A method for unmanned aerial vehicle path planning based on radar omnidirectional detection and improved A* algorithm

Abstract

The application discloses a kind of based on radar omni-directional detection and the path planning method of unmanned plane of improved A* algorithm, comprising: to the elevation topographic map is down-sampling processing, the elevation cost matrix of entire elevation topographic map is calculated;Using radar omni-directional detection technology detects the dynamic threat target of surrounding environment, and obtains threat target speed, direction and position information, power distribution is carried out to dynamic threat target;According to dynamic threat target information, unmanned plane uses improved A* algorithm to carry out flight path planning, obtains the original path of unmanned plane on elevation topographic map, saves this original path as a series of three-dimensional discrete path points;Using cubic spline curve fitting algorithm is carried out to the series of three-dimensional discrete path points obtained and is smoothed, so that it is more in line with actual flight trajectory.The application effectively solves the problem that dynamic threat target avoids under certain conditions, improves the efficiency and safety of unmanned plane path planning.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of autonomous path planning, and particularly relates to a UAV path planning method based on radar omnidirectional detection and improved A* algorithm. BACKGROUND

[0002] With the rapid development of UAV technology, UAVs have been widely used in military reconnaissance, disaster relief, environmental monitoring and other fields. However, when UAVs perform tasks in complex environments, their safety and efficiency face many challenges. Especially in complex terrains such as mountains, due to the dramatic undulating terrain and dense ground obstacles, the path planning of UAVs has great difficulties. In addition, complex terrains are usually accompanied by dynamic threats, such as the sudden appearance of unknown obstacles or the threat of enemy targets, which further increase the difficulty of task execution. How to plan a safe and efficient flight path for UAVs in a dynamic threat environment is a problem that needs to be solved. In view of the difficulty of path planning in mountainous environment and the poor path planning effect, Peng Yi et al. proposed an improved PID search algorithm for mountainous UAV path planning, which effectively improves the path planning ability of UAVs. For the regional reconnaissance task of UAVs in mountainous environment, Wang Cheng et al. proposed an adaptive chaos beetle optimization algorithm for UAV regional coverage path planning, which can efficiently complete the regional coverage task. For the obstacle avoidance problem of UAV path planning in mountainous environment, Hou Chenxi proposed a rolling speed obstacle method to detect whether the UAV collides with obstacles, which can achieve obstacle avoidance and the obstacle avoidance path is relatively smoother. However, the above researches only consider the obstacles in the forward direction of the UAV, and pay less attention to the dynamic threats approaching the UAV from all directions. During the execution of the task of the UAV, dynamic threats may approach the UAV from all directions, posing a safety threat to the UAV. SUMMARY

[0003] The purpose of the present application is to provide a UAV path planning method based on radar omnidirectional detection and improved A* algorithm, which effectively improves the safety of UAV path planning under dynamic threats.

[0004] Technical scheme: The UAV path planning method based on radar omnidirectional detection and improved A* algorithm comprises:

[0005] S1, performing down-sampling processing on the elevation terrain map, calculating the elevation cost matrix of the entire elevation terrain map, so that the UAV can choose to bypass or cross the terrain obstacles in the subsequent flight;

[0006] S2, using radar omnidirectional detection technology to detect dynamic threat targets in the surrounding environment, and obtaining threat target speed, direction and position information, and power allocation for dynamic threat targets;

[0007] S3, the dynamic threat target information obtained in step S2, the unmanned aerial vehicle adopts an improved A* algorithm to plan a flight path, to obtain an original path of the unmanned aerial vehicle on the elevation map, and save the original path as a series of three-dimensional discrete path points;

[0008] S4, a cubic spline curve fitting algorithm is used to smooth the obtained series of three-dimensional discrete path points, so that the three-dimensional discrete path points are more consistent with the actual flight trajectory.

[0009] Further, in step S1, calculating the elevation cost matrix of the entire elevation map includes the following steps:

[0010] Step 1, assuming that the elevation matrix of the elevation map is A n×n , wherein n represents the number of grid divisions of the elevation map, first let the base matrix B n×n equal to A n×n ;

[0011] Step 2, then extend B n×n according to the following formula, extend 1 order around B n×n , and assign a value of ∞, that is, set the elevation of the flight path outside the elevation map to be infinite, limit the unmanned aerial vehicle to fly within the elevation map, and set the part outside the elevation map as a no-fly zone; the extended matrix is represented as:

[0012]

[0013] , wherein the elements b 11 , …, b nn in the matrix represent the altitudes of each grid point on the elevation map;

[0014] Step 3, define that the unmanned aerial vehicle can fly in 8 directions, forward, backward, left, right, and diagonal, during the process of flying from the starting point to the ending point on the elevation map, calculate the elevation cost matrix of the unmanned aerial vehicle flying in the kth direction as C k =B ov -A n×n , wherein B ov is the overlapping matrix of the matrix B (n+2)×(n+2) and the matrix A n×n , and C k determines the elevation difference cost of the unmanned aerial vehicle flying to any point in the specified direction on the elevation map.

