenarios and ranked first among the three methods with a 55.56% success rate. In the ranking, the I-GWO-based method was second with 38.88% and the GWO-based method was third with 5.56%. These results are presented in
Table 3.
Based on the obtained results, it is determined that the Ex-GWO-based method exhibits good performance in large-scale and complex farmland with a high number of obstacles. This is because the Ex-GWO-based method finds the best solution according to the alpha, beta, delta wolves, and whole pack. The wolves use the whole pack’s location knowledge to update their positions, so for experiments with larger population sizes and more iterations, the Ex-GWO-based method has a better chance of reaching the best solution. Therefore, the wolves in the pack minimize the escape paths of the hunt (prey), and hence, the prey can be caught faster. The fact that this mechanism can be better than other methods can be seen more clearly in large and crowded environments. On the other hand, another method proposed in this study, the I-GWO-based path planning method, outperforms good results in smaller and less populated environments. The basic update process in this method is very dependent on the alpha setup. Therefore, the speed of growth and the selection of the right places for the first wolf is of great importance. In this method, there is the possibility of finding problem solutions (prey) much faster in fewer iterations. For this reason, our proposed methods may be the most appropriate choices in various real-life application areas of mobile autonomous systems such as the use of UAVs for different and varied purposes and environments. In general, the GWO-based method has good performance in medium, small-sized, and not too crowded environments. In fact, the usage capacity of it may be between our two methods, but its success rate is not considered good according to the results. Briefly, the Ex-GWO-based path planning method may be performed more successfully in larger and crowded environments with larger populations and more iterations, and the I-GWO-based path planning method may give good results in medium and smaller, less populated environments.
Additionally, in
Figure 7, the movements of UAVs for each algorithm are also shown on the defined map based on the obtained simulation results in various population sizes and iterations. In this figure, the circles show the start state of each UAV, and the star symbols show the destination state of each UAV.
4.3. Analysis and Evaluation (Taken Times and Complexity)
The execution times of the proposed methods are also taken into consideration. The best execution time analyses for each method are presented in
Figure 8 for various population sizes and iterations. The GWO has the best overall-time performance, while Ex-GWO and I-GWO rank second and third, respectively. Indeed, the GWO-based path planning for three UAVs in the parallel periods consumes the minimum time to reach its destination. The reason for this may be due to the fact that it depends on the three first wolves. In the I-GWO, the incorrect position or the wrong movement of the alpha wolf can move the whole pack away from the target or cause them to catch the prey late. At the Ex-GWO, each pack member has more roles and contributions than the other two methods, which means that this algorithm may need a longer execution time. However, the Ex-GWO may not be the worst in terms of time, as seen with its better performance in crowded environments. The results show that in the crowded map scenarios with many barriers, the I-GWO-based method does not perform well with regard to the overall time and optimum path cost. The fact that the GWO had the best overall time does not mean that the other two methods were bad, because these two methods could be concluded in an acceptable time. In addition, the time complexity analysis of the proposed methods is O(n
2).
4.4. Analysis and Evaluation (Convergence Curve)
Figure 9 presents the convergence curve of each proposed path planning algorithm. As mentioned before, the number of obstacles and the boundary sizes of the map are listed in
Figure 3. The three metaheuristic algorithms used have different structures in the exploration and exploitation phases.
Figure 9 illustrates the convergence curve of each method with various iterations and population sizes. In the I-GWO algorithm, the transition from exploration to the exploitation phase is faster than the other two metaheuristic algorithms (GWO and Ex-GWO). As a result of the observations, it was concluded that 50 iterations were enough to analyze of convergence rate because the results achieved did not display remarkable differences [
20].
In
Figure 10, the statistical results of the path planning methods with the boxplot graph are presented. Boxplots are a standard method for displaying data distribution based on statistical indicators such as minimum, first quartile (Q1), median, third quartile (Q3), and maximum. This diagram also provides information regarding the existence of outlier data. In addition, the symmetry ratio in the data can be analyzed from this graph. The values were obtained from three metaheuristic algorithms with a population size of 30 and 50 iterations after 15 runs. The box plot graph analysis describes the maximum and minimum values of the obtained best cost and the frequency of the values. The
x-axis of each figure indicates the name of the respective algorithm, whereas the
y-axis indicates the average of best cost obtained. From
Figure 10, it can be observed that the results obtained using the Ex-GWO algorithm are near to the best solution, whilst the algorithm tries to find the best solution. As well as this, after initial iterations in the exploitation phase the Ex-GWO obtained results near to the best cost.
