An unmanned aerial vehicle post-disaster emergency path planning method based on A-star algorithm

Abstract

The application relates to a kind of unmanned plane post-disaster emergency path planning methods based on A star algorithm, comprising the following steps: based on power grid area, the three-dimensional grid map of the flight area of unmanned plane is constructed, and the starting point and target point are set in the map, wherein the target point is the disaster occurrence place of power grid area, and the disaster grade of each target point is set;Initialize A star algorithm model, create empty open list and closed list, and add the node where the obstacle in the three-dimensional grid map is in the closed list, while improving the node evaluation function of A star algorithm model through the energy consumption model of unmanned plane;The post-disaster emergency path of unmanned plane is planned through A star algorithm model.

Description

TECHNICAL FIELD

[0001] The application relates to an unmanned aerial vehicle post-disaster emergency path planning method based on an A-star algorithm and belongs to the technical field of unmanned aerial vehicles. BACKGROUND

[0002] In recent years, natural disasters have been emerging, and serious earthquakes, floods, mudslides, snowfalls and typhoons seriously threaten the stable operation of power grids. Therefore, power inspection has become an indispensable part of power grid safety, especially when serious natural disasters occur, the damaged parts of the power grid are found in time, and the power safety is ensured.

[0003] Although some small and light inspection unmanned aerial vehicles are currently configured for power inspection, some positive effects are generated, but in the face of complex terrain, many mountain environments and serious shielding, it is difficult to realize long-distance, over-the-horizon inspection and other problems, and it is difficult to realize all-time and all-weather inspection of the whole power grid. Therefore, the intelligent path planning algorithm is developed, the inspection path is optimized, and technical support is provided for power repair.

[0004] The prior art such as patent number CN117109597A discloses an unmanned aerial vehicle path planning method and device based on an improved A-star algorithm, obstacles in the flight area are identified and marked, the obstacles are processed, obstacle weight coefficients are generated according to the processing results of the obstacles, the evaluation function of the A-star algorithm model is weighted, the path of the unmanned aerial vehicle is planned based on the weighted evaluation function, the obstacle weight coefficients in the three-dimensional grid map are used to weight the evaluation function, and the improved A-star algorithm ensures the global optimal path of the unmanned aerial vehicle and improves the efficiency and safety of real-time path planning of the unmanned aerial vehicle.

[0005] However, the above technical solution only considers the obstacles in the flight area of the unmanned aerial vehicle, does not comprehensively consider the energy consumption of the unmanned aerial vehicle, and does not consider the emergency of the planned path. This may result in a non-optimal path, which cannot effectively save the energy consumption of the unmanned aerial vehicle and limits the flight time and range of the unmanned aerial vehicle.

[0006] Meanwhile, the collision problem between the unmanned aerial vehicle and other invading machines is not fully considered, which may cause flight conflicts between the unmanned aerial vehicles and affect the safety and effect of path planning.

[0007] Therefore, the traditional A-star algorithm cannot meet the demand of the extreme post-disaster unmanned aerial vehicle emergency path planning. SUMMARY

[0008] In order to solve the problems in the prior art, the application provides an unmanned aerial vehicle post-disaster emergency path planning method based on an A-star algorithm.

[0009] The technical scheme of the application is as follows:

[0010] In one aspect, the application provides a UAV post-disaster emergency path planning method based on A-star algorithm, comprising the following steps:

[0011] A three-dimensional grid map of the UAV flight area is constructed based on the power grid area, and a starting point and a target point are set in the map, wherein the target point is the disaster occurrence location of the power grid area, and the disaster level of each target point is set;

[0012] An A-star algorithm model is initialized, an empty open list and a closed list are created, and the nodes where obstacles in the three-dimensional grid map are located are added to the closed list, and the node evaluation function of the A-star algorithm model is improved through the energy consumption model of the UAV;

[0013] The UAV post-disaster emergency path is planned through the A-star algorithm model.