[0015] Further, in step S2, the dynamic threat target around the environment is detected using a radar omnidirectional detection technology, including the following steps:

[0016] Step 1, according to the mountainous environment and the flight task, set the working frequency f req of the radar, the scanning angle Maximum detection distance d max And detection accuracy σ

[0017] Step 2, the radar starts to perform 360-degree omnidirectional scanning, continuously transmits and receives radar signals, covering all directions around the UAV;

[0018] Step 3, analyze the received radar echo signal, apply the Doppler effect to measure the relative speed of dynamic threat targets, and judge whether the dynamic threat targets are close to the UAV to cause threats;

[0019] Step 4, evaluate the detected dynamic threat targets, and allocate more detection power to the direction where the dynamic threat targets are located;

[0020] Step 5, transmit the dynamic threat target information detected by the radar to the path planning module of the UAV, and provide dynamic threat target position and speed information.

[0021] Further, in step S3, the UAV uses an improved A* algorithm for flight path planning, including the following steps:

[0022] Step 1, input the starting grid coordinate position p start , the ending grid coordinate position p end , and the UAV flight speed v of the UAV on the elevation map;

[0023] Step 2, create an OPEN table T open and a CLOSE table T close ; in each step of search, calculate the loss function f(p i ) = g(p i ) + h(p i ) of the current node p i , where g(p i ) is the cost of the distance already traveled, h(p i ) is the distance from the current node to the end point, and i is the node number; when the height h z of the current node is greater than the height h z-1 of the last node, that is, △h z = h z - h z-1 > 0, the cost of the distance already traveled is Otherwise, g(p i ) = △h x + △h y + △h z , where △h x , △h y respectively represent the distance difference when the UAV flies in the x direction and the y direction;

[0024] Step 3, when Topen If it is not empty, find T. open loss function f(p) i Find the smallest point, set that point as the current node, and start from T. open Remove from p if the current node is equal to p. end , return by T close The generated path, otherwise add the current node to T. close ;

[0025] Step 4: Calculate the eight nearest neighbors of the current node in the front, back, left, right, and diagonal directions, and compare their loss functions f(p). i ) and elevation cost matrix C k If a neighboring point is neither obstructed nor located in a no-fly zone, meaning the neighboring point is valid, then it is determined whether the neighboring point is within T. open In the middle, if the neighboring point is already in T open In the middle, check if there is a smaller g(p) i If a value exists, update T. open If a point is a neighboring point in T, then add the neighboring point to T. open middle;

[0026] Step 5: Repeat steps 3 and 4 until p is found. end or T open Until it is empty.

[0027] Based on the above steps, the original path of the UAV on the elevation topographic map is obtained, and the original path is saved as a series of three-dimensional discrete path points.

[0028] Furthermore, in step S4, a cubic spline curve fitting algorithm is used to smooth the obtained series of three-dimensional discrete path points, including the following steps:

[0029] Step 1: Extract N discrete path points from the series of 3D discrete path points saved in the original path, and represent the path points as 3D coordinates: Path = [(x1,y1,z1),(x2,y2,z2),…,(x...z1)...z2)... N ,y N ,z N )], where (x j ,y j ,z j () represents the position coordinates of the path point in the three-dimensional coordinate system, j = 0, 1, ..., N;

[0030] Step 2: Define the sub-interval between two adjacent path points as [m i ,m i+1 ], i = 0, 1, ..., N-1, where m i m represents the cumulative arc length from the starting point to the i-th path point. i+1This represents the cumulative distance of the arc from the starting point to the (i+1)th path point. In each subinterval [m] i ,m i+1 Construct cubic spline interpolation functions for the x, y, and z directions respectively; let the cubic spline interpolation function in the x direction be S. x,i (f)=a x,i +b x,i (fm i )+c x,i (fm i ) 2 +d x,i (fm i ) 3 The cubic spline interpolation function in the y-direction is S. y,i (f)=a y,i +b y,i (fm i )+c y,i (fm i ) 2 +d y,i (fm i ) 3 The cubic spline interpolation function in the z-direction is S. z,i (f)=a z,i +b z,i (fm i )+c z,i (fm i ) 2 +d z,i (fm i ) 3 Where f represents the arc length of a 3D path point on a subinterval, and a x,i b x,i c x,i d x,i S represents x,i (f) The coefficients of the cubic spline interpolation function. Similarly, a y,i b y,i c y,i d y,i and a z,i b z,i c z,i d z,i They are S y,i (f) and S z,i The coefficient of (f);

[0031] Step 3: Construct a system of linear equations for the x, y, and z directions respectively, and solve the system of linear equations to obtain the coefficients of the spline function;

[0032] Step 4: For each subinterval [m] i ,mi+1 Apply cubic spline functions S in the x, y, and z directions respectively. x,i (f),S y,i (f),S z,i (f) generates the fitted path, and takes multiple points within the sub-interval according to the accuracy requirements, calculates the coordinates of these points, and connects these three-dimensional points to obtain the fitted smooth path.

