CN117806342A - Online path planning method, medium and device for fixed wing unmanned aerial vehicle based on simulation - Google Patents
Online path planning method, medium and device for fixed wing unmanned aerial vehicle based on simulation Download PDFInfo
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Abstract
The invention relates to the technical field of unmanned aerial vehicle path planning, and provides a fixed wing unmanned aerial vehicle on-line path planning method, medium and device based on simulation, wherein the method comprises the following steps: s1, acquiring a new no-fly zone, fitting the new no-fly zone into a circle, and generating a new existing basic route; s2, obtaining a re-planning air section of a new existing basic air path, which is fitted with a circle through a new no-fly zone; s3, re-planning the route in each re-planning section; s4, generating a new route and correcting through splicing and optimizing; and S5, judging whether the online path planning task of the fixed wing unmanned aerial vehicle is completed, and if not, returning to the step S3. The invention has reasonable and efficient design, can effectively utilize the inherent characteristics of the fixed-wing unmanned aerial vehicle, and solves the problem that the fixed-wing unmanned aerial vehicle needs to complete secondary route re-planning on line within the limited time of the airborne terminal when the environment changes such as the generation of a new no-fly zone.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to a fixed wing unmanned aerial vehicle on-line path planning method, medium and device based on simulation.
Background
The unmanned aerial vehicle has the characteristics of flexible action, convenience, quickness and the like, and is widely applied to the civil and military fields. In the process of executing tasks, path planning is an important link, and the unmanned aerial vehicle needs to fly from a starting point to an end point along a certain path on the premise of avoiding obstacles.
The traditional fixed wing unmanned aerial vehicle path planning method mainly depends on preset waypoints and route planning algorithms. These methods often fail to accommodate complex and diverse flight environments and have limited processing power for dynamic obstacles. In addition, the traditional method usually ignores the dynamic performance and the maneuverability of the unmanned aerial vehicle, and cannot flexibly plan paths according to different task demands.
To overcome these challenges, new path planning techniques, such as genetic algorithm, artificial neural network, and reinforcement learning based methods, have emerged in recent years. These techniques take advantage of big data analysis and machine learning to better address path planning problems in complex environments. However, these methods still have some limitations in practical applications, including high computational complexity, large training data requirements, and poor real-time performance.
Therefore, a new fixed-wing unmanned aerial vehicle path planning method needs to be proposed to overcome the limitations of the conventional method and the prior art. The method can realize efficient, safe and flexible path planning in complex and changeable environments, and simultaneously considers the dynamic performance and the maneuverability of the unmanned aerial vehicle. The method also has real-time performance, can adapt to different task demands, and can adaptively process dynamic obstacles and environmental changes.
When the fixed wing unmanned aerial vehicle deployed in the actual scene executes specific tasks such as rescue after earthquake disaster, the influence of dynamic obstacles and environmental changes on the route and the course is required to be processed in an online self-adaptive manner within the time limited by the airborne end. When the environment of the fixed wing unmanned aerial vehicle changes, the generation of a new route in real time to avoid the obstacle is an important factor to consider, and the prior method does not give an appropriate response to the factor.
Disclosure of Invention
The invention aims to provide an online path planning method of a fixed-wing unmanned aerial vehicle based on a simulation, which aims to solve the problem that the fixed-wing unmanned aerial vehicle needs to complete secondary route re-planning online within the limit time of an airborne terminal when a new no-fly zone generates and other environmental changes.
The invention provides a fixed wing unmanned aerial vehicle on-line path planning method based on a simulation, which comprises the following steps:
s1, acquiring a new no-fly zone, fitting the new no-fly zone into a circle, and generating a new existing basic route;
s2, obtaining a re-planning air section of a new existing basic air path, which is fitted with a circle through a new no-fly zone;
s3, re-planning the route in each re-planning section;
s4, generating a new route and correcting through splicing and optimizing;
and S5, judging whether the online path planning task of the fixed wing unmanned aerial vehicle is completed, and if not, returning to the step S3.
Further, in step S1, obtaining a new no-fly zone and fitting the new no-fly zone to a circle, and generating a new existing basic route includes:
acquiring a new no-fly zone;
fitting the minimum circumscribing circle of the new no-fly zone to obtain the center point and the radius of the minimum circumscribing circle;
acquiring the waypoints of the existing basic navigation path, and taking the current position of the fixed-wing unmanned aerial vehicle as one waypoint;
and deleting the waypoints in the minimum circumcircle of the new no-fly zone in the existing basic route according to the minimum circumcircle of the new no-fly zone, and forming the rest waypoints into the new existing basic route.
