Route planning method based on threat field model
Improved the A* algorithm through raster method and threat field model, and solves the problem that traditional methods are difficult to avoid threats in complex battlefield environments, and improves the security and accuracy of path planning, which is suitable for autonomous navigation of unmanned systems.
Patent Information
- Application Number
- CN202510346362.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-24
- Publication Date
- 2025-07-04
AI Technical Summary
Traditional path planning methods are difficult to effectively avoid dynamic threats and find the optimal path in complex battlefield environments. The existing environmental modeling methods lack dynamic assessment and integration of threat factors.
The raster method is combined with the A* algorithm to build a threat field model, and the threat field model is constructed by calculating the firepower density and distance coefficient, and threat values are introduced into the A* algorithm to form an improved A* algorithm based on the threat field model and perform path planning.
It realizes dynamic evaluation of threat factors in complex environments, improves the security and accuracy of path planning, and is suitable for autonomous navigation of unmanned systems.
Smart Images
Figure CN120255534A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of path planning, and particularly to a route planning method based on a threat field model. Background Art
[0002] In modern warfare, the role played by unmanned aerial vehicles (UAVs) on the battlefield has become increasingly prominent. The tasks they undertake are becoming more and more complex, posing higher requirements for the control systems of UAVs. In complex environments such as the tight air defense areas of the enemy, such as damage interception and electromagnetic interference, the survival ability and combat effectiveness of traditional UAVs are greatly reduced. Therefore, various countries have begun to research new UAV penetration technologies, and reasonable route planning is a key link among them.
[0003] At present, many studies have been conducted on route planning algorithms at home and abroad. According to whether the algorithm conducts path search in a known environment, they can be divided into two categories: global path planning algorithms and local path planning algorithms. Common global path planning algorithms include the A* algorithm, Dijkstra algorithm, etc., and local path planning algorithms include the dynamic window method, artificial potential field method, etc.
[0004] Zhao Wenbin et al. proposed an innovative PSO-GWO path planning algorithm for static environments. This algorithm uses the particle swarm optimization algorithm (PSO) to calculate the initial population values to quickly obtain the initial path solution, and at the same time introduces a stagnation perturbation strategy to effectively avoid the situation where the algorithm converges prematurely to the local optimum. Wang Dianzheng et al. proposed an improved v-BI-RRT algorithm. By combining a collision detection mechanism, it can judge the obstacles in the path in real time and plan a collision-free path. And through path smoothing processing, the redundancy and twists in the path are reduced, providing an efficient obstacle avoidance path planning scheme for the rock drilling manipulator. Lan Xinghui et al. addressed the local path planning problem and adopted an improved dynamic window method that combines the advantages of global path planning algorithms and local path planning algorithms, and designed a global path planning strategy based on the improved Bi-RRT and Dijkstra algorithms. Teng Lei et al. improved the A* algorithm. According to the distance between the current position and the target position, the weight coefficient of the heuristic function in the evaluation function is dynamically adjusted, effectively improving the search efficiency and target orientation of the A* algorithm. Accurately analyzing the airspace flown by the UAV and obtaining correct air situation information are important prerequisites for UAV route planning.
[0005] Path planning is one of the core technologies for the autonomous navigation of unmanned systems. Traditional path planning methods such as the A* algorithm usually conduct path search based on the geographical information of the environment. However, in complex environments, especially in the presence of dynamic threats such as obstacles and enemy radars, traditional methods are difficult to effectively avoid threats and find the optimal path. Existing environmental modeling methods such as the grid method can quantify environmental information, but lack dynamic evaluation and integration of threat factors. Summary of the Invention
[0006] The object of the present invention is to provide a route planning method based on a threat field model, comprising the following steps:
[0007] 1. Environmental Modeling
[0008] Accurately analyzing the airspace over which the UAV flies and obtaining correct air situation information are important prerequisites for UAV route planning. In path planning algorithms, environmental modeling algorithms aim to quantify complex environmental information and input it into the path planning algorithm for path planning. As a widely used environmental modeling algorithm, the grid method can reasonably divide an irregular environment, and by adjusting the size of the grid, the accuracy of environmental modeling can be changed, thereby controlling the computational amount of the model. Moreover, combined with the principle of the A* algorithm, the grid method is more convenient when calculating the cost function g(n).
[0009] In the grid method used in this paper, starting from the top-left first grid, each grid is labeled from top to bottom and from left to right. In this labeling system, taking the m*n grid matrix as an example, the serial number corresponding to the point with coordinates (i, j) in the coordinate method is (j - 1)*m + i, and its corresponding relationship is shown in Equation (1):
[0010]
[0011] 2. Constructing a Threat Field Model
[0012] In a war airspace environment, the UAV is threatened by various types of weapons of different air defense platforms, and the fire coverage ranges cover airspaces such as low altitude, ultra-low altitude, high altitude, and ultra-high altitude, covering and protecting each other, forming a diverse and three-dimensional firepower configuration. To quantify the enemy threat, based on the concept of firepower density, this paper constructs a threat field model through Equations (2) to (5).
[0013]
[0014] Equation (2) is the firepower density calculation formula, where n mb represents the number of target channels that the fire control radar covering this airspace can simultaneously track, represents the average shooting cycle time, represents the average firepower density.
[0015]
[0016] Equation (3) calculates the distance coefficient of the i-th grid, where (x1, y1) is the position of the UAV, (x2, y2) is the position of the radar, and d0 is the effective detection distance of the radar. In the actual combat airspace, the enemy's air defense firepower will cover each other. Equation (4) calculates the superimposed threat degree formed by different threat sources for the i-th grid, and Equation (5) introduces the threat field proportionality coefficient C based on expert experience i , and normalizes and corrects the threat value.
