A power inspection unmanned aerial vehicle path planning method and system considering communication constraints

Abstract

The application discloses a power inspection unmanned aerial vehicle path planning method and system considering communication constraints, and belongs to the technical field of unmanned aerial vehicle autonomous control, and comprises the following steps: a comprehensive track cost function is constructed, the function innovatively introduces a communication quality cost based on accurate channel modeling and a penalty accumulation mechanism, the throughput of each point is calculated by sampling along the path, and an increasing penalty is applied to the points with continuous poor communication, so that the communication blind area is actively avoided in planning, and the improved grey wolf optimization algorithm is adopted for path solving, the global optimization capability and stability are improved through a nonlinear convergence factor, a mutation operation and an optimization leader wolf updating mechanism, compared with the prior art, the optimal path can be planned, the optimal path can safely avoid obstacles and cover all inspection points in complex environments such as mountainous areas and urban areas, and the communication quality of the whole path can ensure that the image transmission demand is met, and the reliability and efficiency of power inspection are greatly improved.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) autonomous control and path planning technology, specifically relating to a UAV path planning method and system for power line inspection in complex environments (such as mountainous areas and urban areas), which comprehensively considers communication quality, obstacle avoidance, inspection target coverage, and path length. Furthermore, this invention relates to the engineering optimization application of intelligent optimization algorithms under multiple constraints. Background Technology

[0002] Unmanned aerial vehicle (UAV) power line inspection is gradually replacing traditional, high-risk, and inefficient manual inspection methods due to its flexibility, efficiency, and low cost. Path planning is a core technology for achieving autonomous UAV inspection. Currently, research on path planning algorithms in this field mainly focuses on the following categories: traditional graph search-based algorithms (such as Dijkstra's algorithm and A* algorithm), random sampling-based algorithms (such as Fast Random Tree Search (RRT)), and biomimetic intelligent algorithms (such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The optimization objectives of these algorithms are primarily focused on minimizing path length, reducing flight energy consumption, and addressing physical and safety constraints such as static / dynamic obstacle avoidance.

[0003] However, power line inspection tasks place extremely high demands on the quality and real-time performance of the inspection images transmitted by UAVs (such as high-resolution images of equipment defects and infrared thermal imaging). This directly depends on a stable and high-speed communication link between the UAV and the ground control station or base station. In actual flight environments, especially in mountainous areas with undulating terrain or densely built-up urban areas, the communication link between the UAV and the base station is easily degraded due to terrain obstructions (creating a non-line-of-sight (NLoS) environment), excessive distance, or signal multipath fading. This manifests as decreased receiving power, reduced signal-to-noise ratio, and insufficient throughput, ultimately leading to a decrease in transmission rate, interrupted image transmission, or unclear images, causing the inspection work to fail.

[0004] Existing technologies contain some solutions that attempt to address communication issues, but these have significant shortcomings:

[0005] (1) Insufficient attention: Most path planning schemes do not incorporate communication quality as a core, quantitative constraint into the planning model, but only as a post-event verification or secondary factor.

[0006] (2) Model simplification: Although some studies have paid attention to this, they usually use overly simplified communication models (such as only considering line-of-sight / non-line-of-sight probabilities), which fail to deeply integrate with the real 3D environment and have poor accuracy.

[0007] (3) Insufficient integration: The communication constraints are not deeply and effectively integrated into the core of the optimization algorithm—the cost function. For example, only Boolean judgments (on / off) are made, without quantifying the “degree” and “duration” of poor communication, which makes it impossible for the algorithm to make a fine trade-off between slight communication problems and severe communication interruptions.

[0008] Therefore, there is an urgent need in this field for a UAV path planning solution that can systematically and meticulously address communication quality constraints and deeply integrate them with path planning, so as to fundamentally ensure the reliable transmission of power inspection data and the ultimate success of the mission. Summary of the Invention

[0009] Purpose of the invention: The present invention aims to solve the technical problem in the existing UAV power line inspection path planning method, which fails to fully consider or deeply integrate the communication quality constraints during flight, resulting in the inability to stably and effectively transmit key data such as inspection images, thus affecting the effectiveness of the inspection task.

