Path planning method for UAV in power transmission and distribution transformer based on improved RRT algorithm and Voronoi diagram

Abstract

A transmission and distribution transformer UAV path planning method based on an improved RRT algorithm combined with a Voronoi diagram includes: defining the corresponding threat level based on the magnetic field distribution of inspection equipment; generating a Voronoi diagram based on the location of the inspection equipment and weighting the Voronoi diagram based on the threat level information; improving the path planning algorithm by using the intersection of the weighted Voronoi diagram boundary as the extension node of the RRT algorithm, selecting the Voronoi diagram point closest to the current node as the next node, so that the tree structure generated by the RRT algorithm extends along the boundary of the Voronoi diagram; introducing informed sampling based on the RRT algorithm, constraining random points within an ellipse, and then combining the RRT* algorithm to reselect parent nodes and reroute the route; using the triangle optimization method to optimize redundant nodes in the planned path; and smoothing the generated path to further optimize the UAV path. This method plans a suitable path while ensuring that the UAV maintains a safe distance, avoiding collisions with obstacles.

Description

Technical Field

[0001] The present invention relates to the technical field of inspection drone path planning, and in particular to a transmission and distribution transformer drone path planning method based on a combination of an improved RRT algorithm and a Voronoi diagram. Background Art

[0002] The deployment environments of my country's power lines vary widely, but most are exposed to the elements for extended periods, subject to numerous adverse weather conditions and human activity. Regular inspections of power systems can promptly identify and resolve these issues, ensuring the stability and safety of power system operations. Regular inspections, special inspections, and fault inspections are crucial for preventive maintenance. With the continuous advancement of industrial drone technology, the power industry has become proficient in the use of drones, currently primarily used for transmission line inspections. Drones offer significant advantages for line inspections, enabling continuous power outages, reducing labor costs, and improving inspection efficiency. Drone path planning involves planning a path that, given a starting point and a destination, satisfies flight requirements while avoiding threats and completing the mission.

[0003] Drones offer advantages such as high flight maneuverability, high inspection efficiency, and short observation range. Therefore, using drones for substation inspections effectively addresses the visual blind spots of intelligent inspection robots. However, these also present numerous safety risks. For example, complex, strong electromagnetic fields can disrupt the normal operation of drones, disrupting communications and distorting collected data. Drones flying close to high-voltage electrodes can cause discharges and damage the drone. Passing between high-voltage electrodes can cause electric field distortion and insulation breakdown. Inspections often involve numerous, densely packed, and complex environments, with strong electromagnetic coupling between live equipment. These complex, strong electromagnetic environments place even higher demands on drone safety. If a drone flies too close to live objects, it could cause discharges in metal or exposed parts of the drone, potentially damaging the drone or causing it to lose control and lead to accidents. Therefore, the effectiveness of drone path planning can impact the effectiveness of intelligent drone inspections. If autonomous drone inspections of power towers are to be achieved, careful route planning is essential. The goal of path planning is to find an optimal collision-free path from the initial state to the target state based on some performance indicators such as shortest time, shortest path length, and least cost, taking into account complex environments, geometric factors, physical properties and time constraints.

[0004] At present, many academic achievements have been made in the research on path planning at home and abroad. The research methods of path planning are mainly divided into sampling-based path, search-based (A* algorithm, genetic algorithm) path planning and path planning based on intelligent optimization algorithm. Among them, the most widely used is the sampling-based rapidly expanding random tree (RRT) algorithm. The RRT algorithm has obvious advantages in planning efficiency in high-dimensional space. However, due to the randomness of sampling in the algorithm, the paths planned by the algorithm are inconsistent each time, the optimal path cannot be obtained, and the path planning speed is slow. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a transmission and distribution transformer UAV path planning method based on the combination of an improved RRT algorithm and a Voronoi diagram. It plans a suitable path while ensuring that the UAV maintains a safe distance, avoids collisions between the UAV and obstacles, and improves the speed of UAV path planning and efficiency.

