CN114326787A - Unmanned aerial vehicle autonomous return route planning method, electronic equipment and medium - Google Patents
Unmanned aerial vehicle autonomous return route planning method, electronic equipment and medium Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle autonomous return route planning method, electronic equipment and a medium, wherein the planning method comprises the following steps: when the unmanned aerial vehicle needs to autonomously return to the flight path, judging whether the current position of the unmanned aerial vehicle is on the designed flight path or not, if so, adding the current position point of the unmanned aerial vehicle as a return source point into a flight point list, otherwise, controlling the unmanned aerial vehicle to return to a cut-out point of the finally cut-out designed flight path along the original flight route, and adding the cut-out point as the return source point into the flight point list; merging waypoints with the same characteristic values and spatial coordinates meeting threshold conditions in the waypoint list to form a waypoint route with a linear list structure; converting the linear table structure of the waypoint route into an undirected graph structure, taking the horizontal distance of adjacent waypoints as the weight of an edge in the undirected graph, and representing the topological relation of the waypoint route by using the undirected graph with the weight; and solving the shortest path from the return source point to the landing approach point from the weighted undirected graph by adopting a single-source shortest path algorithm to serve as a return airway.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an unmanned aerial vehicle autonomous return route planning method, electronic equipment and a medium.
Background
The large-scale fixed wing unmanned aerial vehicle has the advantages of high flying speed, long flight time, long flight distance, strong mounting capacity and the like, and plays an increasingly important role in the field of modern military. The unmanned aerial vehicle has high reliability, viability and autonomous decision-making capability, so that the unmanned aerial vehicle can successfully complete tasks such as reconnaissance and striking in a complex environment and safely return to the home.
The unmanned aerial vehicle should possess the ability of independently returning voyage, when the unmanned aerial vehicle system broke down or ground communication link disconnection leads to unmanned aerial vehicle unsuitable to continue to carry out current task again promptly, it can independently make the judgement and return to the airport according to safe reasonable route.
The scheme of planning the autonomous return flight path commonly used by the existing unmanned aerial vehicle is roughly divided into two types: firstly, unmanned aerial vehicle climbs earlier to safe altitude, then directly returns the airport according to the line fairway of present position and airport position, lands after the height that falls again, though flight distance is the shortest in this kind of scheme, but unmanned aerial vehicle is not according to the flight of design fairway, probably through unmanned aerial vehicle such as cities and towns forbid the flight area, has the potential safety hazard. And secondly, the unmanned aerial vehicle stores the waypoints through which the aircraft finishes flying, returns to the airport along the original routes of the waypoints during return voyage, or continues to fly forwards along the originally designed routes until landing. Although the safety of unmanned aerial vehicle flight route has been guaranteed to this kind of scheme, but the route of returning the journey is often not optimal route, and longer journey has also increased unmanned aerial vehicle and has taken place dangerous possibility.
Therefore, the unmanned aerial vehicle autonomous return flight path planning method is expected to optimize the return flight path and reduce the return flight path on the premise of ensuring the safety of the flight path, so that the unmanned aerial vehicle can safely and rapidly return to the airport autonomously.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle autonomous return route planning method, electronic equipment and a medium, which can optimize a return route, reduce return range and enable an unmanned aerial vehicle to safely and quickly return to an airport autonomously on the premise of ensuring the safety of a flight route.
In order to achieve the purpose, the invention provides an unmanned aerial vehicle autonomous return route planning method, which comprises the following steps:
step 1: when the unmanned aerial vehicle needs to autonomously return to the flight, judging whether the current position of the unmanned aerial vehicle is on a designed route, if so, adding the current position point of the unmanned aerial vehicle as a return source point into a route point list, otherwise, controlling the unmanned aerial vehicle to return to a cut-out point which is finally cut out of the designed route along an original flight route, and adding the cut-out point as a return source point into a route point list, wherein the route point list comprises route point sequences among a plurality of route points in the designed route, and space coordinates and characteristic values of each route point;
step 2: merging waypoints with the same characteristic value and spatial coordinates meeting a threshold condition in a waypoint list to form a waypoint route with a linear table structure, wherein the merged waypoint is represented by the waypoint with the smallest sequence number in the merged waypoints, and the merged waypoint inherits the connection relation of other merged waypoints;
and step 3: converting the linear table structure of the waypoint route into an undirected graph structure, taking the horizontal distance of adjacent waypoints as the weight of the edge in the undirected graph, and representing the topological relation of the waypoint route by using the weighted undirected graph;
and 4, step 4: and solving the shortest path from the return flight source point to the landing approach point from the weighted undirected graph by adopting a single-source shortest path algorithm to serve as a return flight path.