Further, in step S2, obtaining a re-planned leg of the new existing basic route, which is fitted with a circle through the new no-fly zone, includes:
taking a route between every two waypoints in a new existing basic route as a route segment;
sequentially checking whether each navigation segment passes through the minimum circumcircle of the new no-fly zone;
taking the minimum circumcircle leg passing through the new no-fly zone as a re-planning leg.
Further, in step S3, in each re-planning leg, the re-planning route includes:
in each re-planning leg, obtaining the chord length X of the minimum circumcircle of the re-planning leg passing through the new no-fly zone, and 2 waypoints of the minimum circumcircle Y=X/2 of the distance between the re-planning leg and the new no-fly zone;
and taking the 2 waypoints as a starting point and an ending point, and generating a way avoiding a new no-fly zone by applying an improved rapid random tree algorithm RRT based on the flight performance of the fixed-wing unmanned aerial vehicle.
Further, in step S4, generating a new route and correcting the new route by splicing and optimizing includes:
optimizing the route generated in the step S3: in the navigation path generated in the step S3, starting from the starting point, sequentially comparing whether the navigation points and the following navigation points pass through the minimum circumcircle of the new no-fly zone, if the minimum circumcircle of the new no-fly zone is not passed, deleting the middle navigation points until the optimization is completed, wherein the linear distance between the navigation points is smaller than the distance of the re-planning front navigation section and meets the turning radius requirement of the fixed-wing unmanned plane;
forming a new route from the route generated in the step S3 after optimization and a route Duan Pinjie which is not required to be re-planned in the new existing basic route;
correcting whether each waypoint in the new route meets the requirements or not, and correcting whether each leg in the new route meets the requirements or not.
Further, in step S4, correcting whether each waypoint in the new route meets the requirements includes:
correcting whether each waypoint in the new route is in the no-fly zone;
and whether each waypoint in the new way meets the waypoint flying height requirements.
Further, in step S4, correcting whether each leg in the new route meets the requirements includes:
correcting whether each leg in the new route meets the turning radius requirement of the fixed wing unmanned aerial vehicle;
and correcting whether each leg in the new route passes through the no-fly zone.
Further, in step S5, determining whether the online path planning task of the fixed wing unmanned aerial vehicle is completed includes:
judging whether the on-line path planning task of the fixed wing unmanned aerial vehicle is completed or not based on the correction result of the new route in the step S4; if the correction result does not meet the requirement, continuing to return to the step S3, otherwise ending the flow of the fixed wing unmanned aerial vehicle on-line path planning.
The invention also provides a computer terminal storage medium which stores computer terminal executable instructions for executing the online path planning method of the fixed wing unmanned aerial vehicle based on the simulation.
The present invention also provides a computing device comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the method for planning the online path of the fixed wing unmanned aerial vehicle based on the simulation.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
the invention has reasonable and efficient design, can effectively utilize the inherent characteristics of the fixed-wing unmanned aerial vehicle, and solves the problem that the fixed-wing unmanned aerial vehicle needs to complete secondary route re-planning on line within the limited time of the airborne terminal when the environment changes such as the generation of a new no-fly zone.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly describe the drawings in the embodiments, it being understood that the following drawings only illustrate some embodiments of the present invention and should not be considered as limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for planning an online path of a fixed wing unmanned aerial vehicle based on a simulation in an embodiment of the present invention.
FIG. 2 is a schematic diagram of a new off-air zone fitted to a circle to create a new existing basic course in an embodiment of the invention.
FIG. 3 is a schematic diagram of a minimum circumcircle for a new existing primary route to pass through a new no-fly zone in accordance with an embodiment of the present invention.
FIG. 4 is a schematic diagram of determining an initial and final point of an improved fast random tree algorithm in a re-planning leg according to an embodiment of the present invention.
FIG. 5 is a schematic diagram of generating a new route based on a modified fast random tree algorithm in an embodiment of the present invention.
FIG. 6 is a schematic diagram of optimizing a re-planned leg in an embodiment of the present invention.
FIG. 7 is a schematic diagram of a final generated route in an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples
As shown in fig. 1, this embodiment proposes a method for planning an online path of a fixed wing unmanned aerial vehicle based on a simulation, including the following steps:
s1, as shown in FIG. 2, acquiring a new no-fly zone and fitting the new no-fly zone into a circle to generate a new existing basic route:
acquiring a new no-fly zone;
fitting the minimum circumscribing circle of the new no-fly zone to obtain the center point and the radius of the minimum circumscribing circle;
acquiring the waypoints of the existing basic navigation path, and taking the current position of the fixed-wing unmanned aerial vehicle as one waypoint;
and deleting the waypoints in the minimum circumcircle of the new no-fly zone in the existing basic route according to the minimum circumcircle of the new no-fly zone, and forming the rest waypoints into the new existing basic route.