[0006] The technical solution adopted by the present invention is:

[0007] A transmission and distribution UAV path planning method based on the improved RRT algorithm combined with the Voronoi diagram is characterized by comprising the following steps:

[0008] Step 1: Model the environment of the inspection equipment on the transmission and distribution lines, and define the corresponding threat level based on the magnetic field distribution of the inspection equipment;

[0009] Step 2: Generate a Voronoi diagram based on the location of the inspection equipment and perform weighted processing on the Voronoi diagram based on the threat level information;

[0010] Step 3: Improve the path planning algorithm by using the intersection of the weighted Voronoi diagram boundary as the extension node of the RRT algorithm. In the RRT algorithm extension step, select the Voronoi diagram point closest to the current node as the next node, so that the tree structure generated by the RRT algorithm is extended along the boundary of the Voronoi diagram.

[0011] Step 4: Introduce informed sampling based on the RRT algorithm to constrain the random points within the ellipse, and then combine the RRT* algorithm to reselect the parent node and rewire;

[0012] Step 5: Use the triangle optimization method to optimize the redundant nodes of the planned path to improve the path quality;

[0013] Step 6: Smooth the generated path to further optimize the drone path.

[0014] In step 1, when inspecting power system transmission and distribution lines or substations, it is necessary to monitor the operating status and potential faults of equipment such as transformers, switchgear, relay protection devices, cables, and overhead lines. Each inspection device, obstacle, passage, and key location should be marked on the inspection map. In addition to the impact of visible physical obstacles on drone path planning, the impact of invisible magnetic fields on the drone's flight path also needs to be considered. The magnetic field strength of each inspection device is collected through sensors and historical data, and recorded as the threat level of the inspection device.

[0015] In step 2, a weighted Voronoi diagram is used to reflect the difference in the impact of each obstacle on the drone. The improved Delaunay triangle side length calculation formula is:

[0016]

[0017] In formula (1), A, B, and C are three obstacles, each with different degrees of influence on the UAV; △ABC is a Delaunay triangle; L AB is the distance between A and B; T A and T B are the magnetic field magnitudes of A and B respectively; similarly determine the positions of points F and E;

[0018] Connect D, E, and F to get the segmented triangle △DEF, and make an inscribed circle. The lines OD, OE, and OF that are connected to the center of the circle are the edges of the weighted Voronoi diagram. Figure 2 shown.

[0019] In step 3, in the RRT algorithm expansion step, after generating potential nodes, all Voronoi nodes are extracted from the Voronoi diagram. These Voronoi nodes serve as potential expansion nodes of the RRT tree to guide the growth of the tree.

[0020] For the current node, the Voronoi diagram point closest to the current node is selected as the next node to be expanded, and this node is given priority, so that the tree structure generated by the RRT algorithm will be more inclined to expand along the boundary of the Voronoi diagram, thereby improving the safety of the path.

[0021] In step 3, the current node P c The closest Voronoi node P in Voronoi v The distance between them is:

[0022]

[0023] In the above formula, d(P c ,P v ) represents the previous node P cThe closest Voronoi node P in Voronoi v the distance between them;

[0024] x v Represents the Voronoi node P v The horizontal axis of

[0025] x c Represents the current node P c The horizontal axis of

[0026] y v Represents the Voronoi node P v The vertical coordinate of

[0027] y c Represents the current node P c The vertical coordinate of .

[0028] Traverse all nodes in the Voronoi diagram and calculate the value of the node P c The distance between the two nodes is 1, and the node with the smallest distance is selected as the target Voronoi node P. nearest .

[0029]

[0030]

[0031] P new =( x +Δd·v x ,y c +Δd·v y ) (7);

[0032] In the above formula, v is the current node P c To the target Voronoi node P nearest The unit vector of

[0033] x nearest Represents the distance to the current node P c The nearest Voronoi node P nearest The horizontal axis of

[0034] y nearest Represents the distance to the current node P c The nearest Voronoi node P nearest The vertical coordinate of

[0035] x c Represents the current node P c The horizontal axis of

[0036] y c Represents the current node P cThe vertical coordinate of

[0037] v x represents the horizontal coordinate of the unit vector;

[0038] v y Represents the ordinate of a unit vector.

[0039] Get the new node P by step size Δd new . The new node P new Add to the RRT tree and add P current Set as the parent node.