[0014] As a preferred embodiment of the application, the UAV post-disaster emergency path planning includes path planning in a key disaster point inspection stage and path planning in a global power grid inspection stage;

[0015] The path planning in the key disaster point inspection stage comprises the following steps:

[0016] Target points with a disaster level greater than a threshold value are set as target points for path planning in this stage, and are sorted according to the disaster level, and the starting point is added to the open list and regarded as the current node;

[0017] When the open list is not empty, the following steps are repeated: the neighboring nodes of the current node are traversed, if a neighboring node is already stored in the closed list, the node is ignored, if a neighboring node is not in the open list or the closed list, the node is added to the open list as a to-be-explored node, the to-be-explored nodes stored in the open list are evaluated through the node evaluation function, the current node is regarded as the parent node of the to-be-explored node with the minimum evaluation value and the to-be-explored node is regarded as the current node, then the parent node of the current node is removed from the open list and added to the closed list; if a neighboring node is already in the open list, the actual movement cost from the starting point to the current node is compared with the actual movement cost from the starting point to the neighboring node, if the actual movement cost from the starting point to the neighboring node is smaller, the parent node of the neighboring node is regarded as the current node and the current node is re-evaluated through the node evaluation function, the step is repeated until the current node is the target point, and the exploration is successful;

[0018] When the open list is empty, it means that no path is found, and the exploration fails;

[0019] After one target point is successfully explored, the target point is regarded as the starting point to explore the next target point, and all target points are explored according to the sorting.

[0020] As a preferred embodiment of the present application, the path planning step of the global power grid inspection stage is:

[0021] The last target point of the key disaster point inspection stage is taken as a starting point, target points with disaster levels less than a threshold value are taken as target points, the starting point is added to an open list and the starting point is regarded as a current node according to the disaster level size;

[0022] When the open list is not empty, the following steps are repeated: adjacent nodes of the current node are traversed, if the adjacent node has been stored in a closed list, the node is ignored, if the adjacent node is not in the open list and the closed list, the node is added to the open list as a to-be-explored node, the to-be-explored node stored in the open list is evaluated by a node evaluation function, the current node is taken as a parent node of the to-be-explored node with the minimum evaluation value and the to-be-explored node is taken as the current node, then the parent node of the current node is removed from the open list and added to the closed list; if the adjacent node is already in the open list, actual movement cost from the starting point to the current node is compared with actual movement cost from the starting point to the adjacent node, if the actual movement cost from the starting point to the adjacent node is smaller, the parent node of the adjacent node is taken as the current node and the current node is re-evaluated by the node evaluation function, the step is repeated until the current node is the target point, and exploration is successful;

[0023] When the open list is empty, it is indicated that no path is found, and exploration fails;

[0024] When a target point is successfully explored, the target point is taken as a starting point to explore a next target point, and all target points are explored according to the sorting.

[0025] As a preferred embodiment of the present application, the energy consumption model of the unmanned aerial vehicle is:

[0026] E=P×T+P0×T0

[0027] Wherein, E represents total energy consumption of the unmanned aerial vehicle, P represents power of a propeller of the unmanned aerial vehicle, T represents flight time, P0 represents average power when the unmanned aerial vehicle is in a stationary standby state, and T0 represents time when the unmanned aerial vehicle is in the stationary standby state;

[0028] In the path planning process, the distance D between the current node and the previous node is calculated according to the positions of the two nodes, and then the energy cost C(n) of the current node n is obtained based on the energy consumption model of the UAV: C(n) = P x T + P0 x T0 = (D / v) x P + P0 x T0 Wherein: v is the flight speed of the UAV. As a preferred embodiment of the present application, the node evaluation function F(n) of the A-star algorithm model improved by the energy consumption model of the UAV is: F(n) = G(n) + H(n) + C(n) Wherein: G(n) is the actual movement cost from the starting point to the current node; H(n) is the heuristic estimation cost from the current node to the target point; C(n) represents the energy cost of the current node; Before evaluating the current node by the node evaluation function F(n), the costs of the current node need to be normalized: Normalizing the G cost: obtaining all G values, and screening out the maximum value G max , the minimum value G min , and then normalizing the G value of the current node: Normalizing the H cost: obtaining all H values, and screening out the maximum value H max , the minimum value H min , and then normalizing the H value of the current node: Normalizing the energy cost C: obtaining all C values, and screening out the maximum value C max , the minimum value C min , and then normalizing the C value of the current node: As a preferred embodiment of the present application, the nodes where the obstacles in the three-dimensional grid map are calculated by the geo-fencing algorithm. As a preferred embodiment of the present application, the UAV safety interval is set to deal with flight conflicts with other UAVs in the path planning process, and the specific steps are as follows:

