# Virtual Reality Technology of Multi UAVEarthquake Disaster Path Optimization

Authors: Yi Wang; Ensheng Liu
Journal: Mathematical Problems in Engineering
 (2021)

---

## Abstract

China is a country with frequent earthquake disasters. After the occurrence of earthquake disasters, the key to disaster monitoring and rescue is to quickly obtain images of postdisaster areas. Unmanned aerial vehicle (UAV) path planning is the core of multi-UAV cooperative control. With the increasing popularity of UAVs, people with more complex living environments contact with UAVs more frequently, which also poses a challenge to the overall control of UAVs, making single UAV and even multi-UAV cooperative path planning become a hot research issue in recent years. The complexity of communication between aircraft in three-dimensional flight space and multidegree of freedom navigation makes multi-UAV cooperation more challenging. According to the research results at home and abroad, this paper takes multitarget tracking algorithm, ant colony algorithm, and hybrid particle swarm optimization algorithm as research methods. Based on virtual reality technology, by comparing the advantages and disadvantages of several algorithms, the research model of path optimization is established, and a multitarget detection method based on virtual reality technology is established. Through the analysis and improvement of multitarget tracking algorithm, ant colony algorithm, and hybrid particle swarm optimization algorithm, the path optimization problem of UAV after an earthquake based on virtual reality technology is studied. The results show that, compared with the previous research models, the overall optimization efficiency of UAV route is improved by 15%, which is more practical.


---

## Body

## 1. Introduction

With the continuous development of artificial intelligence technology, unmanned aerial vehicle (UAV) autonomous flight technology has been widely concerned and studied by the academic community. Path planning is an important basic guarantee for UAVS to realize automatic flight. The main purpose of UAV path planning is to plan the path that meets the constraints of UAV according to the UAV target. The actual flight environment of UAV is high-dimensional space. If the time limit is considered, the scale will be increased to four dimensions, which will cause the problem of “scale disaster” in the planning process. Therefore, how to choose an appropriate algorithm to develop an optimal path planning system for multiple UAVs is the main problem in the field of UAV research.Path planning of three-dimensional UAV is an important part of UAV autonomous control system. To solve the complex path optimization problem of rotor vertical takeoff and landing (VTOL) aircraft, Chen proposed an improved center force optimization method. Chen used the linear difference equation method to analyze the convergence of the whole modified central force optimization (MCFO) method [1]. Taking a six-degree-of-freedom four-rotor helicopter control system as an example, Chen proposed a new path planning method. Finally, the six algorithms of Chen were compared and simulated, and the problem of angle of arrival tracking using multiple UAVs in three-dimensional space was studied [2]. Xu proposed a distributed 3D AOA target tracking method which is composed of a distributed estimator and multi-UAV path optimization algorithm and a new 3D distributed pseudolinear Kalman filter (DPLKF) to improve the stability of the solution of the extended Kalman filter. DPLKF consists of two coupled filters; Dogancay proposed a method to reduce the deviation and a distributed path optimization algorithm constrained by communication distance and no-fly zone [3]. The algorithm uses gradient descent optimization in XY plane and grid search along z-axis to calculate UAV waypoints. To improve the tracking performance, the tracking error covariance matrix is minimized [4]. UAV relay technology is an important means to realize remote wireless communication. Li proposed a broadcast communication system based on fixed-wing UAV as the relay platform between the base station and mobile mission UAV cluster. To improve the system performance, especially to ensure the quality of service (QoS) of UAV in high priority mission, the route optimization method of relay UAV is investigated. Firstly, the system model and signal model of UAV relay broadcasting communication system are given, and the approximate expression of single link traversal capacity is derived. Then, according to the different requirements of mission UAV, Li proposed a path optimization method for relay UAV based on the weighted and ergodic capacity maximization criteria [5]. On this basis, the exact outage probability and closed-form ergodic capacity of relay links are derived to quantify the system performance. Finally, the optimal path of the relay UAV in the two simulation scenarios is given.Recently, UAV is considered as a means to provide enhanced coverage or relay services for mobile users in wireless systems with limited or no infrastructure [6]. Jeong has studied a mobile cloud computing system based on UAV [7]. The system gives computing power to the mobile UAV and provides computing offloading opportunities for system with limited local processing capacity. Liu proposed a particle swarm optimization method for UAV path planning based on the potential odor intensity grid. The odor intensity is created to color the highest probability region where candidate particles may be located in the search space [8]. Based on different odor intensity levels, Duan designed a potential grid building operator. Potential grid construction operators generate two potential location grids with the highest odor intensity [9]. Then the intermediate point is regarded as the final position in the current particle dimension, and the global optimal solution is solved as the average value.In this paper, based on the research results at home and abroad, multitarget tracking algorithm, ant colony algorithm, and hybrid particle swarm optimization algorithm are used as research methods. Based on virtual reality technology, the research model is established. Through the comparative analysis of several algorithms, the experimental simulation and system research on the UAV path optimization problem are carried out. The main innovations of this paper are as follows: The threat distribution model of various obstacles is designed, and the concept of comprehensive threat field of virtual reality model is defined. This paper extends the traditional method of single threat source and improves some threat distribution models. The smoothing algorithm is used to smooth the initial path, and based on the characteristics of smooth trajectory, a cooperative criterion based on interval distance is proposed, which defines when UAVs can cooperate with each other and how UAVs will cooperate. As the core content of this paper, based on the above points, a set of “environment modeling, initial path planning, and path smoothing collaborative planning” process is established, and the simulation experiment is implemented.