[0040] The new node P new Set it as the current node for the next expansion, and repeat the above operation until the target node is reached. Step 4 includes improving the path planning Informed-RRT* algorithm, as follows:

[0041] For the sampling area, a local elliptical sampling method is used instead of global uniform sampling. By shrinking the sampling area into an elliptical area, a large number of meaningless sampling points are avoided. As the path length shortens, the elliptical sampling area gradually shrinks until the path optimization is completed:

[0042] The ellipse region of the Informed-RRT* algorithm takes the path starting point and the path target point as the focus, and the initial sampling ellipse equation is:

[0043]

[0044] In the above formula, a represents the length of the major axis of the ellipse; b represents the length of the minor axis of the ellipse.

[0045] According to the rotation and translation matrix, the sampling point ellipse coordinates are converted to actual coordinates:

[0046] [x'y']=R·[xy]+T (3);

[0047] Among them, x represents the actual X-axis coordinate after coordinate transformation; y' represents the actual Y-axis coordinate after coordinate transformation. Translation matrix T = [x c y c ], θ represents the angle between the line connecting the two foci of the ellipse and the actual coordinate system X; x c Indicates the amount of translation in the horizontal direction; y c Indicates the amount of translation in the vertical direction.

[0048] In step 4, the RRT* algorithm is used to reselect the parent node and rewire the network, as follows:

[0049] Reselect the parent node mainly with x newAs the center of the circle, within a fixed radius, find all adjacent nodes. These nodes within the range will be used as the backup parent nodes of the new node. Calculate the value of the path after each backup parent node is connected. After comparing the path costs, select the backup parent node with the smallest path cost as the final parent node. The parent node is replaced by the original x nearest Become the new parent node x min .like Figure 3 As shown, the original path is A→C→F→J, with J as the origin, the fixed path as the radius, and the backup nodes as E, F, I, and G. After comparing the path costs, E is selected as the new parent node.

[0050] After completing the reselection of the parent node, perform the rewiring operation and put x new As the parent nodes of the remaining nodes in the circle, and calculate each node with x new As parent node to x start Is the path cost less than the original path cost? If so, choose x new The parent node of this node, otherwise the child nodes are not updated. Figure 3 As shown, for node F, comparing A→C→F and A→E→J→F, the original path is shorter and is not updated; for node I, comparing paths A→E→J→I and A→C→F→I, the updated path is shorter, so node I is taken as x new 's child nodes.

[0051] In step 5, the triangle optimization method is used to optimize the redundant nodes of the planned path to improve the path quality. Specifically, the specific process of the triangle optimization method is as follows:

[0052] Name the nodes in the path [X start , X1, X2...X k-1 、X k 、X goal ], set the starting point X start The length of the side between node X1 and node X2 is recorded as a, the length of the side between node X1 and node X2 is recorded as b, and the length of the side between node X2 and starting point X start The side length is c, forming a triangle;

[0053] Find the first triangle node X in the initial path where the edge length c collides with the obstacle k ;

[0054] Node X k-1 With node X k The connection between them is discretely split into n segments;

[0055] From the starting point X start Perform collision detection with discrete points P in turn and find the child node X of the first collision node in the set P. stemp;

[0056] X stemp Update to the new starting node;

[0057] Follow the above steps, such as Figure 4 As shown, the three black circles represent obstacles, the black path is the initial path, and the path has 9 path nodes from the starting point to the target point; the first triangle from the starting point is X start X1X2, such as Figure 4 As shown by the green dotted line in the middle, the long side of the triangle does not collide with the obstacle, so we continue to detect the second triangle X starting from the starting point. start X2X3, such as Figure 4 As shown by the red dotted line, the long side of the triangle collides with the obstacle, then X3 is the X in the above step. k , X2 is X k Child node X k-1 ; Discrete splitting is performed on the line connecting X2-X3 to obtain a series of discrete points P, such as Figure 4 As shown by the red dot on the X2-X3 line;

[0058] The discrete split expression is as follows:

[0059] P=(x k-1 ,y k-1 )+[(x k ,y k )-(x k-1 ,y k-1 )]×θ

[0060] =(x k-1 ,y k-1 )+(x k ,y k )·θ-(x k-1 ,y k-1 )·θ

[0061] =(1-θ)·(x k-1 ,y k-1 )+θ·(x k ,y k ) (8)

[0062] Among them, the value of θ is θ is the discrete step length, which is used to control the discrete splitting of the line segment into n segments;

[0063] (x k-1 ,y k-1 )、(x k ,y k ) are the coordinates of the path nodes;

[0064] P is the node set after the line segment is discretely split, which is used for collision detection with the starting point.