[0029] The time T of the two UAVs to reach the closest point CPA is set CPA , and T is approximately estimated by the approximation value τ CPA , and τ is defined as follows:

[0030]

[0031] Wherein: r is the relative distance between the two machines; is the change rate of the relative distance between the two machines;

[0032] A fixed interval DMOD is set around the UAV, and τ is corrected by DMOD to obtain τ mod , which is specifically shown as follows:

[0033]

[0034] A horizontal interval HMD is introduced in the horizontal direction as a fixed interval value when the two aircrafts reach the closest point CPA, and the calculation formula of the horizontal interval HMD is as follows:

[0035]

[0036] wherein d x , d y respectively represent the horizontal interval distance of the two aircrafts in x and y dimensions; v rx , v ry respectively represent the relative speed of the two aircrafts in x and y dimensions;

[0037] In summary, the judgment condition of the safety interval of the unmanned aircraft is defined as follows:

[0038] WCV≡HWCV and VWCV

[0039] HWCV≡[‖s‖≤DMOD]or([HMD≤HMD * ]AND[0≤τ mod ≤τ * mod ])

[0040] VWCV≡[-h * ≤d h ≤h * ]

[0041] wherein WCV represents the safety interval of the unmanned aircraft; HWCV represents the horizontal safety interval of the unmanned aircraft; VWCV represents the vertical safety interval of the unmanned aircraft; s represents the horizontal position information of the unmanned aircraft; HMD * represents the set HMD threshold value, which is equal to DMOD; τ * mod represents the set τ mod threshold value; d h represents the current vertical interval distance of the two aircrafts; h * represents the vertical safety interval threshold value.

[0042] On the other hand, the application further provides an unmanned aircraft post-disaster emergency path planning system based on A-star algorithm, comprising a map construction module, an algorithm initialization module and a path planning module;

[0043] The map construction module is used for constructing a three-dimensional grid map of the unmanned aircraft flight area based on the power grid area, setting a starting point and a target point in the map, wherein the target point is the disaster occurrence site of the power grid area, and setting the disaster grade of each target point;

[0044] The algorithm initialization module is used for initializing an A-star algorithm model, creating empty open list and closed list, and adding nodes where obstacles in the three-dimensional grid map are located into the closed list, and improving a node evaluation function of the A-star algorithm model through an energy consumption model of the unmanned aerial vehicle.

[0045] The path planning module is used for planning the post-disaster emergency path of the unmanned aerial vehicle through the A-star algorithm model.

[0046] In another aspect, the application further provides an electronic device, including a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor implements the method according to any one of the embodiments of the application when executing the program.

[0047] In another aspect, the application further provides a computer readable storage medium, which stores a computer program, and the program is executable on a processor to implement the method according to any one of the embodiments of the application.

[0048] The application has the following beneficial effects:

[0049] 1. The application introduces an unmanned aerial vehicle energy consumption model into an A-star algorithm and uses the energy consumption model as an evaluation function, so that the required energy consumption between path points can be evaluated through energy consumption cost, and the path planning can be more effectively performed to prevent the unmanned aerial vehicle from flying in a path planning scheme that cannot meet the requirements of post-disaster inspection.

[0050] 2. The application divides a traditional A* algorithm path planning process into a key disaster point inspection stage and a global power grid inspection stage. The starting point of the key disaster point inspection stage is a current position, and the target point is a key disaster point. The starting point of the global power grid inspection stage is the key disaster point, and the target point is the end point of power grid inspection. Through this design, the planning process of first key and then global can be met, and the unmanned aerial vehicle can orderly perform global inspection.

