CN110823223A - Path planning method and device for unmanned aerial vehicle cluster - Google Patents
Path planning method and device for unmanned aerial vehicle cluster Download PDFInfo
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
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Abstract
The invention discloses a path planning method of an unmanned aerial vehicle cluster based on a BebopDalne system architecture, which comprises the following steps: step 1: constructing a two-dimensional map model of an unmanned aerial vehicle cluster, creating an area, identifying a movable unmanned aerial vehicle in the area, and assuming and setting the unmanned aerial vehicle as a blank area in the two-dimensional map model; step 2: marking obstacle models existing in the space of the two-dimensional map model, obtaining a 3D slope map through the two-dimensional map model and the obstacle models, and 3: using the obtained 3D slope map and assuming that the target area is the lowest altitude point, the route planning points from the starting point and searches for the previous neighboring point of each iteration with a lower height, wherein the obstacle model is higher than the other neighboring points and will not be selected during the route planning, and the drone automatically reaches the target area of the lowest point according to the route planning.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a path planning method and device for an unmanned aerial vehicle cluster.
Background
The unmanned aerial vehicle is widely applied to the fields of military, civil use and the like due to the characteristics of low cost, high maneuverability, flexible deployment, zero casualties and the like. When a single unmanned aerial vehicle executes tasks such as military monitoring and reconnaissance, large-scale disaster scene search and rescue and the like, the tasks fail due to the fact that adverse factors such as wide task areas, complex and changeable environments, limited sensor sensing capability, single-node faults and the like are faced, and therefore the conversion from a traditional research and application mode taking an unmanned aerial vehicle platform as a center to a cooperative mode of a plurality of unmanned aerial vehicles taking a network as a center is promoted. The task execution capacity of the unmanned aerial vehicle cluster is expanded by integrating the environment sensing capacity of a plurality of unmanned aerial vehicles and sharing the calculation processing capacity of the unmanned aerial vehicles, and the probability of successful task execution is improved.
In the future, under complex and variable information battlefield environments, a single unmanned aerial vehicle can hardly complete tasks, and under many conditions, the tasks can be completed only by multiple unmanned aerial vehicles which cooperatively control the flight; each drone requires a 1 to 3 crew member to assign, negotiate and coordinate many human warriors. In addition to the cost of human operators, this approach encounters an unsolved challenge, how to achieve synergy. Under the restriction of the current science and technology, it is quite difficult for an unmanned plane to reach the powerful information processing capacity and intelligence of a pilot, and if the unmanned plane which is absolutely dominant in quantity can reach or even surpass the manned plane which is inferior in quantity by utilizing the clustering intelligence by simulating the clustering phenomenon of the living things in the nature. The evolution characteristics and behavior rules of the biological system are analyzed, certain principles and behaviors of biological population intelligence are combined with the multi-unmanned aerial vehicle cooperative control theory, and the method has a wide engineering application prospect. At present, researches on cooperative flight and track planning of unmanned aerial vehicles have achieved certain research results at home and abroad, but a unified theory and an effective method are not available.
With the gradual maturity of unmanned platform technology in recent years, the research on the target tracking problem is not limited to the research on the tracking theory, but the research is focused on the fusion and control of cooperative tracking of multiple unmanned aerial vehicles, for example, the target tracking is performed by adopting multiple sensors, multi-machine interaction, external auxiliary positioning equipment and the like. The most representative low-cost autonomous attack system developed by the American and air force is mainly used for a plurality of small suicide unmanned aerial vehicles to execute wide-area target tracking and autonomous attack tasks. Most of the existing related researches are developed aiming at a theoretical framework and a target tracking algorithm of cooperative control of a plurality of unmanned aerial vehicles, and practical and feasible technical schemes such as searching and tracking of cooperative targets of a plurality of unmanned aerial vehicles and cooperative landing do not exist aiming at the specific problems of cooperation of a plurality of unmanned aerial vehicles. In the prior art, information acquired by a plurality of unmanned aerial vehicle sensors is gathered and processed by a ground station platform, and the method is not suitable for autonomous cooperation of the plurality of unmanned aerial vehicles under complex conditions.