In an alternative, the threshold condition is: the horizontal distance between the merged waypoints is less than or equal to 500 meters and the height difference between the merged waypoints is less than or equal to 20 meters.
In an alternative, in step 3, the calculation formula of the horizontal distance is as follows:
wherein d is the horizontal distance between two waypoints, R is the radius of the earth, and alpha1、α2Respectively representing the latitude, beta, of two waypoints1、β2Respectively, the longitudes of the two waypoints.
In an alternative, in step 4, the number of the landing approach points is multiple, and the step of solving the shortest path from the return source point to the landing approach point as a return airway includes:
and respectively solving the shortest path from the source point to each landing approach point, and selecting the air route with the shortest path as a return air route.
In an alternative, the single-source shortest path algorithm comprises a Dijkstra algorithm.
In an alternative scheme, in step 1, the condition that the unmanned aerial vehicle needs to return autonomously includes:
the unmanned aerial vehicle needs to return to the home automatically after receiving an autonomous return instruction sent by the ground station; alternatively, the first and second electrodes may be,
the unmanned aerial vehicle needs to return to the home after judging that the system is in fault; alternatively, the first and second electrodes may be,
the unmanned aerial vehicle needs to return to the home independently when the ground communication link is disconnected.
In an alternative, the method further comprises: the unmanned aerial vehicle receives an instruction of withdrawing from the autonomous return flight in real time in the autonomous return flight process, and flies according to an appointed route after withdrawing from the autonomous return flight.
In an alternative, the method further comprises: after the unmanned aerial vehicle flies to the landing approach point, the unmanned aerial vehicle is in a hovering and high-altitude state, and then lands to return to an airport after the unmanned aerial vehicle is in a set altitude state.
The present invention also provides an electronic device, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the unmanned aerial vehicle autonomous return route planning method described above.
The invention also provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the unmanned aerial vehicle autonomous return route planning method.
The invention has the beneficial effects that:
the invention firstly processes the waypoint routes, converts the topological relation of the waypoint routes from a linear table structure into a weighted undirected graph structure, and applies the shortest path algorithm of the undirected graph to solve and calculate the shortest path of return flight, so that the return route can be optimized, the return route can be reduced on the premise of ensuring the safety of the flight routes, and the unmanned aerial vehicle can safely and quickly return to the airport independently.
Furthermore, the unmanned aerial vehicle can autonomously return after receiving an autonomous return instruction sent by the ground station, or can autonomously return after judging that the system is in failure, or can autonomously return when the ground communication link is disconnected; the unmanned aerial vehicle can receive an instruction of withdrawing from the autonomous return flight in real time in the autonomous return flight process, and flies according to the designated route after withdrawing from the autonomous return flight, so that the flexibility of the unmanned aerial vehicle in return flight is improved.
The present invention has other features and advantages which will be apparent from or are set forth in detail in the accompanying drawings and the following detailed description, which are incorporated herein, and which together serve to explain certain principles of the invention.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings.
Fig. 1 shows a flowchart of an autonomous return route planning method for an unmanned aerial vehicle according to an embodiment of the present invention.
FIG. 2 illustrates a design routability graph according to an embodiment of the present invention.
FIG. 3 is a waypoint route map that combines waypoints in the waypoint list that have the same characteristic values and whose spatial coordinates satisfy the threshold condition in accordance with an embodiment of the invention.
Fig. 4 shows a weighted undirected graph of a drone rout graph according to an embodiment of the invention.