[0051] 3. The application uses a keep-out geographic fence algorithm and a Well Clear safety interval standard to maintain a safety interval between the unmanned aerial vehicle and static obstacles and dynamic obstacles, which improves the safety of the unmanned aerial vehicle flight and reduces the risk of collision. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 The figure is a method flowchart of the application.

[0053] Figure 2 The figure is a disaster grade division schematic diagram of the application.

[0054] Figure 3 The figure is a geographic fence schematic diagram of the application.

[0055] Figure 4 Figure 1 is a schematic diagram of the horizontal safety interval of the unmanned aerial vehicle of the present application;

[0056] Figure 5 Figure 2 is a schematic diagram of the safety interval of the unmanned aerial vehicle of the present application. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present application will be clearly and completely described in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0058] It should be understood that the step numbers used herein are only for the convenience of description, and are not limited to the execution sequence of the steps.

[0059] It should be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and the appended claims of the present application, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0060] The terms "comprise" and "include" indicate the presence of described features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0061] The term "and / or" means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0062] Embodiment one:

[0063] Referring to Figure 1 , a post-disaster emergency path planning method for an unmanned aerial vehicle based on the A-star algorithm, comprising the following steps:

[0064] A three-dimensional grid map of the unmanned aerial vehicle flight area is constructed based on the power grid area, and a starting point and a target point are set in the map, wherein the target point is the disaster occurrence location of the power grid area, and the disaster level of each target point is set, as shown in Figure 2 The disaster points are divided into the following levels:

[0065] 1. General or low (General / Low): refers to a disaster point with relatively small impact, such as line tree collision, slight short circuit of transmission line, general equipment failure, etc.

[0066] 2. Level 2 / Moderate: refers to disaster points with relatively large influence, such as partial failure of a line, serious blockage of a radiator by water scale, etc.

[0067] 3. Level 3 / Large: refers to disaster points with relatively serious influence, such as serious failure of a line, major failure of equipment in a substation, or damage to a single transformer, etc.

[0068] 4. Level 4 / Severe: refers to disaster points with serious influence, such as overvoltage breakdown in a substation, serious grounding fault, or generation of a seriously affected area.

[0069] 5. Level 5 / Major: refers to disasters that seriously endanger the stable operation of the power grid, such as power grid fracture, serious failure of a substation, and power outage, etc.

[0070] Initialize the A* algorithm model, create empty open list and closed list, and add nodes where obstacles in the three-dimensional grid map are located to the closed list, and improve the node evaluation function of the A* algorithm model through the energy consumption model of the unmanned aerial vehicle;

[0071] Plan the post-disaster emergency path of the unmanned aerial vehicle through the A* algorithm model.

[0072] As a preferred embodiment of the present embodiment, the post-disaster emergency path planning of the unmanned aerial vehicle includes path planning in the key disaster point inspection stage and path planning in the global power grid inspection stage;

[0073] The path planning steps in the key disaster point inspection stage are:

[0074] Set the target points with disaster levels greater than a threshold value as the target points of path planning in this stage, sort them according to the disaster level, add the starting point to the open list and regard the starting point as the current node;

[0075] When the open list is not empty, repeat the following steps: traverse the adjacent nodes of the current node, if the adjacent node has been stored in the closed list, ignore the node, if the adjacent node is not in the open list and the closed list, add the node to the open list as a to-be-explored node, evaluate the to-be-explored node stored in the open list by the node evaluation function, take the current node as the parent node of the to-be-explored node with the minimum evaluation value and take the to-be-explored node as the current node, then remove the parent node of the current node from the open list and add it to the closed list; if the adjacent node is already in the open list, compare the actual movement cost from the starting point to the current node with the actual movement cost from the starting point to the adjacent node, if the actual movement cost from the starting point to the adjacent node is smaller, take the parent node of the adjacent node as the current node and re-evaluate the current node by the node evaluation function, repeat the step until the current node is the target point, and the exploration is successful;

[0076] When the open list is empty, it means that no path is found, and the exploration fails.

[0077] When a target point is successfully explored, take the target point as the starting point to explore the next target point, and explore all target points according to the sorting.