[0065] Set the starting point X start Perform collision detection with discrete points in turn and find the child node of the first collision node in the set P, which is X in the figure stemp1 ; At this time, the path is X start -X1-X2-X3 updated to X start -X stemp1 , that is, the redundant nodes X1 and X2 are deleted; then X stemp1 Update to the new starting point and repeat the above steps until the target point X goal , you can delete the redundant nodes in the initial path, optimize the tortuosity of the path, and reduce the length of the path; the path after triangle optimization is as follows Figure 4 As shown in the yellow path, X start -X stemp1 -X stemp2 -X goal .

[0066] In step 6, a cubic B-spline curve is used to smooth the path; the details are as follows:

[0067] The B-spline curve expression is

[0068]

[0069] Where: P i Indicates the curve control point; F i,k (t) represents the k-order B-spline basis function, where k=3.

[0070] The basis function expression of the cubic B-spline curve is:

[0071]

[0072] Substituting equation (10) into equation (9), we finally get the cubic B-spline curve expression:

[0073] P(t)=P0*F 0,3 (t)+P1*F 1,3 (t)+P2*F 2,3 (t)+P3*F 3,3 (t) (11)

[0074] After the curve control points are given, a smooth cubic B-spline curve can be obtained, such as Figure 7 shown.

[0075] The present invention provides a transmission and distribution UAV path planning method based on the combination of an improved RRT algorithm and a Voronoi diagram, and the technical effects are as follows:

[0076] 1) The method of the present invention simplifies the search nodes in path planning, reduces the calculation amount of the RRT algorithm, and improves planning efficiency.

[0077] 2) The present invention takes into account the need to maintain a safe distance between drones and live equipment during power inspections due to the concentration of equipment and strong electromagnetic coupling. By preprocessing the map using a weighted Voronoi diagram, the impact of live equipment on the drone is avoided, obstacles are effectively avoided, and the safety of the inspection path is ensured.

[0078] 3) The RRT algorithm of the present invention gives priority to the intersection of the Voronoi diagram boundary when selecting new nodes, and then improves the RRT global uniform sampling strategy to Informed ellipse sampling, which narrows the sampling range of the RRT algorithm and improves the path planning speed.

[0079] 4) The present invention uses B-spline curves to smooth the sudden change of the path curvature, reduce the sharp turning of the UAV during flight, and ensure the safety and stability of the UAV.

[0080] 5) Based on the RRT algorithm, the present invention optimizes the selection and sampling methods of extended nodes, improves the path planning efficiency, shortens the path planning length, realizes rapid UAV path planning, and meets the UAV flight dynamics requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] The present invention will be further described below with reference to the accompanying drawings and examples:

[0082] Figure 1 This is a flow chart of the drone path planning of the present invention.

[0083] Figure 2 Schematic diagram of the improved weighted Voronoi diagram.

[0084] Figure 3 Schematic diagram of reselecting parent nodes and rewiring.

[0085] Figure 4 Schematic diagram of the triangle optimization process.

[0086] Figure 5 This is the traditional Voronoi diagram after preprocessing of the 100×100km inspection map.

[0087] Figure 6 is the weighted Voronoi diagram.

[0088] Figure 7 is a cubic B-spline curve.

