An Unmanned Aerial Vehicle Flight Control Method and Device for Supporting Electric Power Emergency Communication

Abstract

The present invention discloses an unmanned aerial vehicle (UAV) flight control method and device for supporting power emergency communication, belonging to the technical field of power communication. The method includes: determining the target area of power emergency communication, considering the uncertainty of the UAV flight altitude and aiming at maximizing the coverage rate, and using the ant colony optimization algorithm for deployment optimization; taking the deployment position obtained by the deployment optimization as the end point, considering the uncertainty of the UAV flight altitude and taking the path length, altitude change cost, path smoothness cost, and collision penalty as constraint conditions, and using the particle swarm optimization algorithm for path optimization. The present invention uses the ant colony optimization algorithm and the particle swarm algorithm, can cope with complex three-dimensional environments, obtain the optimal deployment points and flight trajectories of the UAV, ensure sufficient coverage rate under the premise of meeting effective communication, and at the same time significantly improve the task execution efficiency of the UAV in a complex power communication network environment and the robustness of path planning, and has broad application prospects.

Description

Technical Field

[0001] The present invention relates to the technical field of power communication, and in particular, to an unmanned aerial vehicle (UAV) flight control method and device for supporting power emergency communication. Background Art

[0002] Natural disasters, such as heavy snow and floods, are likely to cause severe damage to the power grid infrastructure. In order to restore power supply, the data collected by repair personnel needs to be transmitted back to the command center in a timely manner to obtain accurate information about casualties and damages. However, the existing ground network is vulnerable to disasters and lacks network reliability. The rapid development of mobile communication technology, UAVs, and satellites has led to a transformation of the communication network towards a new "space-air-ground integrated" model, where multiple UAVs can be integrated with satellites and the ground network to achieve collaborative fault inspection, long-distance relaying, and enhanced wide-area air-ground coverage capabilities.

[0003] Resource scheduling is a key pillar for achieving space-air-ground integration. In particular, path planning finds the optimal deployment points and flight trajectories for UAVs, ensuring sufficient coverage while meeting the requirements of effective communication. However, there are still many deficiencies in the existing technologies to meet the above requirements. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide an unmanned aerial vehicle (UAV) flight control method and device for supporting power emergency communication, which can cope with complex three-dimensional environments, obtain the optimal deployment points and flight trajectories of UAVs, and ensure sufficient coverage while meeting the requirements of effective communication.

[0005] To achieve the above object, the present invention is implemented by the following technical solutions:

[0006] In a first aspect, the present invention provides an unmanned aerial vehicle (UAV) flight control method for supporting power emergency communication, including:

[0007] Determine the target area for power emergency communication, consider the uncertainty of the UAV flight altitude, and use the ant colony optimization algorithm for deployment optimization with the goal of maximizing coverage;

[0008] Taking the deployment position obtained by the deployment optimization as the end point, consider the uncertainty of the UAV flight altitude, and use the particle swarm optimization algorithm for path optimization with the path length, altitude change cost, path smoothness cost, and collision penalty as constraints.

[0009] Optionally, the use of the ant colony optimization algorithm for deployment optimization includes:

[0010] Determine the set of UAV deployment positions according to the target area;

[0011] Initialize the number of iterations , the The pheromone concentration corresponding to the th drone at the th deployment position in the th iteration, the maximum number of iterations , repeatedly execute the deployment optimization iteration step until , and output the final deployment position of the drone;

[0012] Among them, the deployment optimization iteration step includes:

[0013] Construct the heuristic information function corresponding to each drone in the th iteration based on the set of deployment positions;

[0014] According to the heuristic information function and pheromone concentration corresponding to each drone in the th iteration calculate the selection probability of the deployment position of each drone by the ant in the th iteration;

[0015] Each ant selects the deployment position of each drone in the th iteration according to the selection probability of the deployment position of each drone in the th iteration;

[0016] According to the deployment position of each drone in the th iteration, considering the uncertainty of the drone flight altitude and aiming at maximizing the coverage rate, calculate the pheromone concentration corresponding to each drone in the th iteration ; let .

[0017] Optionally, the heuristic information function is:

[0018]

[0019] In the formula, is the value of the heuristic information function corresponding to the th iteration and the th drone at the th deployment position, is the task weight related to the th target point, is the position coordinate of the th target point, is the number of target points; is the position coordinate of the th deployment position selected by the th drone, is a constant term;

[0020] The selection probability is as follows:

[0021]

[0022] In the formula, is the selection probability of the -th iteration for the -th UAV to the -th deployment location, is the pheromone concentration corresponding to the -th iteration when the -th UAV is at the -th deployment location, is the index value of the deployment location; are the weights of the pheromone concentration and the heuristic information function value, respectively.