[0078] As a preferred embodiment of the present embodiment, the path planning step in the global power grid inspection stage is:

[0079] Take the last target point in the key disaster point patrol stage as the starting point, take the target point with a disaster level less than a threshold value as the target point, and sort the target points according to the disaster level, add the starting point to the open list and take the starting point as the current node;

[0080] When the open list is not empty, repeat the following steps: traverse the adjacent nodes of the current node, if the adjacent node has been stored in the closed list, ignore the node, if the adjacent node is not in the open list and the closed list, add the node to the open list as a to-be-explored node, evaluate the to-be-explored node stored in the open list by the node evaluation function, take the current node as the parent node of the to-be-explored node with the minimum evaluation value and take the to-be-explored node as the current node, then remove the parent node of the current node from the open list and add it to the closed list; if the adjacent node is already in the open list, compare the actual movement cost from the starting point to the current node with the actual movement cost from the starting point to the adjacent node, if the actual movement cost from the starting point to the adjacent node is smaller, take the parent node of the adjacent node as the current node and re-evaluate the current node by the node evaluation function, repeat the step until the current node is the target point, and the exploration is successful;

[0081] When the open list is empty, it means that no path is found, and the exploration fails.

[0082] When a target point is successfully explored, the target point is taken as a starting point to explore the next target point, and all target points are explored according to the sorting.

[0083] As a preferred embodiment of the present embodiment, the energy consumption model of the unmanned aerial vehicle is:

[0084] E = P x T + P0 x T0

[0085] Wherein, E represents the total energy consumption of the unmanned aerial vehicle; P represents the power of the propeller of the unmanned aerial vehicle; T represents the flight time; P0 represents the average power when the unmanned aerial vehicle is in standby state; T0 represents the time when the unmanned aerial vehicle is in standby state;

[0086] In the path planning process, the Euclidean distance D between the current node and the previous node is calculated according to the positions of the two nodes, and then the energy consumption cost C(n) of the current node n is obtained based on the energy consumption model of the unmanned aerial vehicle:

[0087] C(n) = P x T + P0 x T0 = (D / v) x P + P0 x T0

[0088] Wherein, v is the flight speed of the unmanned aerial vehicle.

[0089] As a preferred embodiment of the present embodiment, the node evaluation function F(n) of the A-star algorithm model improved by the energy consumption model of the unmanned aerial vehicle is:

[0090] F(n) = G(n) + H(n) + C(n)

[0091] Wherein, G(n) is the actual movement cost from the starting point to the current node; H(n) is the heuristic estimation cost from the current node to the target point; C(n) represents the energy consumption cost of the current node.

[0092] Before the node evaluation function F(n) evaluates the current node, the costs of the current node need to be normalized to make different costs have the same importance in the path planning process, so as to avoid that a particular cost has too large or too small influence on the planning result:

[0093] The G cost is normalized:

[0094] All G values are obtained, and the maximum value G max and the minimum value G min are screened out, and then the G value of the current node is normalized:

[0095] The H cost is normalized:

[0096] All H values are obtained, and the maximum value H max and the minimum value H minThe H value of the current node is normalized again:

[0097] The energy consumption cost C is normalized:

[0098] All C values are obtained, and the maximum value C is screened out max The minimum value C min The C value of the current node is normalized again:

[0099] As a preferred embodiment of the present embodiment, the node where the obstacle in the three-dimensional grid map is calculated by the geo-fencing algorithm.

[0100] In the present embodiment, the Keep-out geo-fencing algorithm is used to maintain a safe separation distance between the UAV and the obstacle. "Keep-out" is a concept of geo-fencing, which is used to describe an area or boundary that limits the range of activities of aircraft in airspace. When the UAVs are located within this area, they may be restricted or monitored; if they attempt to enter or leave this area, it will be considered as a violation, as shown in the schematic diagram Figure 3 .

[0101] The Keep-out geo-fencing protection zone size δ can be determined according to the size of the UAV and the size of the obstacle, and is usually based on the original size of the obstacle plus the size of the UAV.