[0010] Technical Solution: This invention proposes a path planning method and system for power line inspection drones considering communication constraints, comprising the following steps:

[0011] Step 1: Flight environment modeling, including 3D terrain modeling and air-to-ground channel modeling of the inspection area;

[0012] Step 2: Construct a comprehensive trajectory cost function with converged communication constraints, wherein the comprehensive trajectory cost function includes at least collision cost. Inspection target cost Path length cost and communication quality cost ;

[0013] Step 3: Use an optimization algorithm to optimize and solve the comprehensive trajectory cost function to obtain the optimal flight path;

[0014] Among them, the communication quality cost The calculations include:

[0015] Step 3.1) Sample along the planned path to obtain multiple sampling points;

[0016] Step 3.2) Based on the air-to-ground channel modeling, calculate the communication quality index for each sampling point;

[0017] Step 3.3) Compare the communication quality index with a preset threshold to identify sampling points with poor communication;

[0018] Step 3.4) Count the identified communication failure sampling points and calculate the communication quality cost based on the counting results. .

[0019] Furthermore, in step 1, the air-to-ground channel modeling adopts a channel model based on the 3GPP standard for mountainous scenarios and a channel simulation method based on ray tracing technology for dense urban scenarios.

[0020] Furthermore, the collision cost A segmented judgment method is adopted, imposing a 3x penalty for path nodes located inside obstacles, a 1x penalty for path segments intersecting with obstacle surfaces, and an cumulative penalty for a single line segment colliding with multiple obstacles; the inspection target cost is... By calculating the vertical distance from checkpoints to path nodes and each road segment, it is determined whether a checkpoint is covered; the number of uncovered checkpoints is included in the cost; the path length cost. This is used to calculate the sum of the Euclidean distances of all line segments along the path.

[0021] Furthermore, in step 3.4), a penalty accumulation mechanism is introduced for the sequence of consecutive communication failure sampling points. Specifically, for a sequence of consecutive communication failure sampling points, the cost count of the first point is... Cost count at the t-th point ,in For a predefined fixed increment, the penalty weight of the i-th communication failure sampling point in a continuous sequence of failure sampling points is higher than that of the (i-1)-th sampling point, where i is an integer greater than 1; the communication quality cost The product of the sum of the cost counts of all poorly communicating sampling points and a constant.

[0022] Furthermore, in step 3, the optimization algorithm used is an improved gray wolf optimization algorithm, and the improvement includes at least one of the following:

[0023] 1) Set the convergence factor 'a' of the Grey Wolf optimization algorithm to decrease non-linearly with the number of iterations t;

[0024] 2) After the population location is updated, with a certain probability Randomly mutate the nodes in the individual path;

[0025] 3) Change the original algorithm to force an update in each round. , , The wolf's strategy shifts to globally maintaining the three historically best solutions, updating the leader wolf's position only when a better solution is generated.

[0026] Furthermore, the convergence factor The update formula is:

[0027]

[0028] in, and It is a constant. This represents the maximum number of iterations.

[0029] Furthermore, the integrated trajectory cost function also includes an auxiliary cost function. It includes at least one of the following: path node aggregation cost Cost of abnormal road segment length Intersection cost ;

[0030] Path node aggregation cost This is used to penalize path nodes that are too close together. The path node aggregation cost constant. The number of path nodes that are too close together; the cost of abnormal road segment lengths. This is used to penalize abnormal path segment lengths, where The path length anomaly cost constant, Cost count for abnormal path lengths; segment intersection cost. This is used to penalize intersections where the projections of non-adjacent path segments on the horizontal plane intersect. Let be the intersection cost constant. This represents the total number of intersections on the road segment.