## 2. Path Optimization Method for Multiple UAVs under Earthquake Disaster

### 2.1. Optimization of Earthquake Disaster Path

Path planning is carried out in the configuration space. Firstly, the concept of configuration space is introduced. Path planning problems usually need to know four parts in advance.(1)
The geometric description of UAV.(2)
The description of the planning environment of UAV which is also called the workspace.(3)
The degree-of-freedom description of UAV.(4)
Planning task, through which the configuration space (C space) of UAV can be compiled. These three configuration spaces can be described by several parameters. For example, the configuration space of two UAVs moving in 3D space can be described by three parametersx, y, and z. N-dimensional vector can be used to describe the minimum number of parameters required to describe the configuration space and porthole position of UAVS [10, 11].The vector set corresponding to all positions of UAV is the configuration space of UAV. Due to the limitation of various conditions, the area that UAV cannot reach (such as the area where the obstacles are located) is weighed as the forbidden configuration inC space, and its set is represented by Cx, the remaining area that UAV can reach is called the free configuration, and its set is represented by CR. There is no intersection between available configurations and available configurations, and the two sets together constitute the configuration space; that is, C = CX∩CR = 0.Path planning is one of the key technologies of multi-UAV formation flight, which mainly includes formation assembly route plan, formation maintenance route plan, and formation reconstruction route plan [12,13]. Among them, formation assembly path planning is the process of formation generation of multiple UAVs. The purpose is to make a group of UAVs start from different starting points and reach the corresponding positions in the designated formation assembly area at the same time, to achieve the required formation. Formation maintenance path planning is to plan an optimal reference path, which can safely reach the target destination of UAV formation flying in a specific formation; the formation reconfiguration plan is to plan a set of optimal paths for each UAV, so that each UAV can quickly transfer from the current formation to the new formation. The main constraints considered in the planning process are safety, flight capability, and other constraints: path safety means that the planned path can avoid the threat of obstacles in the flight area and environment, and there will be no collisions between aircraft. Path flightability means that the planned path can meet the kinematic constraints of each UAV, such as the minimum turning radius constraint, path curvature continuity constraint, and maximum climbing angle constraint [14,15]. Other constraints include time coordination constraint and maximum path length constraint.


### 2.2. Ant Colony Algorithm

The flight altitude of UAV is constant in cruising time because frequent altitude changes will consume a lot of fuel and increase engine loss. In the process of flight, the aircraft should not be too high or too low [16,17]. The too high flight will increase the risk of being detected by the enemy’s radar and be attacked by the enemy’s defense system. To avoid the attack of local defense systems and radar, flying too low will lead to bumping into natural obstacles such as mountains. Considering the above concerns, the aircraft needs to keep the altitude within a certain range during the flight process, and the UAV needs to keep the flight altitude no higher than Hmax and no less than Hmin when performing tasks. The formal language description is as follows, where IH represents the real-time flight altitude of each trajectory.(1)Tij0=const,where Tij0 is the pheromone concentration between city I and city J at the initial time; const is a constant, usually 0. When the classical ant colony algorithm is not running, the pheromone concentration on all paths is 0.To avoid falling into local optimization easily in the process of solving the optimization problem, domestic scholars proposed an improved ant colony algorithm based on information entropy in 2005 [18]. The information entropy is used to determine the next transfer city, and random disturbance is introduced to realize the dynamic adjustment of the algorithm. When the value of information entropy rises to the maximum threshold set by the algorithm, the execution of the algorithm ends. According to the value of information entropy, the next step transition probability is obtained. The calculation formula of information entropy and pheromone updating is as follows:(2)Pi=τit∑iesτit,(3)S=−ki∑i=1npilnpi,(4)τt=1−ρτt−1+ρΔτt,where Pi is the ratio of the amount of information on the edge i of the path to the total information; that is, Pi≥0. In the initial stage of the algorithm, the pheromone of each path is the same, and the entropy is the global maximum [19, 20]. After a period of time, the information concentration of some path edges increases, and the entropy becomes smaller and smaller. If we do not pay attention to the adjustment, the entropy may drop to 0; that is to say, in the later stage of the algorithm, only one path has the highest pheromone, which will lead to premature.