[0102] As a preferred embodiment of the present embodiment, during the operation of the aircraft, considering the influence of wake, flight conflict, etc., the flight safety of the aircraft, the "Air Rules" formulated by the International Civil Aviation Organization requires that the aircraft must maintain a certain safety separation distance from other aircraft during operation to ensure the safety of the aircraft operation, and the concept of Well Clear (WC) is used to represent the safety separation standard of the aircraft operation. Well Clear safety separation refers to a means of avoiding traffic conflicts by setting up a spatial boundary around the aircraft or setting a time interval threshold in the direction of the aircraft operation.

[0103] For dynamic obstacles existing in the process of UAV path planning, the present application introduces the UAV safety separation Well Clear according to the safety separation standard.

[0104] The UAV safety separation is set to deal with the flight conflict with other UAVs in the process of path planning, and the specific steps are as follows:

[0105] The time T of the two UAVs to reach the closest point CPA is set CPA , and T is approximately estimated by the approximation value τ (Tau) CPA , and τ is defined as follows:

[0106]

[0107] where: r is the relative distance between two aircrafts; is the relative distance change rate between two aircrafts;

[0108] Generally, the calculated value of τ is not equal to T CPA , when two aircrafts are closer to the CPA point, the value of τ is closer to infinity, and the approximation error is larger; in addition, if two aircrafts are in a slow approach state, the relative change rate is very small, at this time, the calculated value of τ is very large, if the relative distance between two aircrafts is very close at this time, once the intruder aircraft suddenly approaches, it cannot give the UAV enough time to react, therefore, a fixed interval DMOD is set around the UAV, and τ is modified by DMOD to obtain τ mod , if the intruder aircraft suddenly accelerates, the interval can give the UAV a certain reaction time, which is shown in the following formula:

[0109]

[0110] Referring to Figure 4 , the safety interval of the UAV in the horizontal direction is to form a cylindrical interval region around the aircraft, once the system detects that the intruder aircraft intrudes into the region, it is determined that a conflict occurs, this binary judgment standard is simple and intuitive, but it is difficult to intuitively show the conflict relationship between two aircrafts when they converge in the future, if the distance between two aircrafts is too small when they converge, it is also unsafe, therefore, a horizontal interval HMD is introduced in the horizontal direction as a fixed interval value when two aircrafts reach the closest point CPA, HMD can conveniently show the real conflict situation, and has a clearer judgment on the future conflict relationship between two aircrafts, the calculation formula of the horizontal interval HMD is as follows:

[0111]

[0112] where: d x , d y respectively represent the horizontal interval distance of two aircrafts in x and y dimensions; v rx , v ry respectively represent the relative speed of two aircrafts in x and y dimensions;

[0113] Referring to Figure 5 , in summary, the judgment condition of the safety interval of the UAV is defined as follows:

[0114] WCV≡HWCV and VWCV

[0115] HWCV≡[‖s‖≤DMOD]or([HMD≤HMD * ]AND[0≤τmod ≤τ * mod ])

[0116] VWCV≡[-h * ≤d h ≤h * ]

[0117] wherein: WCV represents the safety interval of the UAV; HWCV represents the horizontal safety interval of the UAV; VWCV represents the vertical safety interval of the UAV; s represents the horizontal position information of the UAV; HMD * represents the set HMD threshold, equal to DMOD; τ * mod represents the set τ mod threshold; d h represents the current vertical interval distance of the two UAVs; h * represents the vertical safety interval threshold.

[0118] Embodiment Two:

[0119] A UAV post-disaster emergency path planning system based on A-star algorithm, comprising a map construction module, an algorithm initialization module and a path planning module;

[0120] The map construction module is configured to construct a three-dimensional grid map of a UAV flight area based on a power grid area, and set a starting point and a target point in the map, wherein the target point is a disaster occurrence location of the power grid area, and set a disaster level of each target point;

[0121] The algorithm initialization module is configured to initialize an A-star algorithm model, create an empty open list and a closed list, and add nodes where obstacles are located in the three-dimensional grid map to the closed list, and simultaneously improve a node evaluation function of the A-star algorithm model through a UAV energy consumption model;

[0122] The path planning module is configured to plan a UAV post-disaster emergency path through the A-star algorithm model.

[0123] The system is used to realize the functions in Embodiment One, and will not be described here again.