[0089] Figure 8(a) shows the layout of a 500kV substation Figure 1 ;

[0090] Figure 8(b) shows the layout of the 500kV substation. DETAILED DESCRIPTION

[0091] A transmission and distribution transformer UAV path planning method based on the combination of an improved RRT algorithm and a Voronoi diagram. This method first models the environment of the equipment that needs to be inspected on the transmission and distribution transformer line, and defines the corresponding threat level based on the magnetic field distribution of the equipment. The map is processed by a weighted Voronoi diagram to extract the guide points for path planning, and the global path guide points of the RRT algorithm are generated accordingly. The present invention innovates the path planning algorithm, and uses the boundary intersection of the Voronoi diagram as the node of the RRT algorithm. In the RRT expansion step, the Voronoi diagram point closest to the current node is selected as the next node, so that the tree structure generated by the RRT algorithm will be more inclined to expand along the boundary of the Voronoi diagram. On the basis of the RRT algorithm, Informed sampling is introduced to constrain random points within the elliptical range, and then combined with RRT* to reselect parent nodes and rewire, and finally the generated path is smoothed to further optimize the UAV path.

[0092] The transmission and distribution UAV path planning method based on the improved RRT algorithm combined with the Voronoi diagram is as follows: Figure 1 As shown, the following steps are included:

[0093] Step 1: Environment construction and map preprocessing:

[0094] In step 1, when inspecting power system transmission and distribution lines or substations, it is necessary to pay attention to the operating status and potential faults of equipment such as transformers, switchgear, relay protection devices, cables, and overhead lines. Each inspection device, obstacle, passage, and important location should be marked on the inspection map. Figures 8(a) and 8(b) show a 500kV substation layout. In addition to visible physical obstacles that drones cannot pass through and must detour, the impact of invisible magnetic fields generated by equipment such as transformers on the drone's flight path must also be considered, as shown in Table 1. The magnetic field strength of each inspection device is collected through sensors and historical data. This is recorded as the threat level of the device.

[0095] Table 1 The impact of magnetic field on the flight path of UAV

[0096] Magnetic field strength Impact on drones ≥50μT Inaccurate magnetometer readings, affecting drone stability and flight control ≥100μT May affect the signal quality of the GPS receiver, resulting in navigation errors ≥200μT Loss of control due to magnetic field interference leads to a crash

[0097] Step 2: Generate a Voronoi diagram based on the device location and perform weighted processing on the Voronoi diagram based on the threat level information:

[0098] Generate a Voronoi diagram based on the device location, such as Figure 5As shown in the figure, a conventional Voronoi diagram is generated using a 100×100 km map as an example. The blue dots represent 30 energized devices. This conventional Voronoi diagram assumes that the threat entities have equal strength. Each Voronoi edge is the perpendicular bisector of two adjacent threat points. Therefore, when a drone flies along a Voronoi edge, it does not deviate from either direction. However, during actual power system inspections, drones may be disturbed by strong electromagnetic fields, affecting the normal operation of their navigation and control systems.

[0099] Since not all devices have the same magnetic field strength on the drone, the weighted Voronoi diagram used in this invention can better reflect the difference in the impact of each obstacle on the drone. The improved Delaunay triangle side length calculation formula is:

[0100]

[0101] In the formula, A, B, and C are three obstacles with different threat levels to the drone, and △ABC is a Delaunay triangle. AB is the distance between A and B; T A and T B Determine the magnetic field magnitudes at A and B. Similarly, determine the locations of points F and E. Connect points D, E, and F to obtain the segmented triangle △DEF. Draw an inscribed circle. The lines OD, OE, and OF connecting the resulting triangle to the center of the circle are the edges of the improved weighted Voronoi diagram.

[0102] like Figure 6 As shown in the figure, the pink line is the weighted Voronoi diagram, and the solid pink line is more inclined to threat sources that pose less threat to drones. Compared with the traditional Voronoi diagram, it can better reflect the differences in the threats posed by various threat sources to drones.

[0103] Step 3: Improve the path planning Informed-RRT* algorithm:

[0104] The present invention adopts a local elliptical sampling method for the sampling area instead of global uniform sampling, and avoids generating a large number of meaningless sampling points by shrinking the sampling area into an elliptical area. As the path length shortens, the elliptical sampling area gradually shrinks until the path optimization is completed.

[0105] The ellipse region of the Informed-RRT* algorithm takes the path starting point and the path target point as the focus, and the initial sampling ellipse equation is:

[0106]

[0107] According to the rotation and translation matrix, the sampling point ellipse coordinates are converted to actual coordinates:

[0108] [x' y']=R·[xy]+T (3);

[0109] The rotation matrix Translation matrix T = [x c y c ], θ represents the angle between the line connecting the two foci of the ellipse and the actual coordinate system X.