S2, as shown in FIG. 3, obtaining a re-planning leg fitting a circle through a new no-fly zone in a new existing basic course:
taking a route between every two waypoints in a new existing basic route as a route segment;
sequentially checking whether each navigation segment passes through the minimum circumcircle of the new no-fly zone;
taking the minimum circumcircle leg passing through the new no-fly zone as a re-planning leg.
S3, as shown in FIG. 4, within each re-planning air range, re-planning the air route:
in each re-planning leg, obtaining the chord length X of the minimum circumcircle of the re-planning leg passing through the new no-fly zone, and 2 waypoints of the minimum circumcircle Y=X/2 of the distance between the re-planning leg and the new no-fly zone;
taking the 2 waypoints as a starting point and an ending point, as shown in fig. 5, generating a route avoiding a new no-fly zone by using an improved rapid random tree algorithm RRT based on the flight performance of the fixed-wing unmanned aerial vehicle. The improved fast random tree algorithm RRT is the prior art and will not be described herein.
S4, generating a new route and correcting by splicing and optimizing:
optimizing the route generated in the step S3: in the navigation path generated in the step S3, starting from the starting point, sequentially comparing whether the navigation points and the following navigation points pass through the minimum circumcircle of the new no-fly zone, if the minimum circumcircle of the new no-fly zone is not passed, deleting the middle navigation points until the optimization is completed, wherein the linear distance between the navigation points is smaller than the distance of the re-planning front navigation section and meets the turning radius requirement of the fixed-wing unmanned plane; as shown in fig. 6, as the distance between the waypoint 6 and the waypoint 10 is re-planned, the waypoint 6, the waypoint 8, the waypoint 9 and the waypoint 10 pass through the no-fly zone and are not optimized; the waypoint 7, the waypoint 9 and the waypoint 10 pass through the no-fly zone and are not optimized; the distance from the waypoint 8 to the waypoint 9 to the waypoint 10 is smaller than the linear distance from the waypoint 8 to the waypoint 9, and the single-machine performance requirements such as turning radius are met, so that the waypoint 9 is optimized, namely the waypoint 8 is deleted, and the waypoint 8 is directly connected with the waypoint 10.
Forming a new route from the route generated in the optimized step S3 and a route Duan Pinjie which is not required to be re-planned in the new existing basic route, as shown in fig. 7;
correcting whether each waypoint in the new route meets the requirements: correcting whether each waypoint in the new route is in the no-fly zone; and whether each waypoint in the new way meets the stand-alone performance requirements such as the flying height of the waypoint.
Correcting whether each leg in the new route meets the requirements: correcting whether each aerosegment in the new air route meets the single-machine performance requirement of the turning radius of the fixed-wing unmanned aerial vehicle; and correcting whether each leg in the new route passes through the no-fly zone.
S5, judging whether the online path planning task of the fixed wing unmanned aerial vehicle is completed or not: judging whether the on-line path planning task of the fixed wing unmanned aerial vehicle is completed or not based on the correction result of the new route in the step S4; if the correction result does not meet the requirement, continuing to return to the step S3, otherwise ending the flow of the fixed wing unmanned aerial vehicle on-line path planning.
Furthermore, in some embodiments, a computer terminal storage medium is provided, in which computer terminal executable instructions are stored, where the computer terminal executable instructions are configured to perform the method for planning an online path of a fixed wing unmanned aerial vehicle based on a simulation as described in the foregoing embodiments. Examples of the computer storage medium include magnetic storage media (e.g., floppy disks, hard disks, etc.), optical recording media (e.g., CD-ROMs, DVDs, etc.), or memories such as memory cards, ROMs, or RAMs, etc. The computer storage media may also be distributed over network-connected computer systems, such as stores for application programs.
Furthermore, in some embodiments, a computing device is presented comprising: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method for online path planning for a fixed wing unmanned aerial vehicle based on a shape as described in the previous embodiments. Examples of computing devices include PCs, tablets, smartphones, PDAs, etc.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The online path planning method of the fixed wing unmanned aerial vehicle based on the simulation is characterized by comprising the following steps of:
s1, acquiring a new no-fly zone, fitting the new no-fly zone into a circle, and generating a new existing basic route;
s2, obtaining a re-planning air section of a new existing basic air path, which is fitted with a circle through a new no-fly zone;
s3, re-planning the route in each re-planning section;
s4, generating a new route and correcting through splicing and optimizing;
and S5, judging whether the online path planning task of the fixed wing unmanned aerial vehicle is completed, and if not, returning to the step S3.
