CN111650961A - 5G networked unmanned aerial vehicle formation anti-collision method based on improved artificial potential field - Google Patents

5G networked unmanned aerial vehicle formation anti-collision method based on improved artificial potential field Download PDF

Info

Publication number
CN111650961A
CN111650961A CN202010473587.3A CN202010473587A CN111650961A CN 111650961 A CN111650961 A CN 111650961A CN 202010473587 A CN202010473587 A CN 202010473587A CN 111650961 A CN111650961 A CN 111650961A
Authority
CN
China
Prior art keywords
unmanned aerial
aerial vehicle
field
unmanned
potential field
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010473587.3A
Other languages
Chinese (zh)
Inventor
赵太飞
张港
薛蓉莉
王子晨
李振宁
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xian University of Technology
Original Assignee
Xian University of Technology
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xian University of Technology filed Critical Xian University of Technology
Priority to CN202010473587.3A priority Critical patent/CN111650961A/en
Publication of CN111650961A publication Critical patent/CN111650961A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Traffic Control Systems (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention discloses an anti-collision method for a formation of 5G networked unmanned aerial vehicles based on an improved artificial potential field, which comprises the steps of acquiring the real-time position, the real-time course angle and the acceleration of each unmanned aerial vehicle in the formation of the unmanned aerial vehicles, the distance between the unmanned aerial vehicles and the space between obstacles and each unmanned aerial vehicle based on a 5G network, adjusting the acceleration and the course angle of the unmanned aerial vehicles by changing the artificial potential field between the unmanned aerial vehicles and the potential field of the obstacles in the space, and finally sending the information of the adjusted acceleration and the course angle of the unmanned aerial vehicles to an unmanned aerial vehicle control end through the 5G network to prevent the unmanned aerial vehicles from. The invention adjusts the acceleration and the course angle of the unmanned aerial vehicle by changing the artificial potential field between the unmanned aerial vehicle and the potential field of the obstacle in the airspace, solves the problem of local minimum value existing in the obstacle avoidance by the traditional artificial potential field method, effectively prevents the unmanned aerial vehicle from colliding with other unmanned aerial vehicles or the obstacle in the driving process, and improves the driving safety of the unmanned aerial vehicle.

Description

5G networked unmanned aerial vehicle formation anti-collision method based on improved artificial potential field
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle anti-collision, and relates to a 5G networked unmanned aerial vehicle formation anti-collision method based on an improved artificial potential field.
Background
With the continuous development of mobile internet, more and more communication devices are added, and this explosive data growth will bring great challenges to the communication network and also put higher demands on the wireless communication link. The comprehensive development of a fifth generation mobile communication system (5G) provides a communication link with a large bandwidth, high reliability and low time delay for a wireless communication network, so that the rapid development of unmanned aerial vehicle application is greatly promoted, the unmanned aerial vehicle application industry and a mobile communication technology are in a development trend of tight combination, an internet unmanned aerial vehicle is gradually formed, and a good development demand can be provided for the unmanned aerial vehicle industry application by means of a communication base station with wide distribution.
The unmanned aerial vehicle anti-collision method is one of research hotspots of unmanned aerial vehicles, the research on unmanned aerial vehicle anti-collision gradually goes from single unmanned aerial vehicle to multi-unmanned aerial vehicle formation flying, and the functions of each unmanned aerial vehicle can be exerted as much as possible by realizing effective control, rapid decision making and hierarchical management on the multi-unmanned aerial vehicle formation, so that the efficiency of completing tasks by the unmanned aerial vehicles is improved, and the application range of the unmanned aerial vehicles is enhanced. In the aspect of specific formation control algorithm, relatively mature research results exist in China, and a navigator following method, a behavior-based method, a virtual structure method and an artificial potential field method are mainly used.
The traditional artificial potential field method is proposed by Khatib in 1985, information such as the distribution condition and the state of obstacles is reflected in the environment by establishing a potential field, and the unmanned aerial vehicle determines the traveling speed and direction in turn according to the magnitude of the potential field value. The artificial potential field method is small in calculated amount, good in inclusion and simple to realize, can be used for well avoiding the problems of obstacles and the like, and can realize the problems of obstacle avoidance and collision prevention among the bodies when the unmanned aerial vehicles are flying in formation. But the method still has the problems of flight path oscillation in a narrow environment, unreachable targets near the barrier, easy falling into local minimum values and the like, and limits the exertion of the advantages of the artificial potential field method. Therefore, the traditional artificial potential field method needs to be improved, so that the artificial potential field method is more suitable for the anti-collision method of the formation of multiple unmanned aerial vehicles. The channel characteristics of the 5G network are combined, a traditional artificial potential field method is researched, and a multi-unmanned aerial vehicle formation anti-collision method conforming to the 5G network is designed.