Detailed Description
The present invention will be described in more detail below. While the present invention provides preferred embodiments, it should be understood that the present invention may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
An embodiment of the invention provides an autonomous return route planning method for an unmanned aerial vehicle, and fig. 1 shows a flow chart of the autonomous return route planning method for the unmanned aerial vehicle according to the embodiment of the invention. Referring to fig. 1, the planning method includes:
step 1: when the unmanned aerial vehicle needs to autonomously return to the flight, judging whether the current position of the unmanned aerial vehicle is on a designed route, if so, adding the current position point of the unmanned aerial vehicle as a return source point into a route point list, otherwise, controlling the unmanned aerial vehicle to return to a cut-out point which is finally cut out of the designed route along an original flight route, and adding the cut-out point as a return source point into a route point list, wherein the route point list comprises route point sequences among a plurality of route points in the designed route, and space coordinates and characteristic values of each route point;
step 2: merging waypoints with the same characteristic value and spatial coordinates meeting a threshold condition in a waypoint list to form a waypoint route with a linear table structure, wherein the merged waypoint is represented by the waypoint with the smallest sequence number in the merged waypoints, and the merged waypoint inherits the connection relation of other merged waypoints;
and step 3: converting the linear table structure of the waypoint route into an undirected graph structure, taking the horizontal distance of adjacent waypoints as the weight of the edge in the undirected graph, and representing the topological relation of the waypoint route by using the weighted undirected graph;
and 4, step 4: and solving the shortest path from the return flight source point to the landing approach point from the weighted undirected graph by adopting a single-source shortest path algorithm to serve as a return flight path.
The invention firstly processes the waypoint routes, converts the topological relation of the waypoint routes from a linear table structure into a weighted undirected graph structure, and applies the shortest path algorithm of the undirected graph to solve and calculate the shortest path of return flight, so that the return route can be optimized, the return route can be reduced on the premise of ensuring the safety of the flight routes, and the unmanned aerial vehicle can safely and quickly return to the airport independently.
Specifically, the unmanned aerial vehicle navigation route is formed by n navigation points which are sequentially connected, and each navigation point has longitude, latitude, height and characteristic value information. The longitude, the latitude and the altitude are position information of a waypoint; the feature values represent features of waypoints used to distinguish between source points, landing approach points, landing drift points and other waypoints. The waypoint sequence is in a linear table structure, namely when 1 < i < n, the direct front driving waypoint of the waypoint i is a waypoint i-1, the direct successor waypoint of the waypoint i is a waypoint i +1, and except that the first waypoint has no front driving waypoint and the last waypoint has no successor waypoint, each of the other waypoints has one and only one direct front driving waypoint and one direct successor waypoint.
The mission flight of the large-scale fixed-wing unmanned aerial vehicle generally comprises the stages of take-off, climbing, mission altitude cruise, height descending and landing. The device can climb and slide down for many times in the process of executing tasks, and can also fly away from the original designed air route according to the remote control instruction of the ground station.
The autonomous return solution provided by the invention is that whether the current position of the unmanned aerial vehicle is on a designed air route or not is judged, and if the current position of the unmanned aerial vehicle is on the designed air route, the current position of the unmanned aerial vehicle is added into a waypoint list; and if the current position of the unmanned aerial vehicle is not on the designed route, controlling the unmanned aerial vehicle to return to the point of the last cut-out route along the original flight route, and then adding the cut-out point into the waypoint list.
And then merging the waypoints with the same spatial coordinates (longitude, latitude, altitude) and characteristic values, and representing the waypoints by the waypoints with the smaller sequence numbers, wherein the waypoints inherit the connection relation of other waypoints. The waypoints with small longitude, latitude and altitude differences can also be combined, the threshold condition can be set according to the actual requirement, and in one embodiment, for a large-scale fixed-wing unmanned aerial vehicle, the threshold condition is as follows: the horizontal distance between the merged waypoints is less than or equal to 500 meters and the height difference between the merged waypoints is less than or equal to 20 meters. In other embodiments, other threshold conditions may be set according to other situations, such as merging waypoints whose longitude, latitude, and altitude satisfy a set functional relation. After the combination is completed, the topological relation of the waypoint routes is converted from a linear table structure into an undirected graph structure, and at the moment, other waypoints can have a plurality of direct front driving waypoints and a plurality of direct subsequent waypoints except that the first waypoint does not have a front driving waypoint and the last waypoint does not have a subsequent waypoint. And then, taking the horizontal distance of the adjacent waypoints as a weight, and representing the topological relation of the waypoint and the route by using a weighted undirected graph.
And finally, taking the current position or the route switching-out point of the unmanned aerial vehicle as a source point, applying a single-source shortest path algorithm of the weighted undirected graph to obtain the shortest path from the source point to two landing approach points at two ends of the airport runway, and selecting one of the shortest paths as a return route.