[0023] Optionally, based on the deployment locations of each UAV in the -th iteration, considering the uncertainty of the UAV flight altitude and aiming at maximizing the coverage rate, calculate the pheromone concentration corresponding to each UAV in the -th iteration, including:

[0024] Considering the uncertainty of the UAV flight altitude, construct a UAV flight altitude function and calculate the effective signal coverage range of the UAV:

[0025]

[0026]

[0027] In the formula, is the target flight altitude and the actual flight altitude of the -th UAV, is the flight altitude perturbation of the -th UAV, is the maximum flight altitude perturbation, is the uniform probability distribution in the interval from to ; is the UAV communication radiation adjustment parameter, is the minimum safe flight altitude of the UAV, is the effective signal coverage range of the -th UAV;

[0028] Based on the effective signal coverage range of the UAV, calculate the effective signal coverage status of the UAV for the target point:

[0029]

[0030] In the formula, is the The effective signal coverage status of a drone at the th deployment location for the th target point, is the three-dimensional Euclidean distance operator; is the th position coordinate of the th target point, is the position coordinate of the th deployment location selected by the

[0031] Based on the effective signal coverage status, a coverage reward function of the drone for the target point is constructed with the goal of maximizing the coverage rate:

[0032]

[0033] Where:

[0034]

[0035]

[0036] In the formula, is to maximize the coverage rate, is the number of drones, is the number of target points, is the th position coordinate of the th deployment location selected by the is the deployment overlap penalty term, is the deployment overlap penalty coefficient, is the minimum distance threshold between drones, is the value of the coverage reward function;

[0037] Update the pheromone concentration based on the coverage reward function:

[0038]

[0039]

[0040] In the formula, is the th iteration, the pheromone concentration corresponding to the th drone at the th deployment location, is the th iteration, the value of the coverage reward function of the drone for the target point, is the information enhancement coefficient, is the th iteration, the pheromone concentration volatilization rate, is the initial evaporation rate, is the decay constant.

[0041] Optionally, the path optimization using the particle swarm optimization algorithm includes:

[0042] Construct a fitness function considering the uncertainty of the UAV flight altitude and with the path length, altitude change cost, path smoothness cost, and collision penalty as constraints;

[0043] Set the number of particles, the initial position and initial velocity of each particle, and the maximum number of iterations , where the initial position of each particle is all the waypoints on the path

[0044] Initialize the iteration number , initialize the global optimal position and the individual optimal position of each particle as the initial position, and repeat the path optimization iteration steps until , output the final global optimal position to obtain the optimal path;

[0045] Among them, the path optimization iteration steps include:

[0046] According to the velocity and position, individual optimal position, and global optimal position of each particle in the -th iteration, calculate the velocity and position of each particle in the -th iteration;

[0047] According to the velocity and position of each particle in the -th iteration, use the fitness function to calculate the fitness of each particle;

[0048] Update the global optimal position and the individual optimal position of each particle according to the fitness of each particle; let .

[0049] Optionally, the fitness function is:

[0050]

[0051] Where:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] In the formula, is the path length, is the height change cost, is the path smoothness cost, is the collision penalty term, are the weight coefficients of the path length, height change cost, and path smoothness cost respectively, are the position coordinates of the th waypoint on the path respectively, is the number of waypoints on the path, is the z-axis coordinate value of the th waypoint on the path and the actual flight height of the UAV at the th waypoint, is the flight height perturbation of the UAV at the th waypoint, is the maximum flight height perturbation, is uniformly distributed with equal probability in the interval from to , is the average value of the z-axis coordinate values of all waypoints on the path, is the preset height median, are all weight coefficients, is the angle between the line connecting the th waypoint and its adjacent waypoint on the path; are the xy-axis coordinate values of the th waypoint on the path, is the z-axis coordinate value of the th waypoint on the path, is the center coordinate of the th obstacle, is the minimum and maximum z-axis coordinate values of the th obstacle; is the minimum effective distance between the line connecting the th and th waypoints on the path and the th obstacle, is the safety radius of the th obstacle, is the penalty score, is the number of obstacles, is the interpolation coefficient between 0 and 1;

[0059]

[0060] In the formula, is the three-dimensional Euclidean distance operator, are the The position coordinates of a navigation point.