Disclosure of Invention
The invention aims to provide a 5G networked unmanned aerial vehicle formation anti-collision method based on an improved artificial potential field, and solves the problem that a traditional artificial potential field method obstacle avoidance method has a local minimum value.
The invention adopts the technical scheme that the anti-collision method for the formation of the 5G networked unmanned aerial vehicles based on the improved artificial potential field comprises the steps of acquiring the real-time position, the real-time course angle and the acceleration of each unmanned aerial vehicle in the formation of the unmanned aerial vehicles, the distance between the unmanned aerial vehicles and each unmanned aerial vehicle, and the distance between obstacles in an airspace and each unmanned aerial vehicle based on the 5G network, adjusting the acceleration and the course angle of the unmanned aerial vehicles by changing the artificial potential field between the unmanned aerial vehicles and the potential field of the obstacles in the airspace, and finally sending the information of the adjusted acceleration and the course angle of the unmanned aerial vehicles to an unmanned aerial vehicle control end through the 5G network so as to prevent.
The present invention is also technically characterized in that,
the method is implemented according to the following steps:
step 1, constructing a formation flight model based on multiple unmanned aerial vehicles in a 5G network;
step 2, establishing an unmanned aerial vehicle formation anti-collision potential field aiming at the multi-unmanned aerial vehicle formation flight model established in the step 1, wherein the establishing comprises establishing an artificial potential field between unmanned aerial vehicles and a potential field of obstacles in an airspace;
and 3, adjusting the acceleration and the course angle of the unmanned aerial vehicle which is likely to collide according to the anti-collision potential field of the formation of the unmanned aerial vehicle so as to prevent the unmanned aerial vehicle from colliding, monitoring the flying distance between the unmanned aerial vehicle and the obstacle in real time, and updating the information of the storage table in real time.
In the step 1, the multi-unmanned aerial vehicle formation flight model in the 5G network is composed of a network communication system and a multi-unmanned aerial vehicle formation, the network communication system comprises a 5G signal base station and a centralized controller, and the 5G signal base station is used for carrying out network coverage on an area where the unmanned aerial vehicle formation is located.
The centralized controller is used for acquiring the real-time position, the real-time course angle, the driving speed and the acceleration of each unmanned aerial vehicle in the multi-unmanned aerial vehicle formation, calculating the distance between the unmanned aerial vehicles, the distance between obstacles and each unmanned aerial vehicle in an airspace, the artificial potential field between the unmanned aerial vehicles and the potential field of the obstacles in the airspace, and adjusting the acceleration and the course angle of the unmanned aerial vehicles.
Be provided with pilot A in the unmanned aerial vehicle formation, pilot A and 5G signal base station establish signal connection, make can intercommunication between the unmanned aerial vehicle is inside, pilot A makes corresponding flight instruction according to received signal, and will flight instruction transmits all the other unmanned aerial vehicles in the unmanned aerial vehicle formation of place, and follower B promptly makes and keeps certain flying distance constantly between the adjacent unmanned aerial vehicle.