The single-source shortest path algorithm adopts Dijkstra algorithm, which is a single-source shortest path algorithm and can be used for calculating the shortest path from one waypoint to all other waypoints. In other embodiments, the single-source shortest path algorithm further comprises an SPFA algorithm, a Floyd algorithm, or a Johnson algorithm.
In one embodiment, in step 1, the condition that the drone needs to return autonomously includes:
the unmanned aerial vehicle needs to return to the home automatically after receiving an autonomous return instruction sent by the ground station; or the unmanned aerial vehicle needs to return to the home after judging that the system fails; or, the unmanned aerial vehicle needs to return to the home autonomously when the ground communication link is disconnected. Unmanned aerial vehicle can carry out independently returning a voyage under the multiple condition, improves the flexibility that unmanned aerial vehicle returned a voyage.
In one embodiment, in step 3, the horizontal distance is calculated by the formula:
wherein d is the horizontal distance between two waypoints, R is the radius of the earth, and alpha1、α2Respectively representing the latitude, beta, of two waypoints1、β2Respectively, the longitudes of the two waypoints.
In one embodiment, in step 4, the landing approach point is multiple, and the solving the shortest path from the return source point to the landing approach point as a return path includes: and respectively solving the shortest path from the source point to each landing approach point, and selecting the air route with the shortest path as a return air route.
In one embodiment, the method of route planning further comprises: the unmanned aerial vehicle receives an instruction of withdrawing from the autonomous return flight in real time in the autonomous return flight process, and flies according to an appointed route after withdrawing from the autonomous return flight. After the unmanned aerial vehicle flies to the landing approach point, the unmanned aerial vehicle is in a hovering and high-altitude state, and then lands to return to an airport after the unmanned aerial vehicle is in a set altitude state.
The present invention will be described in detail below with reference to fig. 2 to 4.
The unmanned aerial vehicle enters an autonomous return flight flow when receiving an autonomous return flight instruction of the ground station or judging that the system has a fault or a communication link is disconnected.
Table 1 shows waypoint information of the large fixed-wing drone according to the present embodiment; the waypoint information includes: waypoint number, longitude, latitude, altitude, eigenvalue, and horizontal distance to the next waypoint. In the embodiment, the waypoints 1-20 and 20-27 are original design routes, and if the current position of the unmanned aerial vehicle is on the design routes, the waypoint 0 is the current position of the unmanned aerial vehicle; and if the current position of the unmanned aerial vehicle is not on the designed route, the waypoint 0 is the point of the last route cut out by the unmanned aerial vehicle. Longitude, latitude, and altitude are the location information of the drone. The characteristic value represents the characteristic of a waypoint, 0 represents a normal route flight waypoint, 1 represents an unmanned aerial vehicle landing and pulling floating point, 2 represents an unmanned aerial vehicle landing approach point, and 3 represents the current position of the unmanned aerial vehicle or a point of a route cut by the unmanned aerial vehicle.
TABLE 1
And calculating the horizontal distance from the current waypoint to the next waypoint by using the formula to be used as the weight of the edge in the route undirected graph of the following waypoint.
The three-dimensional routings of drones corresponding to table 1 are shown in fig. 2. The unmanned aerial vehicle firstly takes off from a waypoint 1 of an airport runway and climbs to a waypoint 6 with the altitude of 3500m, then flies for two circles around a square airway anticlockwise, then descends to a waypoint 15 with the altitude of 3000 m, then flies for one and a half circles around the square airway anticlockwise, and finally returns to the waypoint 15 to start gliding and landing. The position ″) of the navigation mark 0 is assumed.
This airway is typical of the way that large fixed wing drones perform reconnaissance missions.
The four waypoints with the same information of longitude, latitude, altitude and characteristic value are merged and represented by the waypoint with the smaller sequence number, and the waypoint inherits the connection relation of other waypoints. Waypoints with small differences in longitude, latitude and altitude can also be combined, the threshold value can be set according to actual requirements, and only waypoints with the same longitude, latitude, altitude and characteristic value information are combined in the embodiment.
After the merging is completed, the waypoint and route topological relation is changed from a linear table structure to an undirected graph structure, as shown in fig. 3.
Then, the horizontal distance of the adjacent waypoints is taken as a weight, and the topological relation of the waypoint and the route is represented by a weighted undirected graph, as shown in FIG. 4.
And then planning a return path, and calculating the shortest path of other waypoints by using the waypoint 0 as a source point and applying a Dijkstra algorithm.