Disclosure of Invention
The present invention is directed to at least solving the problems of the prior art. Therefore, the invention discloses a path planning method of an unmanned aerial vehicle cluster based on a BebopDRone system architecture, which comprises the following steps:
step 1: constructing a two-dimensional map model of an unmanned aerial vehicle cluster, creating an area, identifying a movable unmanned aerial vehicle in the area, and assuming and setting the unmanned aerial vehicle as a blank area in the two-dimensional map model;
step 2: marking obstacle models existing in the space of the two-dimensional map model, and obtaining a 3D slope map through the two-dimensional map model and the obstacle models,
wherein h isrampRepresenting the height of an object, said object being any point in the map, hobstacleHeight of the obstacle, XtargetIs the x-coordinate, Y, of the objecttargetFor the y-coordinate of the target, a complete 3D ramp map is obtained by equations (1) and (2) above:
h(X,Y)＝hramp(X，Y)+hobstncle(X，Y) (3)
wherein h is the altitude value;
and step 3: using the obtained 3D slope map and assuming that the target area is the lowest altitude point, the route planning points from the starting point and searches for the previous neighboring point of each iteration with a lower height, wherein the obstacle model is higher than the other neighboring points and will not be selected during the route planning, and the drone automatically reaches the target area of the lowest point according to the route planning.
Furthermore, during path planning, if the path planning judges that the unmanned aerial vehicle enters the circulation, the point on the height of the unmanned aerial vehicle is increased and the unmanned aerial vehicle automatically moves to the other direction.
Furthermore, any unmanned aerial vehicle in the unmanned aerial vehicle cluster detects a target through the airborne sensor, a wireless communication network of the unmanned aerial vehicle cluster is established, if the unmanned aerial vehicle cluster detects the target, detection data of any unmanned aerial vehicle is processed, and meanwhile processing information is broadcasted to all unmanned aerial vehicles in the unmanned aerial vehicle cluster.
Furthermore, whether an obstacle model suddenly appears exists on a path of a next adjacent point acquired by the unmanned aerial vehicle through the airborne sensor, a new direction position is firstly temporarily set for possible collision, and whether the new moving direction realizes flight collision avoidance is judged.
Furthermore, a safety threshold value of potential field resultant force of unmanned aerial vehicle cooperation is preset according to the attribute value of the unmanned aerial vehicle, a piloting unmanned aerial vehicle is set, other unmanned aerial vehicles track the position of the piloting unmanned aerial vehicle in real time, and the relative distance and the relative angle between each unmanned aerial vehicle and the piloting unmanned aerial vehicle are calculated; and calculating potential field resultant force among the unmanned aerial vehicle groups according to the relative distance and the relative angle, if the calculated potential field resultant force is smaller than a safety threshold, performing a low-control flight state on the unmanned aerial vehicle groups, if the calculated potential field resultant force is larger than the safety threshold, temporarily abandoning the task of flying to a destination and performing steering flight for reducing the potential field resultant force, and after flying for a preset time, performing potential field resultant force calculation among the unmanned aerial vehicle groups again.
The invention further provides a path planning device of the unmanned aerial vehicle cluster based on the BebopDalne system architecture, which comprises an unmanned aerial vehicle cluster formed by a plurality of unmanned aerial vehicles of the BebopDalne system architecture, and the path planning device comprises:
the space modeling unit is used for constructing a two-dimensional map model of the unmanned aerial vehicle cluster, creating an area, identifying a movable unmanned aerial vehicle in the area, and assuming and setting the unmanned aerial vehicle as a blank area in the two-dimensional map model;
a fault modeling unit marking obstacle models existing in a space of the two-dimensional map model and obtaining a 3D slope map through the two-dimensional map model and the obstacle models,
wherein h isrampRepresenting the height of an object, said object being any point in the map, hobstacleHeight of the obstacle, XtargetIs the x coordinate of the target, targetFor the y-coordinate of the target, a complete 3D ramp map is obtained by equations (1) and (2) above:
h(X，Y)＝hramp(X，Y)+hobstacle(X，Y) (3)
wherein h is the altitude value;
and the path planning unit adopts the obtained 3D slope map and assumes that the target place is the lowest altitude point, the path planning points to and searches the previous adjacent point of each iteration with lower height from the starting point, wherein the barrier model is higher than other adjacent points and cannot be selected during the path planning, and the unmanned aerial vehicle automatically reaches the target place with the lowest point according to the path planning.
Furthermore, the cyclic check unit is used for increasing one point of the height of the unmanned aerial vehicle and automatically moving the unmanned aerial vehicle to the other direction when the path planning judges that the unmanned aerial vehicle enters the cycle.