In step 2, the inter-aircraft artificial potential field comprises a gravitational field and a repulsive field of the unmanned aerial vehicle, and the specific process of establishing the inter-aircraft artificial potential field is as follows:
step 2.1, establishing unmanned aerial vehicle U in the modeliAnd the other unmanned aerial vehicles UjThe gravitational field and the repulsive field between the two fields are as follows:
Figure BDA0002515121560000031
Figure BDA0002515121560000041
in the formula (I), the compound is shown in the specification,
Figure BDA0002515121560000042
express unmanned plane UiAnd UjThe gravitational field in between the two magnetic fields,
Figure BDA0002515121560000043
express unmanned plane UiAnd UjRepulsive force field of between, kijIs the gain coefficient of the gravitational field, | | ρijI denotes unmanned plane UiAnd UjRelative distance between them, | ρij||＝||ρi-ρj||；||ρij||minFor unmanned plane UiAnd UjA minimum safe distance therebetween; daRepresenting the extent of action of the gravitational field, DbC table showing the action range of repulsive force fieldUnmanned plane UiAnd UjThe coefficient of the change speed of the repulsive force field between the two, d represents the unmanned plane UiAnd UiThe variation amplitude coefficient of the repulsive force field;
Figure BDA0002515121560000044
Figure BDA0002515121560000045
in the formula, vijRepellent representation unmanned plane UiAnd UjThe repulsive force field between the two electrodes changes the speed in real time,
Figure BDA0002515121560000046
express unmanned plane UiAnd UjPredetermined variation speed of repulsive force field between fij repelExpress unmanned plane UiAnd UjThe repulsive force field between the two electrodes changes the amplitude in real time,
Figure BDA0002515121560000047
express unmanned plane UiAnd UjThe repulsive force field between the two electrodes is preset to change in amplitude;
step 2.2, establishing an unmanned aerial vehicle UiGravitational field of
Figure BDA0002515121560000048
And repulsive force field
Figure BDA0002515121560000049
Figure BDA00025151215600000410
Wherein N isiExpress to unmanned aerial vehicle UiSet of all drones with a potential field effect, aijExpress unmanned plane UiAnd UjLink weight between;
in step 2, the potential field of the barrier in the airspace is a three-dimensional space vector field consisting of X-Y and Y-Z vertical potential fields, and the unmanned aerial vehicleUiAnd an obstacle in a repulsive force field of
Figure BDA0002515121560000051
Figure BDA0002515121560000052
Figure BDA0002515121560000053
Figure BDA0002515121560000054
Wherein, | | ρioI denotes unmanned plane UiDistance from obstacle, | ρio||minExpress unmanned plane UiMinimum safety distance to obstacle, vi repelExpress unmanned plane UiThe repulsive force field between the magnetic field and the obstacle changes the speed in real time,
Figure BDA0002515121560000055
express unmanned plane UiPredetermined speed of change of repulsive force field with obstacle, fi repelExpress unmanned plane UiThe amplitude of the repulsive force field between the magnetic field and the obstacle changes in real time,
Figure BDA0002515121560000056
express unmanned plane UiThe repulsive force field between the obstacle and the obstacle is preset to change in amplitude; c. C0Express unmanned plane UiCoefficient of speed of change of repulsive force field with obstacle, d0Express unmanned plane UiAnd the repulsive force field between the obstacle changes by an amplitude coefficient.
Unmanned plane UiTotal potential field J ofiComprises the following steps:
Figure BDA0002515121560000057
Figure BDA0002515121560000058
Figure BDA0002515121560000059
wherein, JijFor unmanned plane UiAnd UjArtificial potential field in between, i.e. unmanned aerial vehicle UiAnd UjRelative distance | | ρ betweenijThe potential field produced by | l;
then unmanned plane UiThe virtual force at rho is Fi(ρ)：
Figure BDA00025151215600000510
In the formula (I), the compound is shown in the specification,
Figure BDA0002515121560000061
represents the potential field JiThe gradient at p is a vector, the direction being the direction along which the rate of change of the potential field at position p is greatest; when | | | ρijI | → 0, Jij→ infinity, unmanned plane UiThe virtual force is expressed as radial repulsive force; when | | | ρijI | → + ∞ time, Jij→ 0, unmanned plane UiThe virtual force is expressed as radial attraction.
The step 3 specifically comprises the following steps:
step 3.1, initializing a 5G network, recording each position node and link weight information of the unmanned aerial vehicle in a network connection table, and refreshing a current stored information value in real time;
step 3.2, when the distance between the unmanned aerial vehicle and the unmanned aerial vehicle is | | | rhoij||＜||ρij||minThen, proceed to step 3.3, when | | ρij||≥||ρij||minThen, step 3.4 is carried out, and step 3.5 is carried out when an obstacle appears in the monitoring range of the unmanned aerial vehicle;
step 3.3, introducing a repulsive force field between unmanned aerial vehicles
Figure BDA0002515121560000062
According to the link weight aijBy varying the repulsive force field
Figure BDA0002515121560000063
Unmanned aerial vehicle U with possibility of collision during size adjustmentiSpeed and heading angle of (1) to make | | ρij||≥||ρij||min；
Step 3.4, continuously monitoring the flight distance between the unmanned aerial vehicles in real time, and updating the information of the storage table in real time;
step 3.5, judging the distance | rho between the unmanned aerial vehicle and the obstacleioIf/| ρio||＜||ρio||minThen, proceed to step 3.6, when | | ρio||≥||ρio||minThen, step 3.7 is carried out;
step 3.6, in unmanned aerial vehicle UiAnd the barrier to introduce a repulsive force field
Figure BDA0002515121560000064
By varying repulsive force fields
Figure BDA0002515121560000065
Size adjustment unmanned aerial vehicle UiAnd then adjust unmanned aerial vehicle UiCourse angle of (1) to make unmanned aerial vehicle UiAway from the obstacle until the range of action of the obstacle potential field is exceeded;
step 3.7, make unmanned aerial vehicle UiAnd continuing flying, monitoring the distance between the unmanned aerial vehicle and the barrier in real time, and updating the storage information table.