[0124] Embodiment Three:

[0125] The embodiment provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to realize the method according to any one of the embodiments.

[0126] Embodiment Four:

[0127] The embodiment provides a computer readable storage medium, and a computer program is stored on the computer readable storage medium. The computer program is executed by a processor to implement the method in any embodiment of the application.

[0128] In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. The "and / or" describes the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B, which can represent the cases of A alone, A and B together, and B alone. Wherein A and B can be singular or plural. The character " / " generally represents that the associated objects before and after it are in an "or" relationship. "At least one of the following" and the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, wherein a, b, c can be single or multiple.

[0129] Those skilled in the art can realize that each unit and algorithm step described in the embodiments disclosed in the present application can be realized by electronic hardware, computer software and combination of electronic hardware and computer software. Whether the functions are realized by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0130] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0131] In several embodiments provided in the present application, any function, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or the parts of the technical solutions that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes. The above is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent flow transformation obtained by using the content of the specification and drawings of the present application, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A method for post-disaster emergency path planning of a UAV based on A* algorithm, characterized in that, The method comprises the following steps: constructing a three-dimensional grid map of a UAV flight area based on a power grid area, and setting a starting point and a target point in the map, wherein the target point is a disaster occurrence location of the power grid area, and a disaster level of each target point is set; initializing an A-star algorithm model, creating an empty open list and a closed list, and adding nodes where obstacles in the three-dimensional grid map are located to the closed list, and improving a node evaluation function of the A-star algorithm model through a UAV energy consumption model; planning a UAV post-disaster emergency path through the A-star algorithm model; the UAV energy consumption model is: E=P×T+P0×T0 wherein E represents total energy consumption of the UAV, P represents power of a UAV propeller, T represents flight time, P0 represents average power of the UAV when it is at rest, and T0 represents time when the UAV is at rest; in the path planning process, distance D between a current node and a previous node is calculated according to positions of the two nodes, and then energy consumption cost C(n) of the current node is obtained based on the UAV energy consumption model: C(n)=P×T+P0×T0=(D / v)×P+P0×T0 wherein v is flight speed of the UAV; the node evaluation function F(n) of the A-star algorithm model improved through the UAV energy consumption model is: F(n)=G(n)+H(n)+C(n) wherein G(n) is actual movement cost from the starting point to the current node, H(n) is heuristic estimation cost from the current node to the target point, and C(n) represents energy consumption cost of the current node; before the node evaluation function F(n) evaluates the current node, each cost of the current node needs to be normalized: the G cost is normalized as follows: Get all G values and filter out the maximum G value G max , the minimum G value G min , and normalize the G value of the current node: the H cost is normalized as follows: Get all H values, and filter out the maximum value H max , the minimum value H min , and normalize the H value of the current node: the energy consumption cost C is normalized as follows: Get all C values and filter out the maximum value C max Get all C values and filter out the minimum value C min Normalize the C value of the current node again:

2. The method of claim 1, wherein, the UAV post-disaster emergency path planning comprises path planning in a key disaster point inspection stage and path planning in a global power grid inspection stage; the path planning in the key disaster point inspection stage comprises the following steps: target points with disaster levels greater than a threshold value are set as target points of path planning in the stage, the target points are sorted according to the disaster level, the starting point is added to the open list, and the starting point is regarded as a current node; when the open list is not empty, the following steps are repeated: adjacent nodes of the current node are traversed, if an adjacent node is stored in the closed list, the node is ignored, if an adjacent node is not in the open list or the closed list, the node is added to the open list as a to-be-explored node, the to-be-explored node stored in the open list is evaluated through the node evaluation function, the current node is regarded as a parent node of the to-be-explored node with the minimum evaluation value, and the to-be-explored node is regarded as the current node, then the parent node of the current node is removed from the open list and added to the closed list; if an adjacent node is already in the open list, actual movement cost from the starting point to the current node is compared with actual movement cost from the starting point to the adjacent node, if actual movement cost from the starting point to the adjacent node is smaller, the parent node of the adjacent node is regarded as the current node, and the current node is re-evaluated through the node evaluation function, the step is repeated until the current node is the target point, and exploration is successful; When the open list is empty, it means that no path is found, and the exploration fails; When a target point is successfully explored, the target point is taken as a starting point to explore the next target point, and all target points are explored according to the sorting.