[0033] The system corresponding to the method includes:

[0034] The elevation cost matrix calculation unit is used to downsample the elevation topographic map and calculate the elevation cost matrix of the entire elevation topographic map, so that the UAV can choose to fly around or over terrain obstacles.

[0035] The target detection and power allocation unit is used to detect dynamic threat targets in the surrounding environment using radar omnidirectional detection technology, and to obtain information on the speed, direction and position of the threat targets, and to allocate power to the dynamic threats.

[0036] The path planning unit is used to plan the flight path of the UAV using the improved A* algorithm based on the acquired dynamic threat target information, and to obtain the original path of the UAV on the elevation topographic map. The original path is then saved as a series of three-dimensional discrete path points.

[0037] The path smoothing unit is used to smooth a series of three-dimensional discrete path points obtained by using a cubic spline curve fitting algorithm, so that they are more consistent with the actual flight trajectory.

[0038] Optionally, the UAV uses an improved A* algorithm for flight path planning, including the following steps:

[0039] Step 1: Input the starting grid coordinates p of the UAV on the elevation topographic map. start End point grid coordinate position p end And the drone's flight speed v;

[0040] Step 2: Create the OPEN table as T open The CLOSE table is T close In each step of the search, the current node p is calculated. i loss function f(p) i )=g(p i )+h(p i ), where g(p i ) is the cost of the distance already traveled, h(p) i ) represents the distance from the current node to the destination, i is the number of nodes; h is the height of the current node. z Greater than the height h of the previous node z-1 When, i.e., △hz =h z -h z-1 >0, the cost of the distance already traveled is Otherwise g(p i )=△h x +△h y +△h z , where △h x , △h y These represent the distance difference when the drone flies in the x and y directions, respectively.

[0041] Step 3, when T open If it is not empty, find T. open loss function f(p) i Find the smallest point, set that point as the current node, and start from T. open Remove from p if the current node is equal to p. end , return by T close The generated path, otherwise add the current node to T. close ;

[0042] Step 4: Calculate the eight nearest neighbors of the current node in the front, back, left, right, and diagonal directions, and compare their loss functions f(p). i ) and elevation cost matrix C k If a neighboring point is neither obstructed nor located in a no-fly zone, meaning the neighboring point is valid, then it is determined whether the neighboring point is within T. open In the middle, if the neighboring point is already in T open In the middle, check if there is a smaller g(p) i If a value exists, update T. open If a point is a neighboring point in T, then add the neighboring point to T. open middle;

[0043] Step 5: Repeat steps 3 and 4 until p is found. end or T open Until it is empty.

[0044] Based on the above steps, the original path of the UAV on the elevation topographic map is obtained, and the original path is saved as a series of three-dimensional discrete path points.

[0045] Optionally, a cubic spline curve fitting algorithm is used to smooth the obtained series of three-dimensional discrete path points, including the following steps:

[0046] Step 1: Extract N discrete path points from the series of 3D discrete path points saved in the original path, and represent the path points as 3D coordinates: Path = [(x1,y1,z1),(x2,y2,z2),…,(x...z1)...z2)... N ,y N ,z N)], where (x j ,y j ,z j () represents the position coordinates of the path point in the three-dimensional coordinate system, j = 0, 1, ..., N;

[0047] Step 2: Define the sub-interval between two adjacent path points as [m i ,m i+1 ], i = 0, 1, ..., N-1, where m i m represents the cumulative arc length from the starting point to the i-th path point. i+1 This represents the cumulative distance of the arc from the starting point to the (i+1)th path point. In each subinterval [m] i ,m i+1 Construct cubic spline interpolation functions for the x, y, and z directions respectively; let the cubic spline interpolation function in the x direction be S. x,i (f)=a x,i +b x,i (fm i )+c x,i (fm i ) 2 +d x,i (fm i ) 3 The cubic spline interpolation function in the y-direction is S. y,i (f)=a y,i +b y,i (fm i )+c y,i (fm i ) 2 +d y,i (fm i ) 3 The cubic spline interpolation function in the z-direction is S. z,i (f)=a z,i +b z,i (fm i )+c z,i (fm i ) 2 +d z,i (fm i ) 3 Where f represents the arc length of a 3D path point on a subinterval, and a x,i b x,i c x,i d x,i S represents x,i (f) The coefficients of the cubic spline interpolation function. Similarly, a y,i b y,i c y,i d y,i and az,i b z,i c z,i d z,i They are S y,i (f) and S z,i The coefficient of (f);