### 2.3. Hybrid Particle Swarm Optimization Algorithm

The intelligent optimization method is a model that uses biological characteristics to solve practical problems, and the PSO algorithm is a typical swarm intelligence random search optimization algorithm [21]. In PSO, the search space of the problem is similar to the flight space of a group of birds, and each bird is one of them. Each bird is reduced to particles of negligible weight and size, representing a viable solution to the problem. The optimization process of the algorithm is similar to the process of species searching for food and finally finding the most abundant food, which is equivalent to obtaining the optimal solution of a complex optimization problem [22, 23].The selection mechanism of the hybrid PSO algorithm is similar to that of the genetic algorithm [24]. In the hybrid PSO algorithm, we design the fitness degree of each individual’s current position and rank it. Then we replace the position and speed of the weak adaptive individual with the position and speed of the weak adaptive individual and keep the best position of each individual. Therefore, the scope of group search is concentrated in relatively good areas, but it is still affected by the best location of previous individuals. Each particle in the particle swarm is set manually to the propagation probability [25]. In each iteration, a certain number of particles are randomly selected from the population according to the propagation probability to generate the same number of new particles [26, 27]. To keep the number of particles in the population constant, we use the generated new particles to completely replace the original particles. The new particle’s position and velocity update formula is as follows:(5)Xchild−i=μxit+1−μXjt,(6)vchild−j=vit+vjt⋅vitvit+vjt,where μ [0,1], Xi, and Xj are the position vectors of the random particles Vchild−iand Vchild−j and are the corresponding velocity vectors of each primitive particle Vchild−i, and Vchild−j and Vchild−jare new particles. The hybrid particle swarm optimization algorithm has higher search accuracy and faster speed. It can achieve satisfactory results in solving a class of nonlinear optimization problems. The flight speed of particles has a great impact on the performance of the algorithm [26]. When the flight speed is too fast, particles can quickly reach the target area, but when approaching the target value, too fast speed will easily cause the particles to cross the target value and fly to other regions, thus reducing the convergence speed of particles [27]. The algorithm cannot even converge; when the particle flying speed is too slow, the particle can quickly approach the target area, although it can ensure a local fine search, it directly leads to the reduction of the overall detection ability of the algorithm. Therefore, to balance the overall development ability of the algorithm, measures must be taken to control the flight speed of particles [28].


## 2.1. Optimization of Earthquake Disaster Path

Path planning is carried out in the configuration space. Firstly, the concept of configuration space is introduced. Path planning problems usually need to know four parts in advance.(1)
The geometric description of UAV.(2)
The description of the planning environment of UAV which is also called the workspace.(3)
The degree-of-freedom description of UAV.(4)
Planning task, through which the configuration space (C space) of UAV can be compiled. These three configuration spaces can be described by several parameters. For example, the configuration space of two UAVs moving in 3D space can be described by three parametersx, y, and z. N-dimensional vector can be used to describe the minimum number of parameters required to describe the configuration space and porthole position of UAVS [10, 11].The vector set corresponding to all positions of UAV is the configuration space of UAV. Due to the limitation of various conditions, the area that UAV cannot reach (such as the area where the obstacles are located) is weighed as the forbidden configuration inC space, and its set is represented by Cx, the remaining area that UAV can reach is called the free configuration, and its set is represented by CR. There is no intersection between available configurations and available configurations, and the two sets together constitute the configuration space; that is, C = CX∩CR = 0.Path planning is one of the key technologies of multi-UAV formation flight, which mainly includes formation assembly route plan, formation maintenance route plan, and formation reconstruction route plan [12,13]. Among them, formation assembly path planning is the process of formation generation of multiple UAVs. The purpose is to make a group of UAVs start from different starting points and reach the corresponding positions in the designated formation assembly area at the same time, to achieve the required formation. Formation maintenance path planning is to plan an optimal reference path, which can safely reach the target destination of UAV formation flying in a specific formation; the formation reconfiguration plan is to plan a set of optimal paths for each UAV, so that each UAV can quickly transfer from the current formation to the new formation. The main constraints considered in the planning process are safety, flight capability, and other constraints: path safety means that the planned path can avoid the threat of obstacles in the flight area and environment, and there will be no collisions between aircraft. Path flightability means that the planned path can meet the kinematic constraints of each UAV, such as the minimum turning radius constraint, path curvature continuity constraint, and maximum climbing angle constraint [14,15]. Other constraints include time coordination constraint and maximum path length constraint.