2. The method for planning an online path of a fixed wing unmanned aerial vehicle based on a simulation of claim 1, wherein in step S1, obtaining a new no-fly zone and fitting the new no-fly zone to a circle, generating a new existing basic route comprises:
acquiring a new no-fly zone;
fitting the minimum circumscribing circle of the new no-fly zone to obtain the center point and the radius of the minimum circumscribing circle;
acquiring the waypoints of the existing basic navigation path, and taking the current position of the fixed-wing unmanned aerial vehicle as one waypoint;
and deleting the waypoints in the minimum circumcircle of the new no-fly zone in the existing basic route according to the minimum circumcircle of the new no-fly zone, and forming the rest waypoints into the new existing basic route.
3. The method for planning an online path of a fixed wing unmanned aerial vehicle based on a simulation of claim 2, wherein in step S2, obtaining a re-planned leg of a new existing basic route fitted with a circle through a new no-fly zone comprises:
taking a route between every two waypoints in a new existing basic route as a route segment;
sequentially checking whether each navigation segment passes through the minimum circumcircle of the new no-fly zone;
taking the minimum circumcircle leg passing through the new no-fly zone as a re-planning leg.
4. A method for planning an online path of a fixed wing unmanned aerial vehicle based on a simulation of claim 3, wherein in step S3, in each re-planned leg, re-planning the route comprises:
in each re-planning leg, obtaining the chord length X of the minimum circumcircle of the re-planning leg passing through the new no-fly zone, and 2 waypoints of the minimum circumcircle Y=X/2 of the distance between the re-planning leg and the new no-fly zone;
and taking the 2 waypoints as a starting point and an ending point, and generating a way avoiding a new no-fly zone by applying an improved rapid random tree algorithm RRT based on the flight performance of the fixed-wing unmanned aerial vehicle.
5. The method for planning an online path of a fixed wing unmanned aerial vehicle based on a simulation of claim 4, wherein generating a new route and correcting the new route by splicing and optimizing in step S4 comprises:
optimizing the route generated in the step S3: in the navigation path generated in the step S3, starting from the starting point, sequentially comparing whether the navigation points and the following navigation points pass through the minimum circumcircle of the new no-fly zone, if the minimum circumcircle of the new no-fly zone is not passed, deleting the middle navigation points until the optimization is completed, wherein the linear distance between the navigation points is smaller than the distance of the re-planning front navigation section and meets the turning radius requirement of the fixed-wing unmanned plane;
forming a new route from the route generated in the step S3 after optimization and a route Duan Pinjie which is not required to be re-planned in the new existing basic route;
correcting whether each waypoint in the new route meets the requirements or not, and correcting whether each leg in the new route meets the requirements or not.
6. The method for planning an online path of a fixed wing unmanned aerial vehicle based on a simulation of claim 5, wherein in step S4, correcting whether each waypoint in the new route meets the requirements comprises:
correcting whether each waypoint in the new route is in the no-fly zone;
and whether each waypoint in the new way meets the waypoint flying height requirements.
7. The method for planning an online path of a fixed wing unmanned aerial vehicle based on a simulation of claim 1, wherein in step S4, correcting whether each leg in the new route meets the requirements comprises:
correcting whether each leg in the new route meets the turning radius requirement of the fixed wing unmanned aerial vehicle;
and correcting whether each leg in the new route passes through the no-fly zone.
8. The online path planning method of a fixed-wing unmanned aerial vehicle based on the simulation of claim 1, wherein in step S5, determining whether the online path planning task of the fixed-wing unmanned aerial vehicle is completed comprises:
judging whether the on-line path planning task of the fixed wing unmanned aerial vehicle is completed or not based on the correction result of the new route in the step S4; if the correction result does not meet the requirement, continuing to return to the step S3, otherwise ending the flow of the fixed wing unmanned aerial vehicle on-line path planning.
9. A computer terminal storage medium storing computer terminal executable instructions for performing the method for planning an online path of a fixed wing unmanned aerial vehicle according to any of claims 1 to 8.
10. A computing device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of on-line path planning for a fixed wing unmanned aerial vehicle based on a simulation as claimed in any one of claims 1 to 8.
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