[0048] Step 3: Construct a system of linear equations for the x, y, and z directions respectively, and solve the system of linear equations to obtain the coefficients of the spline function;

[0049] Step 4: For each subinterval [m] i ,m i+1 Apply cubic spline functions S in the x, y, and z directions respectively. x,i (f),S y,i (f),S z,i (f) generates the fitted path, and takes multiple points within the sub-interval according to the accuracy requirements, calculates the coordinates of these points, and connects these three-dimensional points to obtain the fitted smooth path.

[0050] An electronic device for storing and executing the method, the device comprising:

[0051] Memory containing executable program code;

[0052] A processor coupled to the memory;

[0053] The processor calls the executable program code stored in the memory to execute the steps of the UAV path planning method based on radar omnidirectional detection and improved A* algorithm.

[0054] A computer-readable storage medium for storing and executing the method, the computer-readable storage medium storing computer instructions, which, when invoked, are used to execute the steps of the UAV path planning method based on radar omnidirectional detection and improved A* algorithm.

[0055] Beneficial effects: Compared with the prior art, the significant technical effects of the present invention are as follows: First, by using radar omnidirectional detection technology, dynamic threat targets in all directions around the UAV can be detected in real time, which can effectively solve the problem of difficult detection of dynamic threat targets. Based on this, the improved A* algorithm is adopted in global path planning. Through dimensionality reduction planning and improved heuristic functions, the UAV can achieve the shortest path and threat avoidance. Attached Figure Description

[0056] Figure 1 This is a flowchart illustrating the overall implementation of the present invention;

[0057] Figure 2 This is a flowchart of the improved A* algorithm of the present invention;

[0058] Figure 3 This is a mountainous environment elevation and topographic map downloaded from SRTM in this embodiment of the invention;

[0059] Figure 4 This is a path planning diagram for the UAV to avoid dynamic threat targets in an embodiment of the present invention;

[0060] Figure 5 This is a power allocation diagram for radar detection of dynamic threat targets in an embodiment of the present invention;

[0061] Figure 6 This is the result of smoothing the original planned path in this embodiment of the invention. Detailed Implementation

[0062] The present invention will be further described below with reference to embodiments and accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and should not be construed as limiting the scope of protection of the present invention.

[0063] In this example, for simplicity, the elevation topographic map used has been downsampled, with the number of raster pixels reduced to 200×200. For example... Figure 1 As shown, this invention proposes a UAV path planning method based on radar omnidirectional detection and an improved A* algorithm, comprising:

[0064] S1. Import the elevation topographic map downloaded by SRTM, perform downsampling processing on the elevation topographic map, and calculate the elevation cost matrix of the entire elevation topographic map so that the UAV can choose to bypass or cross terrain obstacles. The specific content of the calculated elevation cost matrix is ​​as follows:

[0065] Step 1: Assume the elevation matrix of the topographic map is A. n×n Where n represents the number of grids in the elevation topographic map, first let the base matrix B n×n Equals A n×n ;

[0066] Step 2, then expand B according to the following formula n×n In B n×n Extending the matrix by order 1 around its perimeter and assigning it the value ∞, means setting the elevation of the flight path beyond the elevation topographic map to infinity, thus restricting the UAV to fly within the elevation topographic map area, and designating the portion beyond the elevation topographic map as a no-fly zone; the extended matrix is ​​represented as:

[0067]

[0068] Wherein, the element b in the matrix 11 ,…,b nn This indicates the elevation of each grid point on the topographic map.

[0069] Step 3: Define the UAV's flight path as it flies from the starting point to the destination on the elevation topographic map. At each two-dimensional plane grid point, it can fly in eight directions (forward, backward, left, right, and diagonal). Define the direction matrix as D = [0 1; 1 0; 0 -1; -1 0; 1 1; 1 -1; -1 1; -1 -1]. Calculate the elevation cost matrix C for the UAV flying in the k-th direction. k =B ov -A n×n B ov It is matrix B (n+2)×(n+2) Sum matrix A n×n The overlap matrix, C k Determine the elevation difference cost of a UAV flying to any point in a specified direction on a topographic map.