[0110] Step 4: Use the boundary intersection of the weighted Voronoi diagram as the potential expansion node of the RRT algorithm:

[0111] In the RRT expansion step, after generating potential nodes, all Voronoi nodes are extracted from the Voronoi diagram. These nodes are used as potential expansion nodes of the RRT tree to guide the growth of the tree. All Voronoi nodes are as follows Figure 3 As shown in the figure, for the current node, the Voronoi diagram node closest to the current node is selected as the next node to be expanded. This node is given priority, so that the tree structure generated by the RRT algorithm is more inclined to expand along the boundary of the Voronoi diagram, thereby improving the safety of the path.

[0112] Current node P c The closest Voronoi node P in Voronoi v The distance between them is:

[0113]

[0114] Traverse all nodes in the Voronoi diagram and calculate the c The distance between the two nodes is 1, and the node with the smallest distance is selected as the target Voronoi node P. nearest .

[0115]

[0116]

[0117] P new =(x c +Δd·v x ,y c +Δd·v y ) (7)

[0118] v is the current node P c To the target Voronoi node P nearest The unit vector of the node is obtained by step size Δd. new . The new node P new Add to the RRT tree and add P current Set as the parent node.

[0119] The new node Pnew Set it as the current node for the next expansion and repeat the above steps until the target node is reached.

[0120] This method guides the expansion direction of RRT nodes, solves the problem of a large number of redundant nodes in RRT, and can obtain a path faster and more stably when performing path planning in any environment.

[0121] Step 5: Reselect parent nodes and rewire the new nodes:

[0122] On the random tree planned by the RRT algorithm, the RRT* algorithm is used to reselect parent nodes and rewire when adding new nodes, so as to select nodes with higher quality and achieve the purpose of reducing path costs.

[0123] Step 5: Use the triangle optimization method to optimize the redundant nodes of the planned path to improve the path quality.

[0124] Step 6: Use cubic B-spline curve to smooth the path:

[0125] Because the paths ultimately planned using the improved RRT algorithm in this invention are composed of random nodes and, due to limitations on the expansion step size, result in tortuous paths that do not conform to the drone's actual flight trajectory, reducing its operational efficiency and stability. Therefore, a cubic B-spline curve is used to smooth the resulting path curvature. The characteristics of each generated path are recorded and compared in Table 2.

[0126] Table 2 Performance comparison of RRT, RRT* and improved RRT of the present invention under the same environment

[0127] algorithm Time consumed / s Path cost / km Number of nodes RRT 0.456 221.8 28.8 RRT* 0.961 201.3 32.3 The present invention improves the RRT algorithm 0.378 168.5 15.8

[0128] As can be seen from Table 2, compared with the traditional RRT algorithm and the RRT* algorithm, the improved RRT algorithm of the present invention not only takes the shortest time, but also generates the path with the lowest cost and requires the least number of nodes, which proves the superiority and effectiveness of the algorithm.

Claims

1. A transmission and distribution UAV path planning method based on the improved RRT algorithm combined with the Voronoi diagram is characterized by The following steps are involved: Step 1: Model the environment of the inspection equipment on the transmission and distribution lines, and define the corresponding threat level based on the magnetic field distribution of the inspection equipment; Step 2: Generate a Voronoi diagram based on the location of the inspection equipment and perform weighted processing on the Voronoi diagram based on the threat level information; Step 3: Improve the path planning algorithm by using the intersection of the weighted Voronoi diagram boundary as the extension node of the RRT algorithm. In the RRT algorithm extension step, select the Voronoi diagram point closest to the current node as the next node, so that the tree structure generated by the RRT algorithm is extended along the boundary of the Voronoi diagram. Step 4: Introduce informed sampling based on the RRT algorithm to constrain the random points within the ellipse, and then combine the RRT* algorithm to reselect the parent node and rewire; Step 5: Use triangle optimization method to optimize the redundant nodes of the planned path; Step 6: Smooth the generated path to further optimize the drone path.