Unmanned plane UiHas a velocity of Vi：
Figure BDA0002515121560000071
Fi(ρ)＝ma (14)
In the formula (I), the compound is shown in the specification,
Figure BDA0002515121560000072
express unmanned plane UiA denotes the unmanned plane UiThe acceleration of (a) is detected,t denotes unmanned plane UiTime of flight at acceleration a, m denotes the unmanned plane UiMass of (F)i(rho) is unmanned plane UiThe virtual force experienced;
unmanned plane UiIncluding a pitch angle
Figure BDA0002515121560000073
And yaw angle psiiWherein
Figure BDA0002515121560000074
Figure BDA0002515121560000075
Figure BDA0002515121560000076
In the formula, Vxi、Vyi、VziRespectively represent unmanned aerial vehicle UiVelocity ViVelocity components in the x, y, z directions.
The unmanned aerial vehicle has the advantages that the acceleration and the course angle of the unmanned aerial vehicle are adjusted by changing the artificial potential field between the unmanned aerial vehicle and the potential field of the obstacle in the airspace, and finally, the adjusted acceleration and course angle information of the unmanned aerial vehicle is sent to the control end of the unmanned aerial vehicle through the 5G network, so that the problem of local minimum value existing in the obstacle avoidance of the traditional artificial potential field method is solved, the unmanned aerial vehicle is effectively prevented from colliding with other unmanned aerial vehicles or obstacles in the driving process, and the driving safety of the unmanned aerial vehicle is improved; a flight model of unmanned aerial vehicle formation in the 5G network is built, flight service is provided for the unmanned aerial vehicle formation by means of 5G ultra-dense network and millimeter wave communication, the throughput obtained by each user is maximized, and the utilization rate of 5G network resources is improved.
Drawings
FIG. 1 is a schematic diagram of a formation flight model based on multiple unmanned aerial vehicles in a 5G network according to the present invention;
FIG. 2 is a schematic illustration of an artificial potential field in an embodiment of the invention;
FIG. 3 is a flow chart of the inter-locomotive collision avoidance implementation of the present invention;
fig. 4 is a flow chart of the implementation of obstacle avoidance in airspace according to the present invention.
In the figure, 1.5G signal base stations, 2 centralized controllers and 3 multi-unmanned aerial vehicle formation.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
The invention relates to a 5G networking unmanned aerial vehicle formation anti-collision method based on an improved artificial potential field, which comprises the steps of obtaining the real-time position, the real-time course angle and the acceleration of each unmanned aerial vehicle in an unmanned aerial vehicle formation, the distance between unmanned aerial vehicles and the space between the unmanned aerial vehicles, adjusting the acceleration and the course angle of the unmanned aerial vehicles by changing the artificial potential field between the unmanned aerial vehicles and the potential field of the obstacles in the space, and finally sending the adjusted information of the acceleration and the course angle of the unmanned aerial vehicles to an unmanned aerial vehicle control end through the 5G network so as to prevent the unmanned aerial vehicles from colliding.
The method is implemented according to the following steps:
step 1, constructing a formation flight model based on multiple unmanned aerial vehicles in a 5G network
Referring to fig. 1, the flight model for formation of multiple unmanned aerial vehicles in the 5G network of the invention is composed of a network communication system and multiple unmanned aerial vehicle formations 3, wherein the network communication system comprises a 5G signal base station 1 and a centralized controller 2, and the 5G signal base station 1 is used for network coverage of the area where the unmanned aerial vehicle formations 3 are located.