## 2.2. Ant Colony Algorithm

The flight altitude of UAV is constant in cruising time because frequent altitude changes will consume a lot of fuel and increase engine loss. In the process of flight, the aircraft should not be too high or too low [16,17]. The too high flight will increase the risk of being detected by the enemy’s radar and be attacked by the enemy’s defense system. To avoid the attack of local defense systems and radar, flying too low will lead to bumping into natural obstacles such as mountains. Considering the above concerns, the aircraft needs to keep the altitude within a certain range during the flight process, and the UAV needs to keep the flight altitude no higher than Hmax and no less than Hmin when performing tasks. The formal language description is as follows, where IH represents the real-time flight altitude of each trajectory.(1)Tij0=const,where Tij0 is the pheromone concentration between city I and city J at the initial time; const is a constant, usually 0. When the classical ant colony algorithm is not running, the pheromone concentration on all paths is 0.To avoid falling into local optimization easily in the process of solving the optimization problem, domestic scholars proposed an improved ant colony algorithm based on information entropy in 2005 [18]. The information entropy is used to determine the next transfer city, and random disturbance is introduced to realize the dynamic adjustment of the algorithm. When the value of information entropy rises to the maximum threshold set by the algorithm, the execution of the algorithm ends. According to the value of information entropy, the next step transition probability is obtained. The calculation formula of information entropy and pheromone updating is as follows:(2)Pi=τit∑iesτit,(3)S=−ki∑i=1npilnpi,(4)τt=1−ρτt−1+ρΔτt,where Pi is the ratio of the amount of information on the edge i of the path to the total information; that is, Pi≥0. In the initial stage of the algorithm, the pheromone of each path is the same, and the entropy is the global maximum [19, 20]. After a period of time, the information concentration of some path edges increases, and the entropy becomes smaller and smaller. If we do not pay attention to the adjustment, the entropy may drop to 0; that is to say, in the later stage of the algorithm, only one path has the highest pheromone, which will lead to premature.


## 2.3. Hybrid Particle Swarm Optimization Algorithm

The intelligent optimization method is a model that uses biological characteristics to solve practical problems, and the PSO algorithm is a typical swarm intelligence random search optimization algorithm [21]. In PSO, the search space of the problem is similar to the flight space of a group of birds, and each bird is one of them. Each bird is reduced to particles of negligible weight and size, representing a viable solution to the problem. The optimization process of the algorithm is similar to the process of species searching for food and finally finding the most abundant food, which is equivalent to obtaining the optimal solution of a complex optimization problem [22, 23].The selection mechanism of the hybrid PSO algorithm is similar to that of the genetic algorithm [24]. In the hybrid PSO algorithm, we design the fitness degree of each individual’s current position and rank it. Then we replace the position and speed of the weak adaptive individual with the position and speed of the weak adaptive individual and keep the best position of each individual. Therefore, the scope of group search is concentrated in relatively good areas, but it is still affected by the best location of previous individuals. Each particle in the particle swarm is set manually to the propagation probability [25]. In each iteration, a certain number of particles are randomly selected from the population according to the propagation probability to generate the same number of new particles [26, 27]. To keep the number of particles in the population constant, we use the generated new particles to completely replace the original particles. The new particle’s position and velocity update formula is as follows:(5)Xchild−i=μxit+1−μXjt,(6)vchild−j=vit+vjt⋅vitvit+vjt,where μ [0,1], Xi, and Xj are the position vectors of the random particles Vchild−iand Vchild−j and are the corresponding velocity vectors of each primitive particle Vchild−i, and Vchild−j and Vchild−jare new particles. The hybrid particle swarm optimization algorithm has higher search accuracy and faster speed. It can achieve satisfactory results in solving a class of nonlinear optimization problems. The flight speed of particles has a great impact on the performance of the algorithm [26]. When the flight speed is too fast, particles can quickly reach the target area, but when approaching the target value, too fast speed will easily cause the particles to cross the target value and fly to other regions, thus reducing the convergence speed of particles [27]. The algorithm cannot even converge; when the particle flying speed is too slow, the particle can quickly approach the target area, although it can ensure a local fine search, it directly leads to the reduction of the overall detection ability of the algorithm. Therefore, to balance the overall development ability of the algorithm, measures must be taken to control the flight speed of particles [28].