Let G ═ V, E be a waypoint route weighted undirected graph obtained as in the previous method, with V denoting waypoints. Dividing a waypoint set V in the graph into two groups, wherein the first group is a waypoint set with a shortest path solved, and is represented by S, initially, only one source point in S is provided, and then, every time a shortest path is solved, the shortest path is added into the set S until all waypoints are added into S; the second group is the rest waypoint sets without determined shortest path, which is represented by U, and the waypoints of the second group are added into S in sequence according to the increasing order of the shortest path length. In the joining process, the shortest path length from the source point v to each of the waypoints in S is always kept no greater than the shortest path length from the source point v to any of the waypoints in U. In addition, each waypoint corresponds to a distance, the distance of the waypoint in S is the shortest path length from v to the waypoint, and the distance of the waypoint in U is the current shortest path length from v to the waypoint, wherein the waypoint only comprises the waypoint in S as the middle waypoint.
(1) Initially, S only contains source points S; u contains other waypoints than s, and the distance of waypoint in U is the distance from source point s to the waypoint. For example, the distance v from the waypoint v in U is the length of (s, v), and if s and v are not adjacent, the distance v is ∞.
(2) Selecting a waypoint k with the shortest distance from the U, and adding the waypoint k into the S; at the same time, waypoint k is removed from U.
(3) And updating the distance from each waypoint in the U to the source point s. The reason why the distance of the waypoint in U is updated is that since k is determined as the waypoint for obtaining the shortest path in the previous step, the distances of other waypoints can be updated by using k. For example, the distance of (s, v) may be greater than the distance of (s, k) + (k, v).
(4) And (4) repeating the steps (2) and (3) until all the waypoints are traversed.
The embodiment applies Dijkstra algorithm to obtain the shortest path from waypoint 0 to other waypoints as shown in table 2.
TABLE 2
The distances from the waypoint 0 to the two landing approach points (waypoint 2 and waypoint 26) of the unmanned aerial vehicle are 72.46km and 47.6km respectively, and the shorter distance of the distances is selected as a return landing approach point (waypoint 26), and the return path is 0 → 16 → 15 → 23 → 24 → 25 → 26. The unmanned aerial vehicle flies to a navigation point 26 along a return route, then carries out hover height reduction, and lands to return to an airport after the unmanned aerial vehicle descends to a required height. Before the unmanned aerial vehicle enters the landing stage, the unmanned aerial vehicle can receive an 'exit self-exit return flight' instruction and reenter the normal air route flight or instruct the flight.
An embodiment of the present disclosure further provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the unmanned aerial vehicle autonomous return route planning method described above.
An electronic device according to an embodiment of the present disclosure includes a memory and a processor.
The memory is to store non-transitory computer readable instructions. In particular, the memory may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc.
The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of the disclosure, the processor is configured to execute the computer readable instructions stored in the memory.
Those skilled in the art should understand that, in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus, an interface, and the like, and these well-known structures should also be included in the protection scope of the present disclosure.
The disclosed embodiments provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the above-described unmanned aerial vehicle autonomous return route planning method.
A computer-readable storage medium according to an embodiment of the present disclosure has non-transitory computer-readable instructions stored thereon. The non-transitory computer readable instructions, when executed by a processor, perform all or a portion of the steps of the methods of the embodiments of the disclosure previously described.
The computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROMs and DVDs), magneto-optical storage media (e.g., MOs), magnetic storage media (e.g., magnetic tapes or removable disks), media with built-in rewritable non-volatile memory (e.g., memory cards), and media with built-in ROMs (e.g., ROM cartridges).
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.
Claims (10)
1. An unmanned aerial vehicle autonomous return route planning method is characterized by comprising the following steps:
step 1: when the unmanned aerial vehicle needs to autonomously return to the flight, judging whether the current position of the unmanned aerial vehicle is on a designed route, if so, adding the current position point of the unmanned aerial vehicle as a return source point into a route point list, otherwise, controlling the unmanned aerial vehicle to return to a cut-out point which is finally cut out of the designed route along an original flight route, and adding the cut-out point as a return source point into a route point list, wherein the route point list comprises route point sequences among a plurality of route points in the designed route, and space coordinates and characteristic values of each route point;
step 2: merging waypoints with the same characteristic value and spatial coordinates meeting a threshold condition in a waypoint list to form a waypoint route with a linear table structure, wherein the merged waypoint is represented by the waypoint with the smallest sequence number in the merged waypoints, and the merged waypoint inherits the connection relation of other merged waypoints;
and step 3: converting the linear table structure of the waypoint route into an undirected graph structure, taking the horizontal distance of adjacent waypoints as the weight of the edge in the undirected graph, and representing the topological relation of the waypoint route by using the weighted undirected graph;
and 4, step 4: and solving the shortest path from the return flight source point to the landing approach point from the weighted undirected graph by adopting a single-source shortest path algorithm to serve as a return flight path.