The centralized controller 2 is used for acquiring the real-time position, the real-time course angle, the driving speed and the acceleration of each unmanned aerial vehicle in the formation of the unmanned aerial vehicles, calculating the distance between obstacles in an airspace and each unmanned aerial vehicle, calculating the artificial potential field between the unmanned aerial vehicles and the potential field of the obstacles in the airspace, and adjusting the acceleration and the course angle of the unmanned aerial vehicles.
Be provided with pilot A in the unmanned aerial vehicle formation 3, pilot A and 5G signal base station 1 set up signal connection, make can intercommunication between the unmanned aerial vehicle is inside, pilot A makes corresponding flight instruction according to received signal, and will flight instruction transmits all the other unmanned aerial vehicles in the unmanned aerial vehicle formation 3 of place, and follower B promptly makes and keeps certain flight distance constantly between the adjacent unmanned aerial vehicle.
The multiple unmanned aerial vehicle formations are respectively connected with the 5G network through concurrent transmission, and a network communication system consisting of the 5G signal base station and the centralized controller can provide flight service for the unmanned aerial vehicles which can be monitored in the air in real time. In fig. 1, a1, a2 and A3 respectively represent pilots in three sub-teams, and B1, B2, B3, C1, C2 and C3 are followers of respective teams. In each unmanned aerial vehicle formation, a pilot and a follower form a triangular formation with a stable structure, the pilot and the follower can communicate with each other to keep a certain flight distance, and the pilot in the formation can receive signals in the 5G network to guide the unmanned aerial vehicle formation to fly safely.
Step 2, establishing an unmanned aerial vehicle formation anti-collision potential field aiming at the multi-unmanned aerial vehicle formation flight model established in the step 1, wherein the establishing comprises establishing an artificial potential field between unmanned aerial vehicles and a potential field of obstacles in an airspace;
(1) establishing an artificial potential field between unmanned aerial vehicles, wherein the artificial potential field between the unmanned aerial vehicles comprises a gravitational field and a repulsive field of the unmanned aerial vehicles;
step 2.1, establishing unmanned aerial vehicle U in the modeliAnd the other unmanned aerial vehicles UjThe gravitational field and the repulsive field between the two fields are as follows:
Figure BDA0002515121560000101
Figure BDA0002515121560000102
in the formula (I), the compound is shown in the specification,
Figure BDA0002515121560000103
express unmanned plane UiAnd UjThe gravitational field in between the two magnetic fields,
Figure BDA0002515121560000104
express unmanned plane UiAnd UjRepulsive force field of between, kijThe gain coefficient of the gravitational field is used for changing the strength of the gravitational field; | ρ |ijI denotes unmanned plane UiAnd UjRelative distance between them, | ρij||＝||ρi-ρj||；||ρij||minFor unmanned plane UiAnd UjA minimum safe distance therebetween; daRepresenting the extent of action of the gravitational field, DbFor the scope of action of the repulsive field, c denotes unmanned plane UiAnd UjThe coefficient of the change speed of the repulsive force field between the two, d represents the unmanned plane UiAnd UjC and d respectively determine the U of the unmanned aerial vehicleiAnd UjThe real-time change speed of the repulsive force field and the change amplitude of the repulsive force field;
Figure BDA0002515121560000105
Figure BDA0002515121560000106
in the formula, vij repelExpress unmanned plane UiAnd UjThe repulsive force field between the two electrodes changes the speed in real time,
Figure BDA0002515121560000107
express unmanned plane UiAnd UjPredetermined variation speed of repulsive force field between fij repelExpress unmanned plane UiAnd UjThe repulsive force field between the two electrodes changes the amplitude in real time,
Figure BDA0002515121560000108
express unmanned plane UiAnd UjThe repulsive force field between the two electrodes is preset to change in amplitude;
step 2.2, establishing an unmanned aerial vehicle UiGravitational field of
Figure BDA0002515121560000109
And repulsive force field
Figure BDA00025151215600001010
In order to tightly combine the priority of collision prevention between unmanned aerial vehicles and communication topology, the link weight a of the communication topology is introducedijThis means when multiple drones are in communication with the UiThe distance between the unmanned aerial vehicles is the same, and different unmanned aerial vehicles are to UiIn a repulsive force field of UiWhen collision avoidance is executed, the unmanned aerial vehicle with high connection weight can be avoided preferentially, so that the safety of the unmanned aerial vehicle (namely, a pilot) at the root node can be ensured, and the risk can be reduced to the minimum for the whole formation when an emergency occurs.