[0070] S2. Use radar omnidirectional detection technology to detect dynamic threat targets in the surrounding environment, and obtain information on the speed, direction, and position of these targets, and allocate power to them; the specific content is as follows:

[0071] Step 1: Set the radar's operating frequency f based on the mountainous environment and flight mission. req Scanning angle Maximum detection range d max And the detection accuracy σ;

[0072] Step 2: The radar begins a 360-degree omnidirectional scan, continuously transmitting and receiving radar signals to cover all directions around the drone;

[0073] Step 3: Analyze the received radar echo signal, apply the Doppler effect to measure the relative velocity of the dynamic threat target, and determine whether the dynamic threat target is approaching the UAV and posing a threat.

[0074] Step 4: Evaluate the detected dynamic threat targets and allocate more detection power to the direction where the dynamic threat targets are located;

[0075] Step 5: Transmit the information of dynamic threat targets detected by the radar to the path planning module of the UAV, providing the position and speed information of the dynamic threat targets.

[0076] S3. Based on the dynamic threat target information obtained in step S2, the UAV uses the improved A* algorithm for flight path planning. The traditional 3D-A* algorithm expands neighbor nodes to 26 directions (up / down, forward / backward, left / right, and diagonal), and the cost function is calculated in the complete three-dimensional space. The improved A* algorithm restricts the expansion directions to only eight directions—forward / backward, left / right, and diagonal—in a two-dimensional plane, and separates elevation processing from the expansion directions, judging elevation costs separately. Through dimensionality reduction planning, the algorithm's efficiency and computational complexity are improved. The improved A* algorithm's UAV path planning process is as follows: Figure 2 As shown, the specific content is as follows:

[0077] Step 1: Input the starting grid coordinates p of the UAV on the elevation topographic map. start End point grid coordinate position p end And the drone's flight speed v;

[0078] Step 2: Create the OPEN table as T open The CLOSE table is T close In each step of the search, the current node p is calculated. i loss function f(p) i )=g(p i )+h(p i ), where g(p i ) is the cost of the distance already traveled, h(p) i The distance from the current node to the destination is denoted by , where i is the number of nodes. The height h of the current node is also denoted by . z Greater than the height h of the previous node z-1 When, i.e., △h z =h z -h z-1 >0, the cost of the distance already traveled is Otherwise g(p i )=△h x +△h y +△h z , where △h x , △h y These represent the distance difference when the drone flies in the x and y directions, respectively.

[0079] Step 3, when T open If it is not empty, find T. open loss function f(p) i Find the smallest point, set that point as the current node, and start from T. open Remove from p if the current node is equal to p. end , return by T close The generated path, otherwise add the current node to T. close ;

[0080] Step 4: Calculate the eight nearest neighbors of the current node in the front, back, left, right, and diagonal directions, and compare their loss functions f(p). i ) and elevation cost matrix C k If a neighboring point is neither obstructed nor located in a no-fly zone, meaning the neighboring point is valid, then it is determined whether the neighboring point is within T. open In the middle, if the neighboring point is already in T open In the middle, check if there is a smaller g(p) i If a value exists, update T. open If a point is a neighboring point in T, then add the neighboring point to T. open middle;

[0081] Step 5: Repeat steps 3 and 4 until p is found. end or T open Until it is empty.

[0082] Based on the above steps, the original path of the UAV on the elevation topographic map is obtained, and the original path is saved as a series of three-dimensional discrete path points.

[0083] S4. A cubic spline curve fitting algorithm is used to smooth the obtained series of three-dimensional discrete path points to make them more consistent with the actual flight trajectory. The specific content is as follows:

[0084] Step 1: Extract N discrete path points from the series of 3D discrete path points saved in the original path, and represent the path points as 3D coordinates: Path = [(x1,y1,z1),(x2,y2,z2),…,(x...z1)...z2)... N ,y N ,z N )], where (x j ,y j ,z j ) represents the position coordinates of the path point in the three-dimensional coordinate system, j = 0, 1, ..., N.

[0085] Step 2: Define the sub-interval between two adjacent path points as [m i ,m i+1 ], i = 0, 1, ..., N-1, where m i m represents the cumulative arc length from the starting point to the i-th path point. i+1 This represents the cumulative distance of the arc from the starting point to the (i+1)th path point. In each subinterval [m] i ,m i+1 Construct cubic spline interpolation functions for the x, y, and z directions respectively. Let the cubic spline interpolation function in the x direction be S. x,i (f)=a x,i +b x,i (fm i)+c x,i (fm i ) 2 +d x,i (fm i ) 3 The cubic spline interpolation function in the y-direction is S. y,i (f)=a y,i +b y,i (fm i )+c y,i (fm i ) 2 +d y,i (fm i ) 3 The cubic spline interpolation function in the z-direction is S. z,i (f)=a z,i +b z,i (fm i )+c z,i (fm i ) 2 +d z,i (fm i ) 3 Where f represents the arc length of a 3D path point on a subinterval, and a x,i b x,i c x,i d x,i S represents x,i (f) The coefficients of the cubic spline interpolation function. Similarly, a y,i b y,i c y,i d y,i and a z,i b z,i c z,i d z,i They are S y,i (f) and S z,i The coefficient of (f).