[0061] Optionally, calculating the velocity and position of each particle in the

[0062]

[0063]

[0064] In the formula, is the velocity of the th particle in the th iteration, is the inertia weight, is the learning factor, is a random number between 0 and 1, is the individual optimal position of the th particle, is the global optimal position, is the th iteration of the th particle's position.

[0065] Optionally, the UAV flight control method further includes:

[0066] Performing smoothing processing on the path obtained by path optimization by using the cubic spline interpolation method.

[0067] In a second aspect, the present invention provides a UAV flight control device for supporting power emergency communication, including:

[0068] A deployment optimization module, configured to determine the target area of power emergency communication, consider the uncertainty of UAV flight altitude and aim at maximizing the coverage rate, and perform deployment optimization by using the ant colony optimization algorithm;

[0069] A path optimization module, configured to take the deployment position obtained by deployment optimization as the end point, consider the uncertainty of UAV flight altitude and use the path length, altitude change cost, path smoothness cost, and collision penalty as constraint conditions, and perform path optimization by using the particle swarm optimization algorithm.

[0070] Optionally, the UAV flight control device further includes:

[0071] A smoothing processing module, configured to perform smoothing processing on the path obtained by path optimization by using the cubic spline interpolation method.

[0072] Compared with the prior art, the beneficial effects achieved by the present invention:

[0073] A UAV flight control method and device for supporting power emergency communication provided by the present invention. In the positioning stage, the ant colony optimization algorithm is adopted, with the coverage reward function that simultaneously considers the coverage efficiency and deployment coordination as the optimization objective, and a pheromone evaporation mechanism that dynamically decays with the number of iterations is designed to improve the algorithm search efficiency and deployment solution stability. In the path planning stage, the optimal suspension position in the positioning stage is selected as the UAV planning end point, and considering constraints such as path length, altitude change, smoothness, and collision detection, the particle swarm optimization algorithm is used to generate the path. Finally, the cubic spline interpolation method is used for smoothing processing to further improve the path executability. In summary, the present invention can cope with complex three-dimensional environments, obtain the optimal deployment points and flight trajectories of UAVs, ensure sufficient coverage while meeting effective communication requirements, and significantly improve the task execution efficiency of UAVs in complex power communication network environments and the robustness of path planning, with broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 is a schematic flowchart of the UAV flight control method for supporting power emergency communication provided by an embodiment of the present invention;

[0075] Figure 2 is a schematic diagram of the simulation verification scenario of UAV flight control provided by an embodiment of the present invention;

[0076] Figure 3 is a schematic diagram of the three-dimensional path results in one experiment of the method of the present invention, traditional PSO, and A* algorithm provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0077] The present invention will be further described below with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention and cannot be used to limit the protection scope of the present invention.

[0078] Embodiment 1:

[0079] As Figure 1 shown, an embodiment of the present invention provides a UAV flight control method for supporting power emergency communication, including the following steps:

[0080] Step S1. Determine the target area of power emergency communication. Considering the uncertainty of UAV flight altitude and aiming at maximizing the coverage rate, the ant colony optimization algorithm is used for deployment optimization.

[0081] Specifically, using the ant colony optimization algorithm for deployment optimization includes:

[0082] Step S1.1. Determine the UAV deployment position set according to the target area.

[0083] Specifically, in this embodiment, the space of the target area can be evenly divided into multiple sub-spaces in the shape of cubes, and the center point of each sub-space is used as the deployment position. In other optional embodiments, those skilled in the art can set the deployment position according to actual needs.

[0084] Step S1.2: Initialize the iteration count , the pheromone concentration corresponding to the th iteration and the th drone at the th deployment position, the maximum iteration count , and repeatedly execute the deployment optimization iteration step until , and output the final deployment positions of the drones;

[0085] Among them, the deployment optimization iteration step includes:

[0086] Step S1.2.1: Based on the set of deployment positions, construct the heuristic information function corresponding to each drone in the th iteration;

[0087] Step S1.2.2: Calculate the selection probability of each ant for the deployment position of each drone in the th iteration according to the heuristic information function and pheromone concentration corresponding to each drone in the th iteration; Step S1.2.3: Each ant selects the deployment position of each drone in the

[0088] th iteration according to the selection probability of the deployment position of each drone in the th iteration; Step S1.2.4: According to the deployment positions of each drone in the

[0089] th iteration, considering the uncertainty of the flight altitude of the drones and aiming at maximizing the coverage rate, calculate the pheromone concentration corresponding to each drone in the th iteration ; Let . .