2. The transmission and distribution transformer UAV path planning method based on the improved RRT algorithm combined with the Voronoi diagram according to claim 1 is characterized by: In step 1, when inspecting power system transmission and distribution lines or substations, it is necessary to pay attention to the operating status and potential faults of transformers, switchgear, relay protection devices, cables and overhead line equipment, and mark each inspection equipment, obstacle, channel and important location on the inspection map; including the impact of visible physical obstacles on drone path planning, and the impact of invisible magnetic field size on drone flight path; collect the magnetic field size of each inspection equipment through sensors and historical data, and record it as the threat level of the inspection equipment.

3. The transmission and distribution transformer UAV path planning method based on the improved RRT algorithm combined with the Voronoi diagram according to claim 1 is characterized by: In step 2, a weighted Voronoi diagram is used to reflect the difference in the impact of each obstacle on the drone. The improved Delaunay triangle side length calculation formula is: In formula (1), A, B, and C are three obstacles, each with different degrees of influence on the UAV; △ABC is a Delaunay triangle; L AB is the distance between A and B; T A and T B are the magnetic field magnitudes of A and B respectively; similarly determine the positions of points F and E; Connect D, E, and F respectively to obtain the segmented triangle △DEF, draw an inscribed circle, and the lines OD, OE, and OF connecting the obtained triangle and the center of the circle are the edges of the weighted Voronoi diagram.

4. The transmission and distribution transformer UAV path planning method based on the improved RRT algorithm combined with the Voronoi diagram according to claim 1 is characterized by: In step 3, in the RRT algorithm expansion step, after generating potential nodes, all Voronoi nodes are extracted from the Voronoi diagram. These Voronoi nodes serve as potential expansion nodes of the RRT tree to guide the growth of the tree. For the current node, the Voronoi diagram point closest to the current node is selected as the next node to be expanded, and this node is given priority, so that the tree structure generated by the RRT algorithm will be more inclined to expand along the boundary of the Voronoi diagram, thereby improving the safety of the path.

5. The transmission and distribution transformer UAV path planning method based on the improved RRT algorithm combined with the Voronoi diagram according to claim 4 is characterized by: Current node P c The closest Voronoi node P in Voronoi v The distance between them is: In the above formula, d(P c ,P v ) represents the previous node P c The closest Voronoi node P in Voronoi v the distance between them; x v Represents the Voronoi node P v The horizontal axis of x c Represents the current node P c The horizontal axis of y v Represents the Voronoi node P v The vertical coordinate of y c Represents the current node P c The vertical coordinate of Traverse all nodes in the Voronoi diagram and calculate the value of the node P c The distance between the two nodes is 1, and the node with the smallest distance is selected as the target Voronoi node P. nearest ; P new =(x c +Δd·v x ,y c +Δd·v y ) (7); In the above formula, v is the current node P c To the target Voronoi node P nearest The unit vector of x nearest Represents the distance to the current node P c The nearest Voronoi node P nearest The horizontal axis of y nearest Represents the distance to the current node P c The nearest Voronoi node P nearest The vertical coordinate of x c Represents the current node P c The horizontal axis of y c Represents the current node P c The vertical coordinate of v x represents the horizontal coordinate of the unit vector; v y represents the ordinate of the unit vector; Get the new node P by step size Δd new ; The new node P new Add to the RRT tree and add P current Set as the parent node; set the new node P new Set as the current node for the next expansion; Repeat the above steps until the target node is reached.

6. The transmission and distribution transformer UAV path planning method based on the improved RRT algorithm combined with the Voronoi diagram according to claim 1 is characterized by: Step 4 includes improving the path planning Informed-RRT* algorithm, as follows: For the sampling area, a local elliptical sampling method is used instead of global uniform sampling. By shrinking the sampling area into an elliptical area, a large number of meaningless sampling points are avoided. As the path length shortens, the elliptical sampling area gradually shrinks until the path optimization is completed: The ellipse region of the Informed-RRT* algorithm takes the path starting point and the path target point as the focus, and the initial sampling ellipse equation is: In the above formula, a represents the length of the major axis of the ellipse; b represents the length of the minor axis of the ellipse; According to the rotation and translation matrix, the sampling point ellipse coordinates are converted to actual coordinates: [x' y']=R·[xy]+T (3); Among them, x' represents the actual X-axis coordinate after coordinate transformation; y' represents the actual Y-axis coordinate after coordinate transformation; rotation matrix Translation matrix T = [x c y c ], θ represents the angle between the line connecting the two foci of the ellipse and the actual coordinate system X; x c Indicates the amount of translation in the horizontal direction; y c Indicates the amount of translation in the vertical direction.