[0017] 3. Improvement of A* algorithm based on threat field model
[0018] In the traditional A* algorithm, generally, the grid where the obstacle is located is assigned a value of 1, and the grid with free passage is assigned a value of 0 to avoid obstacles in the graph search algorithm. However, in the battlefield environment, more grids are grids that can only be passed at a certain risk, and the cost weight assigned to this grid changes with the change of the grid position. To introduce the threat field into the model, the threat value pun’(n) is introduced into the traditional A* algorithm to form the A* algorithm based on the threat field model, and the corresponding heuristic function calculation formula is shown in Equation (6).
[0019] f(n) = g(n) + h(n) + pun′(n) (6)
[0020] In Equation (6), the function value f(n) is the minimum path cost from the starting point of each flight segment through the current node to the end point of each flight segment. g(n) is the actual cost from the starting point to the current node, h(n) is the estimated cost from the current node to the target node, and the threat value pun’(n) is determined by the algorithm in the environmental modeling. Each grid has its specific threat value, and pun’(n) is accumulated into f(n) along with the selected path when calculating g(n) and h(n).
[0021] 4. Path optimization
[0022] Through iterative search, an optimal path from the starting point to the target point is found to minimize the total cost f(n) of the path.
[0023] To sum up, the present invention has the following beneficial effects: 1. By introducing the threat field model, it can dynamically evaluate the threat factors in the environment and improve the safety of path planning. 2. Combining the grid method and the A* algorithm can improve the accuracy of path planning while ensuring the calculation efficiency. 3. It is applicable to the autonomous navigation of unmanned systems in complex environments and has a wide range of application prospects. Description of the drawings
[0024] Figure 1 is the flowchart of the present invention.
[0025] Figure 2 is the traditional A* algorithm.
[0026] Figure 3 For the path planning diagram obtained by combining this method. Specific implementation mode
[0027] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments:
[0028] Embodiment 1
[0029] Generate an airspace of 60*60 with a threat field,
[0030] Input the parameters in Table 1 into the model for simulation, and the obtained results are as Figure 2 shown. Figure 3 It is the simulation of this scenario using the traditional A* algorithm without considering the threat field.
[0031] In Figure 2 , the white grids represent the airspace that the UAV can fly over freely, the black grids represent the airspace that cannot be flown over, the shaded squares represent the threat field airspace that needs to pay a cost to fly over, and the red line segments represent the flight routes searched by the algorithm. By Figure 2 and Figure 3 comparison, the flight route obtained by the improved search algorithm not only maintains a relatively short total flight distance, but also effectively avoids the airspace that cannot be flown over and stays away from the threat field airspace as much as possible, effectively demonstrating the rationality and accuracy of the algorithm.
[0032] Table 1 Model input parameter table
[0033]
[0034] Figure 2 is the traditional A* algorithm, which does not have the function of avoiding the threat field. For the sake of not affecting understanding, the shadow of the field is not shown in the figure. Figure 3 is the algorithm proposed in this paper. It can be found by comparing with Figure 2 that it can effectively avoid the threat field.
[0035] This specific embodiment is only an explanation of the present invention, and it is not a limitation of the present invention. Those skilled in the art can make modifications to this embodiment without creative contributions according to needs after reading this specification, but as long as they are within the scope of the claims of the present invention, they are protected by the patent law.
Claims
1. A route planning method based on a threat field model, characterized in that It includes the following steps:
1. Environmental modeling Obtain the correct air situation, quantify complex environmental information using environmental modeling algorithms, and input it into the path planning algorithm for path planning. Combining the principles of the grid method and the A* algorithm, calculate the cost function g(n), and by adjusting the size of the grid, the accuracy of environmental modeling can be changed, thereby controlling the computational amount of the model; 2. Construct a threat field model Quantify the threats from the enemy and construct a threat field model through Equations (2) to (5). Equation (2) is the firepower density calculation formula, where n mb represents the number of target channels that the fire control radar covering this airspace can track simultaneously, represents the average firing cycle time, represents the average firepower density; Equation (3) calculates the distance coefficient of the i-th grid, where (x1, y1) is the position of the UAV, (x2, y2) is the position of the radar, and d0 is the effective detection range of the radar. In the actual combat airspace, the enemy's air defense firepower will cover each other. Equation (4) calculates the superimposed threat degree formed by different threat sources for the i-th grid, and Equation (5) introduces the threat field proportionality coefficient C i , to normalize and correct the threat value; 3. Improvement of the A* algorithm based on the threat field model Introduce the threat field into the model and introduce the threat value pun’(n) into the A* algorithm to form the A* algorithm based on the threat field model. The calculation formula of its corresponding heuristic function is shown in Equation (6). f(n) = g(n) + h(n) + pun'(n) (6) In Equation (6), the function value f(n) is the minimum path cost from the starting point of each flight segment through the current node to the end point of each flight segment. g(n) is the actual cost from the starting point to the current node, h(n) is the estimated cost from the current node to the target node, and the threat value pun’(n) is determined by the algorithm in environmental modeling. Each grid has its specific threat value, and pun’(n) is accumulated into f(n) along with the selected path when calculating g(n) and h(n); 4. Path optimization Find an optimal path from the starting point to the target point through iterative search to minimize the total cost f(n) of the path.
2. The route planning method based on the threat field model according to claim 1, characterized in that For the grid method used in Step 1, starting from the upper left first grid, label each grid from top to bottom and from left to right. In this labeling system, taking the m*n grid matrix as an example, in the coordinate method, the serial number corresponding to the point with coordinates (i, j) is (j - 1)*m + i, and its corresponding relationship is shown in Equation (1):