[0090] Among them, the heuristic information function is:

[0091]

[0092] In the formula, is the value of the heuristic information function corresponding to the th iteration and the th drone at the th deployment position, is related to the The task weights associated with each target point, For the The position coordinates of the target points, is the number of target points; For the The drone selected The location coordinates of the deployment locations, is a constant term;

[0093] The probability of selection is:

[0094]

[0095] Where, For the In the iteration A drone against The probability of selecting a deployment location, For the In the iteration The drone in The corresponding pheromone concentration at the deployment location is The index value of the deployment location; are the weights of pheromone concentration and heuristic information function value respectively.

[0096] In step S1.2.4, according to The deployment position of each UAV in the iteration is calculated by considering the uncertainty of the UAV flight height and maximizing the coverage. The pheromone concentration of each drone in the iteration include:

[0097] Considering the uncertainty of the UAV flight height, the UAV flight height function is constructed and the effective signal coverage range of the UAV is calculated:

[0098]

[0099]

[0100] Where, For the The target flight altitude and actual flight altitude of each UAV, For the The flight altitude disturbance of the UAV, is the maximum flight altitude disturbance, For the interval arrive Equal probability distribution between ; Adjust parameters for drone communication radiation, The minimum safe flight altitude of the drone. is the effective signal coverage range of the drone;

[0101] Calculate the effective signal coverage status of the drone for the target point based on the effective signal coverage range of the drone:

[0102]

[0103] In the formula, is the effective signal coverage status of the th drone at the th deployment position for the th target point, is the position coordinate of the th target point, is the position coordinate of the

[0104] Based on the effective signal coverage status, construct a coverage rate reward function for the drone to the target point with the goal of maximizing the coverage rate:

[0105]

[0106] Where:

[0107]

[0108]

[0109] In the formula, is to maximize the coverage rate, is the number of drones, is the number of target points, is the position coordinate of the th deployment position selected by the th drone; is the deployment overlap penalty term, is the deployment overlap penalty coefficient, is the minimum distance threshold between drones, is the value of the coverage rate reward function;

[0110] Update the pheromone concentration based on the coverage rate reward function:

[0111]

[0112]

[0113] In the formula, is the th iteration, the The pheromone concentration corresponding to the th deployment position of the UAV, is the coverage reward function value of the UAV for the target point in the th iteration, is the information enhancement coefficient, is the th iteration, and is the evaporation rate of the pheromone concentration, is the initial evaporation rate, is the decay constant.

[0114] Step S2: Taking the deployment position obtained by deployment optimization as the end point, considering the uncertainty of the UAV flight altitude and taking the path length, altitude change cost, path smoothness cost, and collision penalty as constraint conditions, a particle swarm optimization algorithm is used for path optimization.

[0115] Specifically, using the particle swarm optimization algorithm for path optimization includes:

[0116] Step S2.1: Considering the uncertainty of the UAV flight altitude and taking the path length, altitude change cost, path smoothness cost, and collision penalty as constraint conditions to construct a fitness function;

[0117] Step S2.2: Set the number of particles, the initial position and initial velocity of each particle, and the maximum number of iterations , and the initial position of each particle is all the waypoints on the path

[0118] Step S2.3: Initialize the number of iterations , initialize the global optimal position and the individual optimal position of each particle as the initial position, and repeat the path optimization iteration step until , output the final global optimal position, and obtain the optimal path;

[0119] Among them, the path optimization iteration step includes:

[0120] Step S2.3.1: Calculate the velocity and position of each particle in the th iteration according to the velocity and position, individual optimal position, and global optimal position of each particle in the th iteration;

[0121] Step S2.3.2: According to the velocity and position of each particle in the th iteration, use the fitness function to calculate the fitness of each particle;

[0122] Step S2.3.3: Update the global optimal position and the individual optimal position of each particle according to the fitness of each particle; let .

[0123] Specifically, in this embodiment, the fitness function is:

[0124]

[0125] Where:

[0126]

[0127]

[0128]

[0129]

[0130]

[0131]

[0132] In the formula, is the path length, is the height change cost, is the path smoothness cost, is the collision penalty term, are the weight coefficients of the path length, height change cost, and path smoothness cost respectively, are the position coordinates of the th waypoint on the path respectively, is the number of waypoints on the path, is the z-axis coordinate value of the th waypoint on the path and the actual flight height of the UAV at the th waypoint, is the flight height perturbation of the UAV at the th waypoint, is the maximum flight height perturbation, is the uniform probability distribution in the interval from to respectively, is the average value of the z-axis coordinate values of all waypoints on the path, is the preset height median, are all weight coefficients, is the angle between the connection line of the th waypoint and the adjacent waypoint on the path; are the xy-axis coordinate values of the th waypoint on the path respectively, is the z-axis coordinate value of the th waypoint on the path, is the th obstacle center coordinate, is the The minimum and maximum z-axis coordinates of an obstacle; For the and th connection line of waypoints and the th obstacle, the minimum effective distance; For the th obstacle, the safety radius; The penalty score; The number of obstacles; The interpolation coefficient between 0 and 1;

[0133]

[0134] In the formula, Is the three-dimensional Euclidean distance operator, For the th position coordinate of the waypoint on the path.