[0031] This invention also discloses a path planning system for power line inspection drones that considers communication constraints based on the above method, comprising:

[0032] The environment modeling module is used to perform the terrain modeling and channel modeling.

[0033] The cost function construction module is used to configure and calculate the integrated trajectory cost function;

[0034] The path optimization solution module is used to run the optimization algorithm and output the optimal path.

[0035] The path verification and output module is used to verify and output the final path result.

[0036] The present invention also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described path planning method for power line inspection drones that takes into account communication constraints.

[0037] Beneficial effects:

[0038] 1. This invention deeply integrates precise, environment-integrated channel modeling and a refined communication quality cost function into the core process of path planning, making communication constraints as important as constraints such as obstacle avoidance and inspection. The planned path can proactively bypass communication blind spots or weak areas, significantly reducing the risk of interrupted or poor-quality inspection image transmission. Experimental data shows that in mountainous and urban scenarios, the path using the method of this invention can reduce the duration of communication problems by 100% (mountainous areas) and more than 65% (urban areas) compared to paths that do not consider communication constraints.

[0039] 2. The improved gray wolf optimization algorithm used in this invention balances global exploration and local exploitation through a nonlinear convergence factor, increases diversity through a mutation mechanism, and ensures convergence stability through optimized leader wolf updates. This enables the algorithm to exhibit stronger optimization capabilities in complex, multi-peak solution spaces. Comparative experiments demonstrate that in dense urban scenarios, its path planning success rate (50%) is significantly higher than that of the traditional particle swarm optimization algorithm (10%) and genetic algorithm (28%).

[0040] 3. This invention does not solely pursue communication quality, but rather uses a weighted summation method to ensure communication quality (low quality). While ensuring path safety (zero collisions, low latency), it can still maintain path safety. ), Task integrity (full coverage, low ) and economic efficiency (shorter path, lower cost) This achieves an effective balance among multiple objectives, providing high-quality and highly reliable technical support for automated power line inspection.

[0041] 4. The method of this invention designs corresponding environmental models for both mountainous and urban complex scenarios, and the channel modeling method is also closely aligned with engineering practice. The cost function and algorithm improvements are universal and can be easily extended to other UAV application scenarios that require consideration of communication quality, such as logistics delivery and emergency communication. Attached Figure Description

[0042] Figure 1 The above is a flowchart of the power inspection route planning provided in the embodiments of the present invention.

[0043] Figure 2 A schematic diagram of a 3D terrain model for a mountainous scene.

[0044] Figure 3 Heatmaps of communication throughput at different heights (e.g., 10m, 20m, 30m, 40m) in mountainous scenes.

[0045] Figure 4 A schematic diagram of a 3D building model for an urban scene, constructed based on OpenStreetMap data.

[0046] Figure 5 Heatmap of communication throughput at different height levels (e.g., 20m, 30m, 40m) in urban scenarios.

[0047] Figure 6 A comparison chart showing the path planning results with and without communication constraints, as well as the communication conditions along the way (mountainous scene).

[0048] Figure 7 A comparison chart showing the path planning results with and without communication constraints, along with the communication conditions along the way (urban scene). Detailed Implementation

[0049] The present invention will be described in detail below with reference to the accompanying drawings and embodiments. This embodiment takes power line inspection route planning in a densely populated urban area as an example. For a detailed flowchart of the overall power line inspection route planning, please refer to [link to flowchart]. Figure 1 It includes the following steps:

[0050] Step 1: Detailed Modeling of the Flight Environment

[0051] Terrain modeling:

[0052] 1) Mountain modeling: Model the mountain peaks as cones, with the i-th peak defined by the coordinates of its base center. ,high any point on the terrain height For the superposition of the height fields of all mountain peaks, see [reference needed]. Figure 2 A 3D terrain modeling diagram of a mountain scene, showing the conical mountain peak, starting point, ending point, and checkpoint. Figure 3 Heatmaps of communication throughput at different heights (e.g., 10m, 20m, 30m, 40m) in mountainous scenes.