[0086] Step 3: Construct a system of linear equations for the x, y, and z directions respectively, and solve the system of linear equations to obtain the coefficients of the cubic spline interpolation function;

[0087] Step 4: For each subinterval [m] i ,m i+1 Apply cubic spline functions S in the x, y, and z directions respectively. x,i (f),S y,i (f),S z,i (f) generates the fitted path, and takes multiple points within the sub-interval according to the accuracy requirements, calculates the coordinates of these points, and connects these three-dimensional points to obtain the fitted smooth path.

[0088] The system corresponding to the method includes:

[0089] The elevation cost matrix calculation unit is used to downsample the elevation topographic map and calculate the elevation cost matrix of the entire elevation topographic map, so that the UAV can choose to fly around or over terrain obstacles.

[0090] The target detection and power allocation unit is used to detect dynamic threat targets in the surrounding environment using radar omnidirectional detection technology, and to obtain information on the speed, direction and position of the threat targets, and to allocate power to the dynamic threats.

[0091] The path planning unit is used to plan the flight path of the UAV using the improved A* algorithm based on the acquired dynamic threat target information, and to obtain the original path of the UAV on the elevation topographic map. The original path is then saved as a series of three-dimensional discrete path points.

[0092] The path smoothing unit is used to smooth a series of three-dimensional discrete path points obtained by using a cubic spline curve fitting algorithm, so that they are more consistent with the actual flight trajectory.

[0093] An electronic device for storing and executing the method, the device comprising:

[0094] Memory containing executable program code;

[0095] A processor coupled to the memory;

[0096] The processor calls the executable program code stored in the memory to execute the steps of the UAV path planning method based on radar omnidirectional detection and improved A* algorithm.

[0097] A computer-readable storage medium for storing and executing the method, the computer-readable storage medium storing computer instructions, which, when invoked, are used to execute the steps of the UAV path planning method based on radar omnidirectional detection and improved A* algorithm.

[0098] In this example, the topographic map is elevation data numbered 59_05 downloaded from SRTM, and the terrain environment is as follows: Figure 3 As shown. Assume the drone's starting coordinates are [100, 50] and ending coordinates are [10, 77]. The speeds of the dynamic threats may differ but remain constant. The drone path planned using the improved A* algorithm is shown below. Figure 4 As shown, the black arrow points to the flight direction of the dynamic threat. Figure 4 Therefore, the improved A* algorithm can quickly find the shortest path from the starting point to the destination and can avoid dynamic obstacles, thus achieving safe flight. Figure 5It is the result of the airborne radar's power allocation to dynamic threat targets throughout the entire flight time, by Figure 5 It can be concluded that: Threat target 1 flies away from the drone, initially allocated a certain amount of power, which gradually decreases over time; Threat target 2 flies towards the drone, and its allocated power gradually increases over time; Threat target 3 appears far from the drone's flight path and flies away from the drone, therefore its allocated power also gradually decreases. The result after smoothing the original path using cubic spline interpolation is as follows: Figure 6 As shown, by Figure 6 It can be seen that the original path is not smooth and has many corners, which is insufficient to match the actual flight trajectory of the UAV. By using cubic spline interpolation curve to optimize the original path, it can be found that the discontinuous turns are smoothed, resulting in a smoother curve.

[0099] In summary, by utilizing radar omnidirectional detection technology, dynamic threat targets within all directions around the UAV can be detected, and power can be allocated to dynamic threats, effectively solving the problem of difficult dynamic threat detection. Furthermore, by employing an improved A* algorithm in global path planning, and through dimensionality reduction planning and improved heuristic functions, the UAV can achieve the shortest path and threat avoidance.