Furthermore, the unmanned aerial vehicle cluster communication unit is used for detecting a target through an onboard sensor by any unmanned aerial vehicle in the unmanned aerial vehicle cluster, establishing a wireless communication network of the unmanned aerial vehicle cluster, processing detection data of any unmanned aerial vehicle if the unmanned aerial vehicle cluster detects the target, and broadcasting processing information to all unmanned aerial vehicles in the unmanned aerial vehicle cluster.
Furthermore, the obstacle judgment auxiliary unit is used for acquiring whether an obstacle model suddenly appears on a path of a next adjacent point from the unmanned aerial vehicle through the airborne sensor, temporarily setting a new direction position for possible collision, and judging whether a new moving direction realizes flight collision avoidance.
Furthermore, the unmanned aerial vehicle safety calculation unit is used for presetting a safety threshold value of potential field resultant force of unmanned aerial vehicle cooperation according to the attribute numerical value of the unmanned aerial vehicle, setting a piloting unmanned aerial vehicle, tracking the position of the piloting unmanned aerial vehicle in real time by other unmanned aerial vehicles, and calculating the relative distance and the relative angle between each unmanned aerial vehicle and the piloting unmanned aerial vehicle; and calculating potential field resultant force among the unmanned aerial vehicle groups according to the relative distance and the relative angle, if the calculated potential field resultant force is smaller than a safety threshold, performing a low-control flight state on the unmanned aerial vehicle groups, if the calculated potential field resultant force is larger than the safety threshold, temporarily abandoning the task of flying to a destination and performing steering flight for reducing the potential field resultant force, and after flying for a preset time, performing potential field resultant force calculation among the unmanned aerial vehicle groups again.
Compared with the prior art, the invention has the advantages that: the unmanned aerial vehicle structure of the prior art has the advantages of simple structure, high maneuverability, flexibility in taking off and landing and the like, and the quad-rotor unmanned aerial vehicle is widely applied to military and civil fields. However, the design of the bottom-layer flight controller of the quad-rotor unmanned aerial vehicle is a huge challenge due to the characteristics of nonlinearity, under-actuation, strong coupling, sensitivity to internal and external disturbance and the like. Aiming at the problems, the unmanned aerial vehicle designed by the invention mainly adopts a theoretical modeling method from a flight control and track generation algorithm of a four-rotor unmanned aerial vehicle, establishes models of the four-rotor unmanned aerial vehicle and a low-altitude wind field, completes BebopDRone model parameterization, and realizes the collaborative path planning of a plurality of unmanned aerial vehicles by a lower computer separation operation method.
Drawings
Fig. 1 is a flowchart of a path planning method of an unmanned aerial vehicle cluster of the present invention.
Detailed Description
Example one
The embodiment provides a path planning method for an unmanned aerial vehicle cluster based on a bebopdron system architecture, as shown in fig. 1, the method includes:
step 1: constructing a two-dimensional map model of an unmanned aerial vehicle cluster, creating an area, identifying a movable unmanned aerial vehicle in the area, and assuming and setting the unmanned aerial vehicle as a blank area in the two-dimensional map model;
step 2: marking obstacle models existing in the space of the two-dimensional map model, and obtaining a 3D slope map through the two-dimensional map model and the obstacle models,
wherein h isrampRepresenting the height of an object, said object being any point in the map, hobstacleHeight of the obstacle, XtargetIs the x-coordinate, Y, of the objecttargetFor the y-coordinate of the target, a complete 3D ramp map is obtained by equations (1) and (2) above:
h(X,Y)＝hramp(X，Y)+hobstacle(X，Y) (3)
wherein h is the altitude value;
and step 3: using the obtained 3D slope map and assuming that the target area is the lowest altitude point, the route planning points from the starting point and searches for the previous neighboring point of each iteration with a lower height, wherein the obstacle model is higher than the other neighboring points and will not be selected during the route planning, and the drone automatically reaches the target area of the lowest point according to the route planning.
Furthermore, during path planning, if the path planning judges that the unmanned aerial vehicle enters the circulation, the point on the height of the unmanned aerial vehicle is increased and the unmanned aerial vehicle automatically moves to the other direction.