[0053] 2) Urban area modeling: Based on open street map data, buildings are... Modeled as a three-dimensional polygon, consisting of its base polygons and height Definition. By extracting building outlines and height information from .osm files, an accurate obstacle model is constructed in 3D space. Figure 4 A schematic diagram of a 3D building model for an urban scene, constructed based on OpenStreetMap data.

[0054] Air-to-ground channel modeling:

[0055] 1) Mountainous area channel modeling: A rural macrocell scenario channel model based on the 3GPP standard is adopted. This model calculates the line-of-sight probability. Path loss and shadow decay Finally, the received power and throughput at any point in space are derived. Preferably, to eliminate the impact of the randomness of shadow fading on the stability of the planning, uniform sampling and calculation are performed in the entire three-dimensional space beforehand to construct a definite shadow fading matrix, and the value of any point is obtained by interpolation during planning.

[0056] 2) Urban Channel Modeling: A channel simulation method based on ray tracing technology is adopted. Using MATLAB's ray tracing toolbox, a 3D urban model is imported, and base station parameters (location, transmit power, carrier frequency) are set. By simulating physical processes such as signal propagation reflection and diffraction, the received power at each point in space is accurately calculated. Similarly, a global received power matrix is ​​constructed through traversal sampling for later lookup.

[0057] Step 2: Construct a comprehensive trajectory cost function with converged communication constraints

[0058] Construct the trajectory cost function as follows: Used to quantitatively evaluate any path Advantages and disadvantages:

[0059]

[0060] The specific definitions of each cost term are as follows:

[0061] 1) Collision cost: A segmented judgment method is adopted, which applies a 3x penalty to the case where the path node is inside the obstacle, a 1x penalty to the case where the path segment intersects the surface of the obstacle, and an cumulative penalty to the case where a line segment collides with multiple obstacles.

[0062] 2) Inspection target cost: By calculating the vertical distance from checkpoints to path nodes and each road segment, it is determined whether a checkpoint is covered. The number of uncovered checkpoints is included in the cost.

[0063] 3) Path length cost: Calculate the sum of Euclidean distances for all segments of the path.

[0064] 4) The communication quality cost, which is calculated through the following refined process:

[0065] Path sampling: along the planned path at fixed intervals Dense sampling is performed at a distance of 15 meters to obtain a set of sampling points. .

[0066] Throughput calculation: for each sampling point Query the channel model constructed in S1.2) and calculate its communication throughput. .

[0067] Threshold comparison: Set a minimum throughput threshold. (e.g., 4 Mbps) to meet the requirements for high-definition image transmission.

[0068] Penalty accumulation mechanism: An incremental penalty weight is introduced for a sequence of consecutive poor communication sampling points. Specifically, for a sequence of consecutive poor communication sampling points, the cost count of the first point is... Cost count at the t-th point ,in For a predefined fixed increment, the penalty weight of the i-th communication failure sampling point in a sequence of consecutive communication failure sampling points is higher than that of the (i-1)-th sampling point, where i is an integer greater than 1. In this embodiment, for a sequence of consecutive communication failures, the cost count of the first point is... The second point The third point And so on. This mechanism effectively penalizes prolonged continuous communication interruptions, ensuring that the algorithm-planned path not only has fewer communication failure points, but also minimizes the duration of any communication failures, thereby improving the continuity of image transmission.

[0069] Final calculation: ,in , This is the communication quality cost constant.

[0070] Step 3: Path solving based on the improved Grey Wolf optimization algorithm

[0071] An improved gray wolf optimization algorithm is used to optimize the trajectory cost function in order to find a solution that makes the cost function more efficient. The minimum optimal path. Improvements include the following three points:

[0072] 1) Nonlinear improvement of convergence factor: The convergence factor is... From linear decrease Change to non-linear decreasing.