[0100] Although embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention. Any other corresponding changes and variations made in accordance with the technical concept of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A UAV path planning method based on radar omnidirectional detection and an improved A* algorithm, characterized in that, include: S1. Perform downsampling on the elevation topographic map and calculate the elevation cost matrix of the entire elevation topographic map; The elevation cost matrix is ​​used to determine the elevation difference cost of a UAV flying to any point in a specified direction on a topographic map; it includes the following steps: Step 1: Assume the elevation matrix of the topographic map is as follows: ,in To represent the number of grid cells in an elevation topographic map, first let the base matrix... equal ; Step 2, then expand according to the following formula ,exist Extend the surrounding area by 1 order and assign a value of This means setting the altitude of the flight path exceeding the topographic map to infinity, restricting the drone's flight to within the topographic map area, and designating the portion exceeding the topographic map as a no-fly zone; the expanded matrix representation is as follows: ； Among them, the elements in the matrix This indicates the elevation of each grid point on the topographic map; Step 3: Define the UAV's flight path as it flies from the starting point to the destination on the elevation topographic map. At each two-dimensional plane grid point, the UAV can fly in eight directions: forward, backward, left, right, and diagonally. Calculate the distance the UAV travels along the [missing information - likely a specific direction or path]. The elevation cost matrix for each direction of flight is: ,in It is a matrix sum matrix The overlap matrix, Determine the elevation difference cost of a UAV flying to any point in a specified direction on a topographic map; S2. Use radar omnidirectional detection technology to detect dynamic threat targets in the surrounding environment, and obtain information on the speed, direction and position of the threat targets, and allocate power to the dynamic threat targets; S3. Based on the dynamic threat target information obtained in step S2, the UAV uses the improved A* algorithm to plan its flight path, obtaining the original path of the UAV on the elevation topographic map, and saving the original path as a series of three-dimensional discrete path points; including the following steps: Step 1: Input the starting grid coordinates of the UAV on the elevation topographic map. End point grid coordinates and the flight speed of drones ; Step 2: Create the OPEN table. The CLOSE table is In each step of the search, the current node is calculated. loss function ,in It is the cost of the journey already taken. This represents the distance from the current node to the destination. The number of nodes; the height of the current node. Greater than the height of the previous node At that time, that is The cost of the journey already completed is ,otherwise ,in, , These represent the drones heading towards direction and The distance difference during directional flight; Step 3, when When not empty, find loss function Find the smallest point, set that point as the current node, and start from... Remove from the middle if the current node is equal to , return by The generated path, otherwise add the current node. ; Step 4: Calculate the eight nearest neighbors of the current node in the front, back, left, right, and diagonal directions, and compare their loss functions. and elevation cost matrix C k If a neighboring point is neither obstructed nor located within a no-fly zone, meaning the neighboring point is valid, then it is determined whether the neighboring point is within a no-fly zone. In the middle, if the neighboring point is already In the middle, check if there is a smaller one. Value, if available, update If a point is a neighboring point, then add the neighboring point to the list. middle; Step 5: Repeat steps 3 and 4 until found. or Until it is empty; S4. A cubic spline curve fitting algorithm is used to smooth the obtained series of three-dimensional discrete path points to make them more consistent with the actual flight trajectory.

2. The UAV path planning method based on radar omnidirectional detection and improved A* algorithm according to claim 1, characterized in that, In step S2, radar omnidirectional detection technology is used to detect dynamic threat targets in the surrounding environment, including the following steps: Step 1: Set the radar's operating frequency according to the mountainous environment and flight mission. Scanning angle Maximum detection range and detection accuracy ; Step 2: The radar begins a 360-degree omnidirectional scan, continuously transmitting and receiving radar signals to cover all directions around the drone; Step 3: Analyze the received radar echo signal, apply the Doppler effect to measure the relative velocity of the dynamic threat target, and determine whether the dynamic threat target is approaching the UAV and posing a threat. Step 4: Evaluate the detected dynamic threat targets and allocate more detection power to the direction where the dynamic threat targets are located; Step 5: Transmit the information of dynamic threat targets detected by the radar to the path planning module of the UAV, providing the position and speed information of the dynamic threat targets.

3. The UAV path planning method based on radar omnidirectional detection and improved A* algorithm according to claim 1, characterized in that, In step S4, a cubic spline curve fitting algorithm is used to smooth the obtained series of three-dimensional discrete path points, including the following steps: Step 1: Extract N discrete path points from a series of 3D discrete path points saved in the original path, and represent the path points as 3D coordinates. ,in, This represents the position coordinates of the path point in the three-dimensional coordinate system. ; Step 2: Define the sub-interval between two adjacent path points as... ,in, Indicates the distance from the starting point to the... The cumulative distance of the arc length of each path point Indicates the distance from the starting point to the... The cumulative distance of the arc length of each path point; in each sub-interval Above, respectively for , , Construct a cubic spline interpolation function in the direction; let The cubic spline interpolation function for the direction is: , The cubic spline interpolation function for the direction is: , The cubic spline interpolation function for the direction is: ,in, The independent variable representing the arc length of a 3D path point on a subinterval is... , , , express The coefficients of the cubic spline interpolation function; , , , and , , , They are and The coefficient; Step 3, for , , Construct a system of linear equations for each direction, solve the system of linear equations, and obtain the coefficients of the spline function; Step 4: For each sub-interval , respectively in , , Direction using cubic spline function The fitted path is generated, and multiple points are taken within the sub-interval according to the accuracy requirements. The coordinates of these points are calculated, and the fitted smooth path is obtained by connecting these three-dimensional points.