[0135] In step S2.3.1, calculating the velocity and position of each particle in the th iteration includes:

[0136]

[0137]

[0138] In the formula, Is the velocity of the th iteration of the th particle, Is the inertia weight, Is the learning factor, Is a random number between 0 and 1, Is the individual optimal position of the th particle, Is the global optimal position, Is the position of the th iteration of the th particle.

[0139] Step S3: For the path obtained by path optimization, use the cubic spline interpolation method for smoothing.

[0140] Cubic Spline Interpolation, abbreviated as Spline Interpolation, is a smooth curve passing through a series of shape value points. Mathematically, it is a process of obtaining a set of curve functions by solving a system of three-moment equations. Cubic Spline Interpolation constructs a path through piecewise cubic polynomials, ensuring the continuity of function values, first-order derivatives (velocities), and second-order derivatives (accelerations) in adjacent intervals. This property avoids the "broken line effect" of traditional linear interpolation and the "Runge oscillation phenomenon" of high-order polynomial interpolation, and is particularly suitable for scenarios with high requirements for motion continuity such as unmanned vehicles and drones, which can reduce mechanical vibrations and energy losses caused by sudden changes in path curvature.

[0141] As Figure 2 shown, an embodiment of the present invention provides a simulation verification scenario for unmanned aerial vehicle flight control, which includes a power communication network control center, three fixed base stations (BS1, BS2, BS3), and five unmanned aerial vehicles (UAV1-UAV5). The control center obtains the deployment points and flight trajectories of the unmanned aerial vehicles through the unmanned aerial vehicle flight control method proposed in the embodiment of the present invention according to the requirements of the power grid inspection task. The test area simulates mountainous terrain, including dynamic obstacles (such as high-voltage line towers, temporary construction areas) and random user demand distributions.

[0142] Parameter settings:

[0143] For the setting of environmental parameters, the regional range is 5km * 5km, the terrain undulation height difference is 0 - 200m, the number of dynamic obstacles is 10 (randomly distributed, with a radius of 10 - 50m), the positions change randomly with the experimental batches, and the number of user demand points is 20 (randomly generated, with high / medium / low three levels of priority).

[0144] For the deployment and positioning stage, the ant colony optimization algorithm is used to search for the optimal suspension deployment points of the five unmanned aerial vehicles in three-dimensional space. The number of ants is 20, the maximum number of iterations is 100 times, the initial pheromone concentration is 1.0, and the evaporation factor adopts a dynamic attenuation form:

[0145]

[0146] The pheromone enhancement factor Q is 100, and the heuristic information function weight parameters 、 are 1.0 and 2.0 respectively. The flight height perturbation is modeled as:

[0147]

[0148] The communication coverage radius is:

[0149]

[0150] The coverage rate calculation is based on the perturbed coverage radius, and the deployment plan is optimized according to the following objective function:

[0151]

[0152] Finally, the deployment result with the optimal coverage rate is selected as the candidate endpoint for path planning.

[0153] For the path planning stage, a feasible path from the communication center to the deployment point is planned. The maximum number of iterations of the particle swarm optimization algorithm is 200 times. The range of the inertia weight for speed update is (0.9~0.4). The weight coefficient w1 (path length) = 0.5, w2 (height change) = 0.3, w3 (smoothness) = 0.2, and the random perturbation intensity = 5m (standard deviation of height adjustment). In this embodiment, the method proposed in the embodiment of the present invention is considered to be compared with the traditional PSO algorithm and the typical A* algorithm in path planning. The experimental results are shown in Table 1.