## 3. Simulation Research and Design of Multi-UAV Path Optimization for Rapid Assessment after Earthquake Disaster

Global path planning is the first step to realize collision avoidance in the autonomous path planning system, and it is the basis of subsequent path planning (such as local path planning and renavigation). According to the research results of some scholars and the general steps of global path planning, the global path planning of UAV is realized: firstly, the autonomous navigation environment model of UAV is established [2], then the initial path planning is carried out by using Dijkstra algorithm, and finally, the initial path is optimized by hybrid particle swarm optimization algorithm, to find the global shortest path.(1)
Environment modeling: according to the distribution and size of obstacles in the electronic chart, the corresponding convex surface is defined, and the autonomous navigation environment model of the unmanned ship is established by link graph method and network topology diagram (link graph) [29].(2)
Initial path planning: use the Dijkstra algorithm to search the link graph from the starting point to the destination, and prepare the initial optimal path by the punctuation method, which is used for the path optimization of hybrid particle swarm optimization algorithm [30].(3)
Path optimization: based on the initial path, the hybrid particle swarm optimization algorithm is used to optimize the path to find the best path.Different from the ant colony algorithm, when using a hybrid particle swarm optimization algorithm, the priority of each target is fixed, which makes the system more predictable. Therefore, it is more appropriate to use the hybrid particle swarm optimization algorithm to design the target priority of the simulation system. However, the result of target priority division is not absolute, so it cannot be designed according to the hybrid particle swarm optimization algorithm in practical application [31], because the hybrid particle swarm optimization algorithm is based on a series of assumptions, and all the targets in the target set must be periodic objectives.(1)
The setting time and period of each goal are equal(2)
Each target is independent of each other, and the operation of each target is not associated with other targets(3)
The use time of the target remains unchanged in each cycle(4)
Ignore the target plan and target replacement timeModel the third-party software, set the scene material, so that the model can get the real mapping, and finally initialize the virtual reality system [32]. To model the real scene, first of all, we must accurately collect the real building data, measure the actual length, width, and height with a ruler, and then take photos of the real objects. After obtaining the front data, the scene is modeled with 3ds Max referring to digital photos. To ensure fidelity, the material and texture of the scene are essential. Our goal is to refer to the real model for material setting and mapping, to restore its authenticity as much as possible and make the performance as close to reality as possible.


## 4. Simulation Results Analysis of Multi-UAV Path Optimization Based on Virtual Reality for Rapid Evaluation after Earthquake Disaster

### 4.1. Simulation Analysis of Multi-UAV Path Optimization in Virtual Environment

As shown in Table1, in addition to the obstacles with known motion law and unknown motion law, there is also a kind of obstacle whose motion information is partially known; that is, the movement trend of the obstacle is known, but the actual position cannot be determined. These obstacles can be divided into two categories: one is the obstacle with a known trajectory and moving speed within a certain range [33]; the other is obstacles. The speed of the object is known, but the other direction is unknown. To verify the superiority of the improved hybrid particle swarm optimization algorithm, ant colony algorithm and improved hybrid particle swarm optimization algorithm with artificial potential field are used to simulate and compare the path planning environment of UAV, and the number of sampling points is less. In the simulation, 400 sampling points are used, and four kinds of obstacles are set up in the environment, and the risk degree of obstacles ranges from 1 to 10.Table 1
Obstacle distribution information.
Obstacle sourceObstacle 1Obstacle 2Obstacle 3Obstacle 4Central coordinates164,247133,165246,214184,269Radius50396842Risk level6485It can be seen from the table that the larger the radius of the obstacle, the higher the risk score. When the radius of obstacle 3 is 68, the risk degree score is the highest, reaching 8 points, which shows that the theoretical accuracy of the target location can be significantly improved by using multipath information effectively. The fact that the detection probability is 1 is considered in the theoretical calculation. The detection probability of the outbound base station equivalent to the multipath effect is less than 1. As the observation angle increases, the observation and measurement information will increase, and the accuracy of target estimation will be improved.As shown in Figure1, in order to verify the effectiveness of the proposed hybrid particle swarm optimization algorithm in probability graphs with different scales, the environment in the graph is simulated with 100, 200, 300, and 400 sampling points. From the comparison of iterative curves, it can be seen that the algorithm can quickly converge to the final result in the case of different sampling points. With the increase of the number of sampling points, the planning environment described by the probability graph becomes more and more detailed, and the shortest path length calculated is becoming shorter and shorter.Figure 1
The iterative curve of path length with a different number of sampling points.In a word, the update of particle speed takes into account the current velocity and the particle’s own optimization. The position and optimal position of the particle swarm optimization algorithm are calculated by the weighted sum of the three. Therefore, the method is an efficient algorithm with strong global search ability and fast convergence speed in practical application. In addition, this algorithm is also a kind of intelligent algorithm with good generality, which can be widely used in a variety of optimization. At the same time, it has good scalability and is easy to combine with other traditional and intelligent algorithms. The advantages of this algorithm complement each other, providing more ideas and methods for solving practical problems.As shown in Figure2, the verification of the periodic task scheduling strategy can be carried out by calculating the running frequency of each periodic task. If every periodic task can run periodically according to the set frequency, the design of the periodic task scheduling strategy is correct. In the simulation example, the operation frequency of each cycle task is calculated in a specific cycle second from the start of UAV taxiing to takeoff [34]. From the verification results, the running frequency of periodic tasks is consistent with the set frequency, and the scheduling strategy of periodic tasks meets the system requirements.Figure 2
System target operating frequency test results.