Furthermore, any unmanned aerial vehicle in the unmanned aerial vehicle cluster detects a target through the airborne sensor, a wireless communication network of the unmanned aerial vehicle cluster is established, if the unmanned aerial vehicle cluster detects the target, detection data of any unmanned aerial vehicle is processed, and meanwhile processing information is broadcasted to all unmanned aerial vehicles in the unmanned aerial vehicle cluster.
Furthermore, whether an obstacle model suddenly appears exists on a path of a next adjacent point acquired by the unmanned aerial vehicle through the airborne sensor, a new direction position is firstly temporarily set for possible collision, and whether the new moving direction realizes flight collision avoidance is judged.
Furthermore, a safety threshold value of potential field resultant force of unmanned aerial vehicle cooperation is preset according to the attribute value of the unmanned aerial vehicle, a piloting unmanned aerial vehicle is set, other unmanned aerial vehicles track the position of the piloting unmanned aerial vehicle in real time, and the relative distance and the relative angle between each unmanned aerial vehicle and the piloting unmanned aerial vehicle are calculated; and calculating potential field resultant force among the unmanned aerial vehicle groups according to the relative distance and the relative angle, if the calculated potential field resultant force is smaller than a safety threshold, performing a low-control flight state on the unmanned aerial vehicle groups, if the calculated potential field resultant force is larger than the safety threshold, temporarily abandoning the task of flying to a destination and performing steering flight for reducing the potential field resultant force, and after flying for a preset time, performing potential field resultant force calculation among the unmanned aerial vehicle groups again.
Example two
This embodiment further provides a path planning device based on unmanned aerial vehicle crowd of bebopdron system architecture, including the unmanned aerial vehicle cluster that unmanned aerial vehicle of a plurality of bebopdron system architectures constitutes, the device includes:
and the space modeling unit is used for constructing a two-dimensional map model of the unmanned aerial vehicle cluster, creating an area, identifying the moving unmanned aerial vehicles in the area, and assuming and setting the airplanes as blank areas in the two-dimensional map model because most unmanned aerial vehicles are at constant flight height.
A fault modelling unit is also included, marking obstacle models that exist in the space of the two-dimensional map model, which must be marked first so that the drone can avoid collisions. An obstacle is considered a continuous obstacle if it is a large object, such as a building.
Marking obstacle models existing in the space of the two-dimensional map model, and respectively obtaining the map model and the obstacle models through the following formula:
wherein h isrampRepresenting the height of an object, said object being any point in the map, hobstacleHeight of the obstacle, XtargetIs the x-coordinate, Y, of the objecttargetFor the y-coordinate of the target, a complete 3D ramp map can be obtained by equations (1) and (2):
h(X,Y)＝hramp(X，Y)+hobstacle(X，Y) (3)
the device further comprises a path planning unit which adopts the obtained 3D slope map and assumes that the target place is the lowest altitude point, the route planning unit points to and searches the previous adjacent point of each iteration with lower height from the starting point, wherein the barrier model is higher than other adjacent points and cannot be selected during the path planning, and finally, the unmanned aerial vehicle automatically reaches the target place with the lowest point according to the path planning.
Assuming that the map is a two-dimensional matrix, map information is created, the start coordinates and the end point are input into the map model, and the coordinates of the obstacle are also input into the model. By importing the path model, the unmanned aerial vehicle searches for the lowest point position by using the center of the unmanned aerial vehicle. The route planning is iterated starting from a starting point and searching for neighboring points having a lower height than the previous point. Finally, the drone will automatically reach the lowest point to find the lowest altitude point and target. However, if the direction of movement is perpendicular to the obstacle, the drone will jam. When the drone is stuck, it can be added a little bit in the height of the drone and moved automatically to the other direction. This prevents the drone from re-entering the trap area. By using this mechanism, it is possible to avoid that the drone enters this area if the path is planned again. Thus, this approach may reduce search iterations and find the best path.
And obtaining a 3D slope map through a two-dimensional map model and an obstacle model, setting the path planning of the unmanned aerial vehicle to start from a starting point, searching for adjacent points with heights lower than the former point for iteration, automatically reaching the lowest point and finding a target. Furthermore, autonomous movement mechanisms are applied to prevent the drone from re-entering the trap area. In practice, it can be applied to the optimal route planning of unmanned aerial vehicles.