[0073]

[0074] in, and It is a constant. This represents the maximum number of iterations. For example... .

[0075] This approach causes the convergence factor to decrease rapidly in the early stages of iteration, facilitating a quick entry into global exploration. The decrease slows down in the later stages, allowing for more algebraic refinement of local searches near the optimal solution, thus increasing the probability of finding the global optimum.

[0076] 2) Introduce a mutation mechanism: After each round of population position updates, a probability-based mutation mechanism is introduced. Mutate random nodes in the individual paths (i.e., re-randomize them within the solution space). This operation simulates the mutation process in genetic algorithms, effectively enhancing population diversity and helping the algorithm escape local optima.

[0077] 3) Leader Wolf Update Mechanism Optimization: Change the original algorithm to force an update every round. , , The wolf's strategy shifts to globally maintaining the three historically best solutions. Updates are only performed when a newly generated solution is superior to the current leader wolf. This mechanism prevents the degradation of population guidance information caused by inferior solutions generated in a single iteration, ensuring the stability and convergence of the optimization process.

[0078] In this embodiment, an auxiliary cost function can also be introduced into the trajectory cost function. It is used to guide algorithms to converge faster in complex environments. This may include path node aggregation cost. Cost of abnormal road segment length Intersection cost Path node aggregation cost This is used to penalize path nodes that are too close together. The path node aggregation cost constant. Cost of abnormal road segment length This is used to penalize abnormal path segment lengths, where The path length anomaly cost constant, Cost count for abnormal path lengths; segment intersection cost. This is used to penalize intersections where the projections of non-adjacent path segments on the horizontal plane intersect. , where is the intersection cost constant. This represents the total number of intersections on the road segment.

[0079] The present invention also provides a system for implementing the above method, comprising:

[0080] The environment modeling module is used to perform the terrain modeling and channel modeling.

[0081] The cost function construction module is used to configure and compute the integrated track cost function. .

[0082] The path optimization solution module is used to run the improved Grey Wolf optimization algorithm and output the optimal path.

[0083] The path verification and output module is used to verify the feasibility of the path and output the results.

[0084] The core objective of this invention is to plan a path in a complex three-dimensional environment that not only safely avoids obstacles (collision cost) ), fully cover all inspection targets (inspection target cost) The total flight distance is relatively short (path length cost). Furthermore, it ensures that the communication quality throughout the flight meets the minimum requirements for image transmission (communication quality cost). The optimal or second-best flight path.

[0085] Experimental data:

[0086] S101: Environment Setup and Modeling

[0087] The inspection area was selected from a real urban area in Hangzhou, with a size of 1500m × 1200m and a flight altitude limit of 20m to 40m.

[0088] Terrain modeling: Download the .osm file for the region from OpenStreetMap, import it into MATLAB, parse the building polygons and height information, and construct the model as shown in the attached figure. Figure 4 The three-dimensional building model shown.

[0089] Channel modeling: Using MATLAB's ray tracing toolbox. The base station is positioned at the center of the region, with coordinates bs (769, 556, 30), and the carrier frequency... =2.4GHz, transmit power =30dBm. Ray tracing simulation was performed, with the maximum number of reflections set to 2. A global received power matrix was constructed by traversing and sampling the entire 3D space in 10m steps. Finally, the throughput at each point was calculated according to the formula. The generated result is shown in the attached image. Figure 5 The throughput heatmap shown.

[0090] S102: Parameter Configuration and Cost Function Construction

[0091] Set the constant of the trajectory cost function: .

[0092] Configure communication parameters: minimum throughput threshold =4 Mbps, path sampling interval =15 meters.

[0093] Set the parameters for the improved gray wolf optimization algorithm: population size = 50, maximum number of iterations. Probability of mutation Convergence factor according to renew.

[0094] Optionally, an auxiliary cost function can be enabled, and corresponding constants can be set. Minimum / maximum segment length limits .