[0154] Table 1 Comparison of results of three path planning methods

[0155] Index The present invention Traditional PSO A* algorithm Average path length (km) 8.7±0.3 9.4±0.5 11.2±0.8 Target coverage rate (%) 93.5±2.5 82.0±3.0 76.5±4.0 Number of collisions 1.2±0.5 4.8±1.2 6.5±1.5 Computation time (s) 52±8 28±5 145±20

[0156] Combined with the data results in Table 1, the path length generated by the method in the embodiment of the present invention is the shortest (8.7 km), which is about 7.4% shorter than the traditional PSO (only optimizing the path length) and 22.3% shorter than the A* algorithm. The traditional PSO generates redundant detours in the path due to ignoring the height change and smoothness constraints; the A* algorithm has a high degree of discretization of the generated path due to relying on the grid resolution, resulting in a significant increase in length; at the same time, while the method of this patent combines multiple constraints such as path length and ensures path safety, it also combines the cubic spline interpolation technology, significantly shortening the path length and improving the path feasibility.

[0157] In terms of obstacle avoidance performance, the method in the embodiment of the present invention combines the collision detection and random perturbation mechanisms, and the number of collisions (1.2 times) is significantly lower than that of the traditional PSO (4.8 times) and the A* algorithm (6.5 times). The traditional PSO is prone to falling into a local optimal path and causing collisions due to the lack of dynamic adjustment ability; the A* algorithm has the highest collision risk due to the obstacle projection error and the principle of discrete path points in the three-dimensional environment, indicating that in this embodiment, the method of this patent has strong robustness in a complex dynamic environment.

[0158] In terms of computing performance, the computing time (52 s) of the method of this patent is slightly higher than that of the traditional PSO (28 s), but much lower than that of the A* algorithm (145 s). The traditional PSO has the shortest computing time due to simple single-objective optimization calculation; the method of this patent increases the computing complexity due to multi-constraint optimization and dynamic perturbation; the A* algorithm has the lowest computing efficiency due to the explosion of the three-dimensional grid search space.

[0159] As Figure 3 shown, the three-dimensional path results of the method of the present invention and the traditional PSO and A* algorithms in one experiment are presented. It can be intuitively observed from Figure 3 this that in a relatively complex obstacle scenario, due to its poor obstacle avoidance performance, the A* algorithm generates an infeasible path that directly passes through the obstacle. At the same time, compared with the traditional PSO, the path length of the method of the present invention is significantly shorter and the smoothness is better, and the generated path has better feasibility.

[0160] Example Two:

[0161] The embodiment of the present invention provides an unmanned aerial vehicle flight control device for supporting power emergency communication, including:

[0162] A deployment optimization module configured to determine the target area of power emergency communication, consider the uncertainty of the flight altitude of the unmanned aerial vehicle, and perform deployment optimization using the ant colony optimization algorithm with the goal of maximizing the coverage rate;

[0163] A path optimization module configured to take the deployment position obtained by the deployment optimization as the end point, consider the uncertainty of the flight altitude of the unmanned aerial vehicle, and perform path optimization using the particle swarm optimization algorithm with the path length, altitude change cost, path smoothness cost, and collision penalty as constraints;

[0164] A smoothing processing module configured to perform smoothing processing on the path obtained by the path optimization using the cubic spline interpolation method.

[0165] The final output result can be three-dimensionally visualized using the MATLAB tool to intuitively display the whole process of path planning and the optimization effect.

[0166] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0167] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device produce a means for realizing the functions specified in one flow Figure 1 one flow or more flows and / or blocks Figure 1 or in one block or more blocks.

[0168] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means that realizes the functions specified in one flow Figure 1 one flow or more flows and / or blocks Figure 1 or in one block or more blocks.

[0169] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in one flow Figure 1 one flow or more flows and / or blocks Figure 1 or in one block or more blocks.