The device further comprises a cyclic check unit which is used for increasing one point on the height of the unmanned aerial vehicle and automatically moving the unmanned aerial vehicle to the other direction when the path planning is carried out and the path planning is judged to be circulating.
The device further comprises an unmanned aerial vehicle group communication unit, any unmanned aerial vehicle in the unmanned aerial vehicle group detects a target through an onboard sensor through the unmanned aerial vehicle group communication unit, a wireless communication network of the unmanned aerial vehicle group is established, if the unmanned aerial vehicle group detects a target place, detection data of any unmanned aerial vehicle are processed, and processing information is broadcasted to all unmanned aerial vehicles in the unmanned aerial vehicle group.
The device further comprises an obstacle judgment auxiliary unit which is used for acquiring whether an obstacle model suddenly appears on a path of a next adjacent point where the unmanned aerial vehicle arrives through the airborne sensor, temporarily setting a new direction and position for possible collision, and judging whether a new moving direction realizes flight collision avoidance.
The device further comprises an unmanned aerial vehicle safety calculation unit, wherein the unmanned aerial vehicle safety calculation unit is used for presetting a safety threshold value of potential field resultant force of unmanned aerial vehicle cooperation according to the attribute value of the unmanned aerial vehicle, setting a piloting unmanned aerial vehicle, tracking the position of the piloting unmanned aerial vehicle in real time by other unmanned aerial vehicles and calculating the relative distance and the relative angle between each unmanned aerial vehicle and the piloting unmanned aerial vehicle; calculating potential field resultant force among the unmanned aerial vehicle groups according to the relative distance and the relative angle, and if the calculated potential field resultant force is smaller than a safety threshold, performing low-control flight state on the unmanned aerial vehicle groups; and if the calculated potential field resultant force is larger than a safety threshold value, temporarily abandoning the task of flying to the destination and performing steering flight for reducing the potential field resultant force, and after flying for a preset time, calculating the potential field resultant force among the unmanned aerial vehicle groups again.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Although the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications may be made without departing from the scope of the invention. It is therefore intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is the following claims, including all equivalents, that are intended to define the spirit and scope of this invention. The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (10)
1. A path planning method for an unmanned aerial vehicle cluster based on a BebopDale system architecture is characterized by comprising the following steps:
step 1: constructing a two-dimensional map model of an unmanned aerial vehicle cluster, creating an area, identifying a movable unmanned aerial vehicle in the area, and assuming and setting the unmanned aerial vehicle as a blank area in the two-dimensional map model;
step 2: marking obstacle models existing in the space of the two-dimensional map model, and obtaining a 3D slope map through the two-dimensional map model and the obstacle models,
wherein h isrampRepresenting the height of an object, said object being any point in the map, hobstacleHeight of the obstacle, XtargetIs the x-coordinate, Y, of the objecttargetFor the y-coordinate of the target, a complete 3D ramp map is obtained by equations (1) and (2) above:
h(X，Y)＝hramp(X，Y)+hobstacle(X，Y) (3)
wherein h is the altitude value;
and step 3: using the obtained 3D slope map and assuming that the target area is the lowest altitude point, the route planning points from the starting point and searches for the previous neighboring point of each iteration with a lower height, wherein the obstacle model is higher than the other neighboring points and will not be selected during the route planning, and the drone automatically reaches the target area of the lowest point according to the route planning.
2. The method of claim 1, wherein during path planning, if the path planning determines that the loop is entered, increasing a point on the height of the drone and automatically moving to another direction.
3. The method according to claim 2, wherein any unmanned aerial vehicle in the unmanned aerial vehicle cluster detects a target through an onboard sensor, a wireless communication network of the unmanned aerial vehicle cluster is established, and if the unmanned aerial vehicle cluster detects the target ground, detection data of any unmanned aerial vehicle is processed, and processing information is broadcast to all unmanned aerial vehicles in the unmanned aerial vehicle cluster.
4. The method of claim 2, wherein an airborne sensor is used to acquire whether an obstacle model suddenly appears on a path of a next adjacent point from the unmanned aerial vehicle, a new direction position is temporarily set for a possible collision, and whether a flight collision avoidance is realized in a new moving direction is judged.