Figure 11 illustrates the distributions of costs in 15 runs. While the UAV
2 has an almost uniform distribution, both UAV
1 and UAV
3 have lower cost densities. Note that obstacles are employed as a marker for these metaheuristic algorithms, and paths with an appropriate number of obstacles help to improve the performances. For this purpose, a Student’s t-test was applied for all combinations of UAVs. The
p-value of UAV
1 and UAV
2 for all combinations of algorithms, population, and iterations count in 15 runs is 9.3 × 10
–12, the
p-value of UAV
1, and UAV
3 is 0.201, and the
p-value of UAV
2 and UAV
3 is 9.8 × 10
–6. Generally,
p-values less than 0.05 are accepted for hypothesis rejection. The null hypothesis is that all UAV s do not have a meaningful relationship. The significant difference between UAV
2 with both UAV
1 and UAV
3 is proved. Therefore, the null hypothesis is rejected.
5. Conclusions
The focus of the paper was to solve the NP-hard problem of efficient crop harvesting by finding the most suitable and optimal paths for UAVs. This study presented adaptive 3D path planning methods using metaheuristic algorithms (I-GWO and Ex-GWO) for autonomous agricultural UAVs. Therefore, in this study, maximum profit was achieved by consuming the least energy by harvesting the most crops in the shortest possible time. In addition, the use of resources such as human and natural resources was carried out efficiently by creating sustainable and smart agriculture. In other words, the method allows farmers to monitor crop variability and stress conditions continuously and harvest the best crops, resulting in efficient resource consumption and an increase in profits. The proposed methods tried to find the best solution in an acceptable time without falling into any local optima trap. The proposed method’s aim was to reduce the cost of each path and try to find the optimal path with the minimum cost for multi-UAVs. In addition, this study also proposed a mechanism for obstacle management. In this study, a large-scale farmland map with many various obstacles was considered. From the results, it can be concluded that in terms of the minimum execution time parameter, the GWO-based method did the best, whereas in finding the optimal path with the minimum cost, the Ex-GWO-based method was better. The proposed method based on the Ex-GWO attained a 55.56% success rate, the I-GWO, and the GWO-based method attained 38.88% and 5.56% success rates in optimal path costs, respectively. In addition, in the analysis of convergence curve behavior for metaheuristic algorithms, the proposed I-GWO-based method was observed to offer the best solution. Thanks to the algorithms proposed in this study, efficient resource consumption and product growth rate can be achieved with low risk and cost. They can also be used in real agricultural applications. In addition, the consideration and installation of specific mechanical and hardware devices to mobile robots and farmland can play an important role. In this regard, information about the mission environment can be gathered by other types of sensors (e.g., laser spot), which are mounted on mobile UAVs. These sensors can provide information about the shape, size, and location of an obstacle. Using sensory information, robots may advance towards a target without colliding with an obstacle or coming under enemy radars. On the other hand, various sensor devices are used to collect information on parameters such as humidity, temperature, etc. in agricultural land for different applications. Briefly, since the map information and the starting and destination points of each mobile device are certain, the developed method can be easily embedded on these devices. Thanks to the obstacle and object detection feature used of the method, the supposed parameters in the defined fitness function, and the station selection feature on the map, method can be applicable in the real world as well. It can also be even more useful when combined with special equipment used in farmland and mobile devices.
In future studies, we would like to explain our roadmap below with a focus on smart and sustainable agriculture. Alongside mobile robots, it will focus on a method that tracks and harvests crops in large-scale farmland with Internet of Vehicles (IoVs). In such a scenario, the mobile robots would only be tasked with monitoring the farmland. In this case, a blended mechanism with image processing methods will be presented. It will then use the results from these autonomous robots as an input matrix for the IoVs. Since a complex and NP-hard type of problem will arise here, metaheuristic-based algorithms will again come into play. In this regard, hybrid or new algorithms will be presented. On the other hand, the 3D path planning methods proposed in this study can be applied to IoT systems such as smart cities, industries, and agriculture in hybrid form with machine learning algorithms such as reinforcement-learning- or game-theory-based algorithms.