### 4.2. Schedulability Analysis of UAV Path Planning in Virtual Environment

From the path of hybrid particle swarm optimization, it can be seen that the path planned by the algorithm is close to the boundary of obstacles or passes through the vertices of obstacles to meet the requirements of the shortest path. Although the obstacle model has been extended to a certain extent, due to the interference of wind, wave, and currents in the actual navigation process, UAVs may collide with obstacles when navigating according to the global path, resulting in damage or mission failure of UAVs. Through the analysis of the schedulability verification method of simulation research, it can be seen that the maximum schedulability utilization calculation method of the hybrid particle swarm optimization algorithm requires that the task set is periodic [35].As shown in Figure3, to verify the effectiveness of the algorithm under different sampling time, four obstacle environments are used to simulate the path search under different sampling times. Finally, compare the different results between the primary and secondary plans based on the time of the obstacle. The comparison of the four groups of data in the figure shows that the time required for the second plan is less than that for the first plan when the number of sampling points is different, and the calculation time increases with the increase of sampling time. The growth of the primary plan is faster than that of the secondary plan. This is due to the expansion of all feasible locations in the planning environment in the initial plan. With the increase of sampling time, the number of path points in the search tree increases, and the collision detection time increases rapidly. However, due to the introduction of a sorting mechanism in the secondary plan, the number of conflict detections will not increase significantly with the increase of the number of nodes [36].Figure 3
Planning cycle comparison results.


### 4.3. UAV Planning Path Assessment in Virtual Environment

For different sampling times, the path length obtained by the algorithm is compared. Because the UAV flies at a constant speed, the arrival time of the target point can directly reflect the information of the path length.The results of multiple evaluations under the same conditions are different. The first assessment of Route 2 failed and the others succeeded. However, for the second assessment, the assessment of Route 1 and Route 3 failed and the assessment of other routes was successful. The two assessments provide a correct assessment of each route. However, for the second assessment, there was no significant difference in the output values corresponding to “medium” and “good” in Route 1 assessment results. According to the principle that the evaluation result is the maximum weight, the evaluation grade of Route 3 is “poor,” while the actual route level is good, so the evaluation result is wrong. Routes 1 and 2 have similar disadvantages. In the first assessment, due to the above reasons, there will be assessment errors.Figure4 shows the simulation results of path arrival time obtained by primary planning and secondary planning. The results show that, with the increase of sampling time, the arrival time becomes smaller and shorter, which indicates that the path length is shorter and shorter.Figure 4
Trend of sampling path length change.When the number of samples increases to 400, the change trend of path length becomes slower. This is because the system has completed most of the planned space coverage, and the search results are close to the best path. The arrival time of the initial plan is shorter than that of the secondary plan. This is because, after a sudden threat, the path obtained through the initial plan is no longer feasible. To avoid a sudden threat, it needs to take a longer path, and the arrival time increases accordingly. The simulation results show that although there are some differences between the two evaluation results, both of them have made a correct evaluation of the advantages and disadvantages of the route. The figure shows that the output value of the path level is significantly higher than that of other levels, and the path level can be judged intuitively and accurately from the diagram. The experimental results show that firstly, the initial weight and threshold are optimized, and then the accuracy of routing evaluation can be significantly improved by network training, which is an effective evaluation method. The fuzzy comprehensive evaluation method is based on the experience of relevant experts to comprehensively evaluate the route [37], judge the advantages and disadvantages of the route, carry out a quantitative evaluation on the route, give the corresponding score, and make a more detailed evaluation. If it is necessary to make a fine evaluation on the route evaluation of the BP neural network, the samples and evaluation values of the fuzzy comprehensive evaluation method can be used as the training samples of the BP neural network trained by the network, and the score value can be used as the expected output of training samples. Not only can the test samples be used to test the advantages and disadvantages of the route, but also the route score can obtain volume value.