5. The method according to claim 4, characterized in that a safety threshold of potential field resultant force of unmanned aerial vehicle cooperation is preset according to the attribute value of the unmanned aerial vehicle, a piloting unmanned aerial vehicle is set, other unmanned aerial vehicles track the position of the piloting unmanned aerial vehicle in real time and calculate the relative distance and relative angle of each unmanned aerial vehicle and the piloting unmanned aerial vehicle; and calculating potential field resultant force among the unmanned aerial vehicle groups according to the relative distance and the relative angle, if the calculated potential field resultant force is smaller than a safety threshold, performing a low-control flight state on the unmanned aerial vehicle groups, if the calculated potential field resultant force is larger than the safety threshold, temporarily abandoning the task of flying to a destination and performing steering flight for reducing the potential field resultant force, and after flying for a preset time, performing potential field resultant force calculation among the unmanned aerial vehicle groups again.
6. A path planning device of unmanned aerial vehicle cluster based on BebopDRON system architecture, includes the unmanned aerial vehicle cluster that unmanned aerial vehicle of a plurality of BebopDRON system architectures constitutes, its characterized in that, path planning device includes:
the space modeling unit is used for constructing a two-dimensional map model of the unmanned aerial vehicle cluster, creating an area, identifying a movable unmanned aerial vehicle in the area, and assuming and setting the unmanned aerial vehicle as a blank area in the two-dimensional map model;
a fault modeling unit marking obstacle models existing in a space of the two-dimensional map model and obtaining a 3D slope map through the two-dimensional map model and the obstacle models,
wherein h isrampRepresenting the height of an object, said object being any point in the map, hobstacleHeight of the obstacle, XtargetIs the x-coordinate, Y, of the objecttargetFor the y-coordinate of the target, a complete 3D ramp map is obtained by equations (1) and (2) above:
h(X,Y)＝hramp(X，Y)+hobstacle(X，Y) (3)
wherein h is the altitude value;
and the path planning unit adopts the obtained 3D slope map and assumes that the target place is the lowest altitude point, the path planning points to and searches the previous adjacent point of each iteration with lower height from the starting point, wherein the barrier model is higher than other adjacent points and cannot be selected during the path planning, and the unmanned aerial vehicle automatically reaches the target place with the lowest point according to the path planning.
7. The device of claim 6, wherein the path planning device further comprises a cyclic check unit, which is used for increasing a point on the height of the unmanned aerial vehicle and automatically moving the unmanned aerial vehicle to another direction when the path planning determines that the unmanned aerial vehicle enters the cycle.
8. The apparatus according to claim 7, wherein the path planning apparatus further includes a drone swarm communication unit, and through the drone swarm communication unit, any drone in the drone swarm detects a target through an onboard sensor, and establishes a wireless communication network of the drone swarm, and if the drone swarm detects a target ground, the unmanned drone processes detection data of any drone and broadcasts processing information to all drones in the drone swarm.
9. The device of claim 6, wherein the path planning device further comprises an obstacle judgment auxiliary unit, which is used for acquiring whether an obstacle model suddenly appears on the path of the next adjacent point from the unmanned aerial vehicle through the airborne sensor, temporarily setting a new direction position for the possible collision at first, and judging whether the new moving direction realizes flight collision avoidance.
10. The apparatus of claim 9, wherein the path planning apparatus further comprises a drone safety calculation unit for presetting a safety threshold of potential field resultant force of drone cooperation according to an attribute value of a drone, setting a pilot drone, other drones tracking a position of the pilot drone in real time and calculating a relative distance and a relative angle of each drone to the pilot drone; calculating potential field resultant force among the unmanned aerial vehicle groups according to the relative distance and the relative angle, and if the calculated potential field resultant force is smaller than a safety threshold, performing low-control flight state on the unmanned aerial vehicle groups; and if the calculated potential field resultant force is larger than the safety threshold value, temporarily abandoning the task of flying to the destination and performing steering flight for reducing the potential field resultant force, and after flying for a preset time, calculating the potential field resultant force among the unmanned aerial vehicle groups again.
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| CN111538255A (en) * | 2020-06-19 | 2020-08-14 | 中国人民解放军国防科技大学 | Aircraft control method and system for anti-swarm unmanned aerial vehicle |
| CN113190047A (en) * | 2021-05-28 | 2021-07-30 | 广东工业大学 | Unmanned aerial vehicle group path identification method based on two-dimensional plane |
| CN113485362A (en) * | 2021-07-30 | 2021-10-08 | 美的集团(上海)有限公司 | Robot movement method and device and electronic equipment |
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