[0170] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A UAV flight control method supporting power emergency communication, characterized in that: include: Determine the target area for power emergency communications, consider the uncertainty of drone flight altitude and aim to maximize coverage, and use the ant colony optimization algorithm for deployment optimization; Taking the deployment position obtained by deployment optimization as the end point, considering the uncertainty of the UAV's flight altitude and taking path length, altitude change cost, path smoothness cost and collision penalty as constraints, the particle swarm optimization algorithm is used for path optimization. The deployment optimization using the ant colony optimization algorithm includes: Determine the deployment location set of drones based on the target area; Initialization iteration count , No. In the iteration The drone in The corresponding pheromone concentration at the deployment location , maximum number of iterations , repeat the deployment optimization iteration steps until , output the final deployment position of the UAV; The deployment optimization iteration step includes: Construct the first The heuristic information function corresponding to each UAV in the iteration; According to The heuristic information function and pheromone concentration corresponding to each drone in the iteration Counting ants in the The probability of selecting the deployment location of each UAV in the iteration; Each ant The probability of selecting the deployment location of each UAV in the iteration is The deployment position of each drone in the iteration; According to The deployment position of each UAV in the iteration is calculated by considering the uncertainty of the UAV flight height and maximizing the coverage. The pheromone concentration of each drone in the iteration ;make ; Wherein, the heuristic information function is: ； Where, For the In the iteration The drone in The corresponding heuristic information function value when the deployment position is For the The task weights associated with each target point, For the The position coordinates of the target points, is the number of target points; For the The drone selected The location coordinates of the deployment locations, is a constant term; The selection probability is: ； Where, For the In the iteration A drone against The probability of selecting a deployment location, For the In the iteration The drone in The corresponding pheromone concentration at the deployment location is For the In the iteration The drone in The corresponding heuristic information function value when the deployment position is The index value of the deployment location; are the weights of pheromone concentration and heuristic information function value respectively; Among them, according to The deployment position of each UAV in the iteration is calculated by considering the uncertainty of the UAV flight height and maximizing the coverage. The pheromone concentration of each drone in the iteration include: Considering the uncertainty of the UAV flight height, the UAV flight height function is constructed and the effective signal coverage range of the UAV is calculated: ； ； Where, For the The target flight altitude and actual flight altitude of each UAV, For the The flight altitude disturbance of the UAV, is the maximum flight altitude disturbance, For the interval arrive Equal probability distribution between ; Adjust parameters for drone communication radiation, The minimum safe flight altitude of the drone. For the Effective signal coverage of drones; Calculate the effective signal coverage status of the drone to the target point based on the effective signal coverage range of the drone: ； Where, For the The drone in deployment location The effective signal coverage status of each target point, is the three-dimensional Euclidean distance operator; For the The position coordinates of the target points, For the The drone selected The location coordinates of each deployment location; Based on the effective signal coverage status, a reward function for the coverage rate of the drone to the target point is constructed with the goal of maximizing the coverage rate: ； in: ； ； Where, To maximize coverage, is the number of drones, is the number of target points, For the The drone selected The location coordinates of each deployment location; To deploy the overlapping penalty term, is the deployment overlap penalty coefficient, is the minimum distance threshold between drones, is the coverage reward function value; Update the pheromone concentration based on the coverage reward function: ； ； Where, For the In the iteration The drone in The corresponding pheromone concentration at the deployment location is For the The reward function value of the drone's coverage rate of the target point in the iteration, is the information enhancement coefficient, For the The pheromone concentration in the iteration The volatility rate, is the initial volatility, is the decay constant.

2. The UAV flight control method supporting power emergency communication according to claim 1 is characterized in that: The path optimization using the particle swarm optimization algorithm includes: Considering the uncertainty of UAV flight altitude, the fitness function is constructed with path length, altitude change cost, path smoothness cost and collision penalty as constraints; Set the number of particles, the initial position and initial velocity of each particle, and the maximum number of iterations , the initial position of each particle is all the waypoints on the path Initialization iteration count Initialize the global optimal position and the individual optimal position of each particle as the initial position, and repeat the path optimization iterative steps until , output the final global optimal position and obtain the optimal path; The path optimization iteration step includes: According to The speed and position of each particle in the iteration, the individual optimal position, and the global optimal position are calculated. The velocity and position of each particle in the iteration; According to The speed and position of each particle in the iteration are used to calculate the fitness of each particle using the fitness function; Update the global optimal position and the individual optimal position of each particle according to the fitness of each particle; let .

3. The UAV flight control method supporting power emergency communication according to claim 2 is characterized in that: The fitness function is: ； in: ； ； ； ； ； ； Where, is the path length, is a highly variable cost, is the path smoothness cost, is the collision penalty term, are the weight coefficients of path length, height change cost and path smoothness cost, respectively. The first The position coordinates of the waypoints, is the number of waypoints on the path, For the path The z-axis coordinate value of the waypoint and the drone at the The actual flight altitude of each waypoint, For drones The flight altitude disturbance of the waypoints, is the maximum flight altitude disturbance, For the interval arrive The equal probability distribution between is the average value of the z-axis coordinates of all waypoints on the path, is the preset median height, are weight coefficients, For the path The angle between a waypoint and the line connecting the adjacent waypoints; For the path The xy coordinate values of the waypoints, For the path The z-axis coordinate value of the waypoint, For the The center coordinates of the obstacles, For the The minimum and maximum z-axis coordinates of each obstacle; For the path and The waypoint is connected to the The minimum effective distance between obstacles, For the The safety radius of obstacles, is the penalty score, is the number of obstacles, is an interpolation coefficient between 0 and 1; ； Where, is the three-dimensional Euclidean distance operator, For the path The location coordinates of the waypoint.