4. A UAV path planning system based on radar omnidirectional detection and an improved A* algorithm, characterized in that, include: The elevation cost matrix calculation unit is used to downsample the elevation topographic map and calculate the elevation cost matrix of the entire topographic map, enabling subsequent UAVs to choose whether to bypass or traverse terrain obstacles; it includes: Assume the elevation matrix of the topographic map is ,in To represent the number of grid cells in an elevation topographic map, first let the base matrix... equal ; Then expand according to the following formula. ,exist Extend the surrounding area by 1 order and assign a value of This means setting the altitude of the flight path exceeding the topographic map to infinity, restricting the drone's flight to within the topographic map area, and designating the portion exceeding the topographic map as a no-fly zone; the expanded matrix representation is as follows: ； Among them, the elements in the matrix This indicates the elevation of each grid point on the topographic map; Define the UAV as flying from the starting point to the ending point on a topographic map. At each two-dimensional plane grid point, it can fly in eight directions: forward, backward, left, right, and diagonally. Calculate the UAV's flight path along the [missing information - likely a specific direction or path]. The elevation cost matrix for each direction of flight is: ,in It is a matrix sum matrix The overlap matrix, Determine the elevation difference cost of a UAV flying to any point in a specified direction on a topographic map; The target detection and power allocation unit is used to detect dynamic threat targets in the surrounding environment using radar omnidirectional detection technology, and to obtain information on the speed, direction and position of the threat targets, and to allocate power to the dynamic threats. The path planning unit is used to plan the flight path of the UAV using an improved A* algorithm based on the acquired dynamic threat target information, obtaining the original path of the UAV on the elevation topographic map, and saving the original path as a series of three-dimensional discrete path points; including: Step 1: Input the starting grid coordinates of the UAV on the elevation topographic map. End point grid coordinates and the flight speed of drones ; Step 2: Create the OPEN table. The CLOSE table is In each step of the search, the current node is calculated. loss function ,in It is the cost of the journey already taken. This represents the distance from the current node to the destination. The number of nodes; the height of the current node. Greater than the height of the previous node At that time, that is The cost of the journey already completed is ,otherwise ,in, , These represent the drones heading towards direction and The distance difference during directional flight; Step 3, when When not empty, find loss function Find the smallest point, set that point as the current node, and start from... Remove from the middle if the current node is equal to , return by The generated path, otherwise add the current node. ; Step 4: Calculate the eight nearest neighbors of the current node in the front, back, left, right, and diagonal directions, and compare their loss functions. and elevation cost matrix C k If a neighboring point is neither obstructed nor located within a no-fly zone, meaning the neighboring point is valid, then it is determined whether the neighboring point is within a no-fly zone. In the middle, if the neighboring point is already In the middle, check if there is a smaller one. Value, if available, update If a point is a neighboring point, then add the neighboring point to the list. middle; Step 5: Repeat steps 3 and 4 until found. or Until it is empty; The path smoothing unit is used to smooth a series of three-dimensional discrete path points obtained by using a cubic spline curve fitting algorithm, so that they are more consistent with the actual flight trajectory.

5. The UAV path planning system based on radar omnidirectional detection and improved A* algorithm according to claim 4, characterized in that, A cubic spline curve fitting algorithm is used to smooth a series of three-dimensional discrete path points, including the following steps: Step 1: Extract N discrete path points from a series of 3D discrete path points saved in the original path, and represent the path points as 3D coordinates. ,in, This represents the position coordinates of the path point in the three-dimensional coordinate system. ; Step 2: Define the sub-interval between two adjacent path points as... ,in, Indicates the distance from the starting point to the... The cumulative distance of the arc length of each path point Indicates the distance from the starting point to the... The cumulative distance of the arc length of each path point; in each sub-interval Above, respectively for , , Construct a cubic spline interpolation function in the direction; let The cubic spline interpolation function for the direction is: , The cubic spline interpolation function for the direction is: , The cubic spline interpolation function for the direction is: ,in, The independent variable representing the arc length of a 3D path point on a subinterval is... , , , express The coefficients of the cubic spline interpolation function; , , , and , , , They are and The coefficient; Step 3, for , , Construct a system of linear equations for each direction, solve the system of linear equations, and obtain the coefficients of the spline function; Step 4: For each sub-interval , respectively in , , Direction using cubic spline function The fitted path is generated, and multiple points are taken within the sub-interval according to the accuracy requirements. The coordinates of these points are calculated, and the fitted smooth path is obtained by connecting these three-dimensional points.

6. An electronic device, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the steps of the UAV path planning method based on radar omnidirectional detection and improved A* algorithm as described in any one of claims 1-3.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked, are used to execute the steps of the UAV path planning method based on radar omnidirectional detection and improved A* algorithm as described in any one of claims 1-3.

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