[0095] S103: Path Planning Execution

[0096] Initialize the wolf pack. Each "wolf" represents a path consisting of random nodes from the starting point S(100,580,30) to the ending point E(1355,595,30).

[0097] Run the improved gray wolf optimization algorithm. In each iteration:

[0098] Calculate the cost for each individual in the population. Update global maintenance. , , Leader wolves. Update wolf pack positions. Perform mutation operations. After iteration, output the historical best. The path represented by the wolf.

[0099] S104: Results Analysis and Verification

[0100] Path visualization: as shown in the attached document Figure 6 a and appendix Figure 7 As shown in a (considering communication constraints), the planned path can cleverly weave between buildings and tends to choose areas with better communication quality.

[0101] Communication quality analysis: as attached Figure 6 a and appendix Figure 7 As shown in diagram a (considering communication constraints), the communication throughput of this path is above the 4Mbps threshold for most of the flight time, with communication downtime lasting only 5 seconds. In contrast, the planned path without considering communication constraints (see attached diagram) Figure 6 b and appendix Figure 7 (b) Although the basic task was also completed, the path was longer (3611m vs 2084m) and there were unnecessary communication failures in open areas, with communication failures lasting up to 19 seconds. This demonstrates the effectiveness of the invention in ensuring communication quality.

[0102] Algorithm performance statistics: As shown in Table 1, 50 rounds of Monte Carlo experiments were conducted. The improved Grey Wolf algorithm achieved an average success rate of 50%, an average communication failure duration of 3.94 seconds, and an average total cost of 41072. All these indicators are superior to the compared Particle Swarm Optimization algorithm (success rate 10%) and Genetic Algorithm (success rate 28%). This verifies the superiority of the improved optimization algorithm used in this invention in solving such complex, multi-constraint problems.

[0103] Table 1: Performance Comparison Chart of Different Algorithms (Improved Grey Wolf Algorithm, Particle Swarm Optimization Algorithm, Genetic Algorithm) for Path Planning (including success rate, average cost, etc.)

[0104] Evaluation indicators Improved Gray Wolf Algorithm Particle Swarm Optimization Genetic Algorithm Planning success rate 50% 10% 28% Average number of missed checkpoints 0.32 1.08 0.14 Average number of collisions 1.04 0.56 2.02 Average path length (meters) 2.6758e+03 2.7182e+03 3.2891e+03 Average duration of communication failure (seconds) 3.94 3.12 8.1000 Average cost 4.1072e+04 8.8422e+04 4.5261e+04

[0105] The scope of protection of this invention is not limited to the specific embodiments described above. For example:

[0106] Communication quality cost In the calculation, the sampling interval Both the penalty cumulative weight increment (0.2) can be adjusted according to the actual application scenario.

[0107] The channel modeling method can also be replaced by other accurate models, such as machine learning-based channel prediction models.

[0108] The specific expression for the nonlinear convergence factor and the mutation probability in the improved gray wolf optimization algorithm can be optimized and adjusted.

[0109] The auxiliary cost function can be modified by adding or removing cost terms as needed.

[0110] Any method or system that adopts the core concept of this invention, namely, constructing a comprehensive cost function that includes communication quality costs based on channel modeling and penalty accumulation mechanism, and solving it using an improved gray wolf optimization algorithm with nonlinear convergence factor, mutation mechanism and optimized leader wolf update, thereby achieving UAV path planning that ensures communication quality, falls within the protection scope of this invention.

Claims

1. A path planning method for power line inspection drones considering communication constraints, characterized in that, Includes the following steps: Step 1: Flight environment modeling, including 3D terrain modeling and air-to-ground channel modeling of the inspection area; Step 2: Based on the 3D terrain and air-to-ground channel, construct a comprehensive trajectory cost function that incorporates communication constraints. The comprehensive trajectory cost function includes at least collision cost. Inspection target cost Path length cost and communication quality cost ; Step 3: Use an optimization algorithm to optimize and solve the comprehensive trajectory cost function to obtain the optimal flight path; Among them, the communication quality cost The calculations include: Step 3.1) Sample along the planned path to obtain multiple sampling points; Step 3.2) Based on the air-to-ground channel modeling, calculate the communication quality index for each sampling point; Step 3.3) Compare the communication quality index with a preset threshold to identify sampling points with poor communication; Step 3.4) Count the identified communication failure sampling points and calculate the communication quality cost based on the counting results. .