4. The UAV flight control method supporting power emergency communication according to claim 2, characterized in that: The calculation The velocity and position of each particle in the iterations are: ； ； Where, For the In the iteration The speed of the particle, is the inertia weight, is the learning factor, is a random number between 0 and 1, For the The individual optimal position of each particle, is the global optimal position, For the In the iteration The position of a particle.

5. The UAV flight control method supporting power emergency communication according to claim 1, characterized in that: The UAV flight control method further includes: The path obtained by path optimization is smoothed using the cubic spline interpolation method.

6. A UAV flight control device supporting power emergency communication, characterized in that: include: The deployment optimization module is configured to determine the target area for power emergency communication, consider the uncertainty of UAV flight altitude and aim to maximize coverage, and use the ant colony optimization algorithm for deployment optimization; The path optimization module is configured to use the deployment position obtained by deployment optimization as the end point, consider the uncertainty of the UAV's flight altitude, and use the path length, altitude change cost, path smoothness cost, and collision penalty as constraints, and use the particle swarm optimization algorithm to optimize the path; The deployment optimization using the ant colony optimization algorithm includes: Determine the deployment location set of drones based on the target area; Initialization iteration count , No. In the iteration The drone in The corresponding pheromone concentration at the deployment location , maximum number of iterations , repeat the deployment optimization iteration steps until , output the final deployment position of the UAV; The deployment optimization iteration step includes: Construct the first The heuristic information function corresponding to each UAV in the iteration; According to The heuristic information function and pheromone concentration corresponding to each drone in the iteration Counting ants in the The probability of selecting the deployment location of each UAV in the iteration; Each ant The probability of selecting the deployment location of each UAV in the iteration is The deployment position of each drone in the iteration; According to The deployment position of each UAV in the iteration is calculated by considering the uncertainty of the UAV flight height and maximizing the coverage. The pheromone concentration of each drone in the iteration ;make ; Wherein, the heuristic information function is: ； Where, For the In the iteration The drone in The corresponding heuristic information function value when the deployment position is For the The task weights associated with each target point, For the The position coordinates of the target points, is the number of target points; For the The drone selected The location coordinates of the deployment locations, is a constant term; The selection probability is: ； Where, For the In the iteration A drone against The probability of selecting a deployment location, For the In the iteration The drone in The corresponding pheromone concentration at the deployment location is For the In the iteration The drone in The corresponding heuristic information function value when the deployment position is The index value of the deployment location; are the weights of pheromone concentration and heuristic information function value respectively; Among them, according to The deployment position of each UAV in the iteration is calculated by considering the uncertainty of the UAV flight height and maximizing the coverage. The pheromone concentration of each drone in the iteration include: Considering the uncertainty of the UAV flight height, the UAV flight height function is constructed and the effective signal coverage range of the UAV is calculated: ； ； Where, For the The target flight altitude and actual flight altitude of each UAV, For the The flight altitude disturbance of the UAV, is the maximum flight altitude disturbance, For the interval arrive Equal probability distribution between ; Adjust parameters for drone communication radiation, The minimum safe flight altitude of the drone. For the Effective signal coverage of drones; Calculate the effective signal coverage status of the drone to the target point based on the effective signal coverage range of the drone: ； Where, For the The drone in deployment location The effective signal coverage status of each target point, is the three-dimensional Euclidean distance operator; For the The position coordinates of the target points, For the The drone selected The location coordinates of each deployment location; Based on the effective signal coverage status, a reward function for the coverage rate of the drone to the target point is constructed with the goal of maximizing the coverage rate: ； in: ； ； Where, To maximize coverage, is the number of drones, is the number of target points, For the The drone selected The location coordinates of each deployment location; To deploy the overlapping penalty term, is the deployment overlap penalty coefficient, is the minimum distance threshold between drones, is the coverage reward function value; Update the pheromone concentration based on the coverage reward function: ； ； Where, For the In the iteration The drone in The corresponding pheromone concentration at the deployment location is For the The reward function value of the drone's coverage rate of the target point in the iteration, is the information enhancement coefficient, For the The pheromone concentration in the iteration The volatility rate, is the initial volatility, is the decay constant.

7. The UAV flight control device supporting power emergency communication according to claim 6, characterized in that: The UAV flight control device also includes: The smoothing processing module is configured to perform smoothing processing on the path obtained by path optimization by using a cubic spline interpolation method.

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