7. The transmission and distribution transformer UAV path planning method based on the improved RRT algorithm combined with the Voronoi diagram according to claim 6 is characterized by: In step 4, the RRT* algorithm is used to reselect the parent node and rewire the network, as follows: Reselect the parent node mainly with x new As the center of the circle, within a fixed radius, find all adjacent nodes. These nodes within the range will be used as the backup parent nodes of the new node. Calculate the value of the path after each backup parent node is connected. After comparing the path costs, select the backup parent node with the smallest path cost as the final parent node. The parent node is replaced by the original x nearest Become the new parent node x min ; After completing the reselection of the parent node, perform the rewiring operation and put x new As the parent nodes of the remaining nodes in the circle, and calculate each node with x new As parent node to x start Is the path cost less than the original path cost? If so, choose x new The parent node of this node, otherwise the child nodes are not updated.

8. The transmission and distribution transformer UAV path planning method based on the improved RRT algorithm combined with the Voronoi diagram according to claim 1 is characterized by: In step 5, the triangle optimization method is used to optimize the redundant nodes of the planned path. The specific process is as follows: Name the nodes in the path [X start , X1, X2...X k-1 、X k 、X goal ], set the starting point X start The length of the side between node X1 and node X2 is recorded as a, the length of the side between node X1 and node X2 is recorded as b, and the length of the side between node X2 and starting point X start The side length is c, forming a triangle; Find the first triangle node X in the initial path where the edge length c collides with the obstacle k ; Node X k-1 With node X k The connection between them is discretely split into n segments; From the starting point X start Perform collision detection with discrete points P in turn and find the child node X of the first collision node in the set P. stemp ; X stemp Update to the new starting node.

9. The transmission and distribution transformer UAV path planning method based on the improved RRT algorithm combined with the Voronoi diagram according to claim 8 is characterized by: The discrete split expression is as follows: P=(x k-1 ,and k-1 )+[(x k ,and k )-(x k-1 ,and k-1 )]×θ (x k-1 ,y k-1 )+(x k ,y k )·θ−(x k-1 ,y k-1 )·θ =(1-θ)·(x k-1 ,y k-1 )+θ·(x k ,y k ) (8) Among them, the value of θ is θ is the discrete step length, which is used to control the discrete splitting of the line segment into n segments; (x k-1 ,y k-1 )、(x k ,y k ) are the coordinates of the path nodes; P is the node set after the segment is discretely split, which is used for collision detection with the starting point; Set the starting point X start Perform collision detection with discrete points in turn and find the child node of the first collision node in the set P, which is X in the figure stemp1 ; At this time, the path is X start -X1-X2-X3 updated to X start -X stemp1 , that is, the redundant nodes X1 and X2 are deleted; then X stemp1 Update to the new starting point and repeat the above steps until the target point X goal , you can delete the redundant nodes in the initial path, optimize the tortuosity of the path, and reduce the length of the path; the path after triangle optimization is X start -X stemp1 -X stemp2 -X goal .

10. The transmission and distribution transformer UAV path planning method based on the improved RRT algorithm combined with the Voronoi diagram according to claim 1 is characterized by: In step 6, a cubic B-spline curve is used to smooth the path; specifically, the B-spline curve expression is: Where: P i Indicates the curve control point; F i,k (t) represents the k-order B-spline basis function; The basis function expression of the cubic B-spline curve is: Substituting equation (10) into equation (9), we finally get the cubic B-spline curve expression: P(t)=P0*F 0,3 (t)+P1*F 1,3 (t)+P2*F 2,3 (t)+P3*F 3,3 (t) (11) After the curve control points are given, a smooth cubic B-spline curve is obtained.

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