## 4.1. Simulation Analysis of Multi-UAV Path Optimization in Virtual Environment

As shown in Table1, in addition to the obstacles with known motion law and unknown motion law, there is also a kind of obstacle whose motion information is partially known; that is, the movement trend of the obstacle is known, but the actual position cannot be determined. These obstacles can be divided into two categories: one is the obstacle with a known trajectory and moving speed within a certain range [33]; the other is obstacles. The speed of the object is known, but the other direction is unknown. To verify the superiority of the improved hybrid particle swarm optimization algorithm, ant colony algorithm and improved hybrid particle swarm optimization algorithm with artificial potential field are used to simulate and compare the path planning environment of UAV, and the number of sampling points is less. In the simulation, 400 sampling points are used, and four kinds of obstacles are set up in the environment, and the risk degree of obstacles ranges from 1 to 10.Table 1
Obstacle distribution information.
Obstacle sourceObstacle 1Obstacle 2Obstacle 3Obstacle 4Central coordinates164,247133,165246,214184,269Radius50396842Risk level6485It can be seen from the table that the larger the radius of the obstacle, the higher the risk score. When the radius of obstacle 3 is 68, the risk degree score is the highest, reaching 8 points, which shows that the theoretical accuracy of the target location can be significantly improved by using multipath information effectively. The fact that the detection probability is 1 is considered in the theoretical calculation. The detection probability of the outbound base station equivalent to the multipath effect is less than 1. As the observation angle increases, the observation and measurement information will increase, and the accuracy of target estimation will be improved.As shown in Figure1, in order to verify the effectiveness of the proposed hybrid particle swarm optimization algorithm in probability graphs with different scales, the environment in the graph is simulated with 100, 200, 300, and 400 sampling points. From the comparison of iterative curves, it can be seen that the algorithm can quickly converge to the final result in the case of different sampling points. With the increase of the number of sampling points, the planning environment described by the probability graph becomes more and more detailed, and the shortest path length calculated is becoming shorter and shorter.Figure 1
The iterative curve of path length with a different number of sampling points.In a word, the update of particle speed takes into account the current velocity and the particle’s own optimization. The position and optimal position of the particle swarm optimization algorithm are calculated by the weighted sum of the three. Therefore, the method is an efficient algorithm with strong global search ability and fast convergence speed in practical application. In addition, this algorithm is also a kind of intelligent algorithm with good generality, which can be widely used in a variety of optimization. At the same time, it has good scalability and is easy to combine with other traditional and intelligent algorithms. The advantages of this algorithm complement each other, providing more ideas and methods for solving practical problems.As shown in Figure2, the verification of the periodic task scheduling strategy can be carried out by calculating the running frequency of each periodic task. If every periodic task can run periodically according to the set frequency, the design of the periodic task scheduling strategy is correct. In the simulation example, the operation frequency of each cycle task is calculated in a specific cycle second from the start of UAV taxiing to takeoff [34]. From the verification results, the running frequency of periodic tasks is consistent with the set frequency, and the scheduling strategy of periodic tasks meets the system requirements.Figure 2
System target operating frequency test results.


## 4.2. Schedulability Analysis of UAV Path Planning in Virtual Environment

From the path of hybrid particle swarm optimization, it can be seen that the path planned by the algorithm is close to the boundary of obstacles or passes through the vertices of obstacles to meet the requirements of the shortest path. Although the obstacle model has been extended to a certain extent, due to the interference of wind, wave, and currents in the actual navigation process, UAVs may collide with obstacles when navigating according to the global path, resulting in damage or mission failure of UAVs. Through the analysis of the schedulability verification method of simulation research, it can be seen that the maximum schedulability utilization calculation method of the hybrid particle swarm optimization algorithm requires that the task set is periodic [35].As shown in Figure3, to verify the effectiveness of the algorithm under different sampling time, four obstacle environments are used to simulate the path search under different sampling times. Finally, compare the different results between the primary and secondary plans based on the time of the obstacle. The comparison of the four groups of data in the figure shows that the time required for the second plan is less than that for the first plan when the number of sampling points is different, and the calculation time increases with the increase of sampling time. The growth of the primary plan is faster than that of the secondary plan. This is due to the expansion of all feasible locations in the planning environment in the initial plan. With the increase of sampling time, the number of path points in the search tree increases, and the collision detection time increases rapidly. However, due to the introduction of a sorting mechanism in the secondary plan, the number of conflict detections will not increase significantly with the increase of the number of nodes [36].Figure 3
Planning cycle comparison results.