2. The path planning method for power line inspection UAVs considering communication constraints according to claim 1, characterized in that, In step 1, the air-to-ground channel modeling adopts a channel model based on the 3GPP standard for mountainous scenarios and a channel simulation method based on ray tracing technology for dense urban scenarios.

3. The path planning method for power line inspection UAVs considering communication constraints according to claim 1, characterized in that, The collision cost A segmented judgment method is adopted, imposing a 3x penalty for path nodes located inside obstacles, a 1x penalty for path segments intersecting with obstacle surfaces, and an cumulative penalty for a single line segment colliding with multiple obstacles; the inspection target cost is... By calculating the vertical distance from checkpoints to path nodes and each road segment, it is determined whether a checkpoint is covered; the number of uncovered checkpoints is included in the cost; the path length cost. This is used to calculate the sum of the Euclidean distances of all line segments along the path.

4. The path planning method for power line inspection UAVs considering communication constraints according to claim 1, characterized in that, In step 3.4), a penalty accumulation mechanism is introduced for a sequence of consecutive communication failure sampling points. Specifically, for a sequence of consecutive communication failure sampling points, the cost count of the first point is... Cost count at the t-th point ,in For a predefined fixed increment, the penalty weight of the i-th communication failure sampling point in a continuous sequence of failure sampling points is higher than that of the (i-1)-th sampling point, where i is an integer greater than 1; the communication quality cost The product of the sum of the cost counts of all poorly communicating sampling points and a constant.

5. A path planning method for power line inspection UAVs considering communication constraints according to claim 4, characterized in that, In step 3, the optimization algorithm used is the improved Grey Wolf optimization algorithm, and the improvement includes at least one of the following: 1) Set the convergence factor 'a' of the Grey Wolf optimization algorithm to decrease non-linearly with the number of iterations t; 2) After the population location is updated, with a certain probability Randomly mutate the nodes in the individual path; 3) Change the original algorithm to force an update in each round. , , The wolf's strategy shifts to globally maintaining the three historically best solutions, updating the leader wolf's position only when a better solution is generated.

6. The path planning method for power line inspection UAVs considering communication constraints according to claim 5, characterized in that, The convergence factor The update formula is: ； in, and It is a constant. This represents the maximum number of iterations.

7. The path planning method for power line inspection UAVs considering communication constraints according to claim 1, characterized in that, The comprehensive trajectory cost function also includes an auxiliary cost function. It includes at least one of the following: path node aggregation cost Cost of abnormal road segment length Intersection cost ; Path node aggregation cost This is used to penalize path nodes that are too close together. The path node aggregation cost constant. The number of path nodes that are too close together; the cost of abnormal road segment lengths. This is used to penalize abnormal path segment lengths, where The path length anomaly cost constant, Cost count for abnormal path lengths; segment intersection cost. This is used to penalize intersections where the projections of non-adjacent path segments on the horizontal plane intersect. Let be the intersection cost constant. This represents the total number of intersections on the road segment.

8. A path planning system for a power line inspection UAV considering communication constraints, based on the method described in any one of claims 1 to 7, characterized in that, include: The environment modeling module is used to perform the terrain modeling and channel modeling. The cost function construction module is used to configure and calculate the integrated trajectory cost function; The path optimization solution module is used to run the optimization algorithm and output the optimal path. The path verification and output module is used to verify and output the final path result.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the path planning method for power line inspection drones that takes into account communication constraints as described in any one of claims 1 to 7.

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