3. The method of claim 2, wherein, The path planning step of the global power grid inspection stage is: The last target point in the key disaster point inspection stage is taken as a starting point, target points with disaster levels less than a threshold value are taken as target points, the starting point is added to an open list, and the starting point is regarded as a current node according to the disaster level size; When the open list is not empty, the following steps are repeated: adjacent nodes of the current node are traversed, if the adjacent node has been stored in a closed list, the node is ignored, if the adjacent node is not in the open list and the closed list, the node is added to the open list as a to-be-explored node, the to-be-explored node stored in the open list is evaluated by a node evaluation function, the current node is taken as a parent node of the to-be-explored node with the minimum evaluation value, and the to-be-explored node is taken as the current node, and then the parent node of the current node is removed from the open list and added to the closed list; If the adjacent node is already in the open list, the actual movement cost from the starting point to the current node is compared with the actual movement cost from the starting point to the adjacent node, if the actual movement cost from the starting point to the adjacent node is smaller, the parent node of the adjacent node is taken as the current node, and the current node is re-evaluated by the node evaluation function, and the step is repeated until the current node is the target point, and the exploration is successful; When the open list is empty, it means that no path is found, and the exploration fails; When a target point is successfully explored, the target point is taken as a starting point to explore the next target point, and all target points are explored according to the sorting.

4. The method of claim 1, wherein, The nodes where the obstacles in the three-dimensional grid map are located are calculated by a geographic fence algorithm.

5. The method of claim 1, wherein, The safety interval of the unmanned aerial vehicle is set to deal with flight conflicts with other unmanned aerial vehicles in the path planning process, and the specific steps are as follows: Setting the time T for the two drones to reach the closest point CPA CPA And approximating T by an approximation τ CPA The definition of τ is as follows: wherein: r is the relative distance between the two machines; is the rate of change of the relative distance between the two machines; A fixed interval DMOD is set around the UAV, and τ is corrected by DMOD to obtain τ mod , as shown in the following formula: A horizontal interval HMD is introduced in the horizontal direction as a fixed interval value when two machines reach a closest point CPA, and the calculation formula of the horizontal interval HMD is as follows: wherein: d x , d y respectively represent the horizontal interval distance of the two machines in x, y dimensions; v rx , v ry respectively represent the relative speed of the two machines in x, y dimensions; In summary, the judgment condition of the safety interval of the unmanned aerial vehicle is defined as follows: WCV≡HWCV and VWCV HWCV≡ [‖s‖≤DMOD] or ([HMD≤HMD * ] AND [0≤τ mod ≤τ * mod ]) Wherein: WCV represents the safety interval of the UAV; HWCV represents the horizontal safety interval of the UAV; VWCV represents the vertical safety interval of the UAV; s represents the horizontal position information of the UAV; HMD * represents the set HMD threshold, equal to DMOD; τ * mod represents the set τ mod threshold; d h represents the current vertical interval distance of the two machines; h * represents the vertical safety interval threshold.

6. A UAV post-disaster emergency path planning system based on A* algorithm, characterized in that, The method is used to realize the method in any one of claims 1 to 5, comprising a map construction module, an algorithm initialization module and a path planning module; The map construction module is used to construct a three-dimensional grid map of an unmanned aerial vehicle flight area based on a power grid area, and set a starting point and a target point in the map, wherein the target point is a disaster occurrence location of the power grid area, and set a disaster level of each target point; The algorithm initialization module is used to initialize an A-star algorithm model, create empty open and closed lists, and add nodes where obstacles in the three-dimensional grid map are located to the closed list, and improve a node evaluation function of the A-star algorithm model through an energy consumption model of the unmanned aerial vehicle; The path planning module is used to plan a post-disaster emergency path of the unmanned aerial vehicle through the A-star algorithm model.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the method in any one of claims 1 to 5 when executing the program.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by the processor, implements the method of any one of claims 1 to 5.

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