## 4.3. UAV Planning Path Assessment in Virtual Environment

For different sampling times, the path length obtained by the algorithm is compared. Because the UAV flies at a constant speed, the arrival time of the target point can directly reflect the information of the path length.The results of multiple evaluations under the same conditions are different. The first assessment of Route 2 failed and the others succeeded. However, for the second assessment, the assessment of Route 1 and Route 3 failed and the assessment of other routes was successful. The two assessments provide a correct assessment of each route. However, for the second assessment, there was no significant difference in the output values corresponding to “medium” and “good” in Route 1 assessment results. According to the principle that the evaluation result is the maximum weight, the evaluation grade of Route 3 is “poor,” while the actual route level is good, so the evaluation result is wrong. Routes 1 and 2 have similar disadvantages. In the first assessment, due to the above reasons, there will be assessment errors.Figure4 shows the simulation results of path arrival time obtained by primary planning and secondary planning. The results show that, with the increase of sampling time, the arrival time becomes smaller and shorter, which indicates that the path length is shorter and shorter.Figure 4
Trend of sampling path length change.When the number of samples increases to 400, the change trend of path length becomes slower. This is because the system has completed most of the planned space coverage, and the search results are close to the best path. The arrival time of the initial plan is shorter than that of the secondary plan. This is because, after a sudden threat, the path obtained through the initial plan is no longer feasible. To avoid a sudden threat, it needs to take a longer path, and the arrival time increases accordingly. The simulation results show that although there are some differences between the two evaluation results, both of them have made a correct evaluation of the advantages and disadvantages of the route. The figure shows that the output value of the path level is significantly higher than that of other levels, and the path level can be judged intuitively and accurately from the diagram. The experimental results show that firstly, the initial weight and threshold are optimized, and then the accuracy of routing evaluation can be significantly improved by network training, which is an effective evaluation method. The fuzzy comprehensive evaluation method is based on the experience of relevant experts to comprehensively evaluate the route [37], judge the advantages and disadvantages of the route, carry out a quantitative evaluation on the route, give the corresponding score, and make a more detailed evaluation. If it is necessary to make a fine evaluation on the route evaluation of the BP neural network, the samples and evaluation values of the fuzzy comprehensive evaluation method can be used as the training samples of the BP neural network trained by the network, and the score value can be used as the expected output of training samples. Not only can the test samples be used to test the advantages and disadvantages of the route, but also the route score can obtain volume value.


## 5. Conclusion

In geological disaster emergency response, UAV image acquisition provides a scientific and reasonable decision-making basis for rescue and disaster assessment. The efficiency and effect of unmanned path planning directly affect the whole emergency process. Autonomous optimal path planning is one of the main functions of UAV, which reflects the intelligence level of UAV. It mainly includes overall path planning and partial path planning. Among them, the overall path planning is the basic guarantee for the normal and safe driving of UAV and is also the basis of partial path planning, which has a certain value for the research of UAV optimal path planning. However, at present, most of the global optimal path planning algorithms at home and abroad are easy to fall into the local optimal solution and the convergence speed is slow, and most of them only consider the single target of the shortest path, which leads to the problem that the corner is too large and the distance is too close, and the actual navigation of obstacles and unmanned aircraft is inconsistent.To solve these problems, based on the research results at home and abroad, this paper takes multitarget tracking algorithm, ant colony algorithm, and hybrid particle swarm optimization algorithm as research methods; based on virtual reality technology, through the comparative analysis of the advantages and disadvantages of several algorithms, the research model of path optimization method for multiple UAVs based on virtual reality technology is established. The path optimization method of UAV after an earthquake based on virtual reality technology is studied. The results show that the hybrid particle swarm optimization algorithm based on virtual reality technology is necessary and advanced for UAV global path planning. This paper analyzes the basic particle swarm optimization algorithm and hybrid particle swarm optimization algorithm based on virtual reality technology. This paper introduces the formal definition of UAV path planning and the kinematic constraints of various UAVs. It focuses on several commonly used path planning algorithms in this field, some of which focus on environmental modeling, and some focus on path search. Generally speaking, intelligent methods are the future trend.At present, the development of multi-UAV cooperative path planning is still a hot issue. Due to the author’s limited experience, the research on some problems in this paper is not comprehensive enough. In the future, UAV collaborative planning, UAV task allocation, and path planning can be combined, and the communication between UAVs can be considered. The individual to master intelligence makes the whole system more flexible.



---

*Source: 5525560-2021-03-10.xml*