2. The unmanned aerial vehicle autonomous return route planning method according to claim 1, wherein the threshold condition is: the horizontal distance between the merged waypoints is less than or equal to 500 meters and the height difference between the merged waypoints is less than or equal to 20 meters.
3. The unmanned aerial vehicle autonomous return route planning method according to claim 1, wherein in step 3, the calculation formula of the horizontal distance is as follows:
wherein d is the horizontal distance between two waypoints, R is the radius of the earth, and alpha1、α2Respectively representing the latitude, beta, of two waypoints1、β2Respectively, the longitudes of the two waypoints.
4. The method according to claim 1, wherein in step 4, the number of the landing approach points is multiple, and the solving a shortest path from the return source point to the landing approach point as a return path includes:
and respectively solving the shortest path from the source point to each landing approach point, and selecting the air route with the shortest path as a return air route.
5. The unmanned aerial vehicle autonomous return airway planning method of claim 1, wherein the single-source shortest path algorithm comprises Dijkstra's algorithm.
6. The method for planning the autonomous return flight path of the unmanned aerial vehicle according to claim 1, wherein in step 1, the condition that the unmanned aerial vehicle needs to autonomously return flight includes:
the unmanned aerial vehicle needs to return to the home automatically after receiving an autonomous return instruction sent by the ground station; alternatively, the first and second electrodes may be,
the unmanned aerial vehicle needs to return to the home after judging that the system is in fault; alternatively, the first and second electrodes may be,
the unmanned aerial vehicle needs to return to the home independently when the ground communication link is disconnected.
7. The unmanned aerial vehicle autonomous return flight path planning method according to claim 1, wherein the method further comprises: the unmanned aerial vehicle receives an instruction of withdrawing from the autonomous return flight in real time in the autonomous return flight process, and flies according to an appointed route after withdrawing from the autonomous return flight.
8. The unmanned aerial vehicle autonomous return flight path planning method according to claim 1, wherein the method further comprises: after the unmanned aerial vehicle flies to the landing approach point, the unmanned aerial vehicle is in a hovering and high-altitude state, and then lands to return to an airport after the unmanned aerial vehicle is in a set altitude state.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the drone autonomous return route planning method of any of claims 1-8.
10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of unmanned aerial vehicle autonomous return route planning of any of claims 1-8.
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| CN104881039A (en) * | 2015-05-12 | 2015-09-02 | 零度智控（北京）智能科技有限公司 | Method and system for returning of unmanned plane |
| CN108496136A (en) * | 2017-05-24 | 2018-09-04 | 深圳市大疆创新科技有限公司 | Control method of making a return voyage, equipment and the unmanned vehicle of unmanned vehicle |
| CN109407704A (en) * | 2018-12-13 | 2019-03-01 | 佛山单常科技有限公司 | A kind of intelligent unmanned plane makes a return voyage control system |
| CN110262552A (en) * | 2019-06-26 | 2019-09-20 | 南京拓兴智控科技有限公司 | Flight control method, device, equipment and the storage medium of unmanned plane |
| CN111665868A (en) * | 2020-07-21 | 2020-09-15 | 陕西欧卡电子智能科技有限公司 | Unmanned ship return method, device, equipment and storage medium based on virtual channel |
| CN112327924A (en) * | 2020-11-20 | 2021-02-05 | 中国直升机设计研究所 | Link failure emergency disposal method for flight of unmanned helicopter airline |
| CN112666971A (en) * | 2020-12-15 | 2021-04-16 | 广州极飞科技有限公司 | Unmanned aerial vehicle return method and device, unmanned aerial vehicle and storage medium |
| CN113487915A (en) * | 2021-09-06 | 2021-10-08 | 江苏启航航空科技有限公司 | Unmanned aerial vehicle-based flight service supervision system and method |
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