Figure BDA0002515121560000111
Wherein N isiExpress to unmanned aerial vehicle UiSet of all drones with a potential field effect, aijExpress unmanned plane UiAnd UjLink weight between;
(2) establishing a potential field of an obstacle in an airspace
The potential field of the obstacle in the airspace is a three-dimensional space vector field consisting of X-Y and Y-Z vertical potential fields, and the three-dimensional space vector field guides the unmanned aerial vehicle to select the optimal path to avoid the obstacle.
Because unmanned aerial vehicle is avoiding the in-process of barrier, only need consider the problem that unmanned aerial vehicle avoids the barrier, and need not consider the action of barrier, therefore the virtual force of its artifical potential field only need provide repulsion, and need not provide gravitation, unmanned aerial vehicle UiAnd an obstacle in a repulsive force field of
Figure BDA0002515121560000112
Figure BDA0002515121560000113
Figure BDA0002515121560000114
Figure BDA0002515121560000115
Wherein, | | ρioI denotes unmanned plane UiDistance from obstacle, | ρio||minExpress unmanned plane UiMinimum safety distance to obstacle, vi repelExpress unmanned plane UiThe repulsive force field between the magnetic field and the obstacle changes the speed in real time,
Figure BDA0002515121560000116
express unmanned plane UiPredetermined speed of change of repulsive force field with obstacle, fi repelExpress unmanned plane UiThe amplitude of the repulsive force field between the magnetic field and the obstacle changes in real time,
Figure BDA0002515121560000121
express unmanned plane UiThe repulsive force field between the obstacle and the obstacle is preset to change in amplitude; c. C0Express unmanned plane UiCoefficient of speed of change of repulsive force field with obstacle, d0Express unmanned plane UiCoefficient of variation of repulsive force field with obstacle, c0、d0Determine unmanned plane U respectivelyiThe speed of the change of the repulsive force field between the barrier and the barrier in real time and the change amplitude of the repulsive force field;
unmanned plane UiTotal potential field J ofiComprises the following steps:
Figure BDA0002515121560000122
Figure BDA0002515121560000123
Figure BDA0002515121560000124
wherein, JijFor unmanned plane UiAnd UjArtificial potential field in between, i.e. unmanned aerial vehicle UiAnd UjRelative distance | | ρ betweenijProduct | |A generated potential field;
as shown in FIG. 2, J (ρ) represents the artificial potential field of a single UAV in the multi-UAV formation flight model at ρ, J (ρ)1)、J(ρ2)、J(ρ3) Are respectively unmanned aerial vehicle U1、U2、U3The potential field of (2).
Unmanned plane UiThe virtual force at rho is Fi(ρ)：
Figure BDA0002515121560000125
In the formula (I), the compound is shown in the specification,
Figure BDA0002515121560000126
represents the potential field JiThe gradient at p is a vector, the direction being the direction along which the rate of change of the potential field at position p is greatest; when | | | ρijI | → 0, Jij→ infinity, unmanned plane UiThe virtual force is expressed as radial repulsive force; when | | | ρijI | → + ∞ time, Jij→ 0, unmanned plane UiThe virtual force is expressed as radial attraction.
In the artificial potential field method, the negative gradient of the potential field
Figure BDA0002515121560000131
As the virtual force acting on the unmanned aerial vehicle, the obstacle generates repulsive force to the unmanned aerial vehicle, the target point generates attractive force, and the resultant force of the attractive force and the repulsive force is used as the acceleration of the unmanned aerial vehicle to guide the unmanned aerial vehicle to do collision-free motion.
And 3, adjusting the acceleration and the course angle of the unmanned aerial vehicle which is likely to collide according to the anti-collision potential field of the formation of the unmanned aerial vehicle, monitoring the flight distance between the unmanned aerial vehicle and the obstacle in real time, and updating the information of the storage table in real time.
Referring to fig. 3, the inter-drone formation inter-station collision prevention includes the following steps:
step 3.1, initializing a 5G network, recording each position node and link weight information of the unmanned aerial vehicle in a network connection table, and refreshing a current stored information value in real time;
step 3.2, when the distance between the unmanned aerial vehicle and the unmanned aerial vehicle is | | | rhoij||＜||ρij||minThen, proceed to step 3.3, when | | ρij||≥||ρij||minThen, step 3.4 is carried out;
step 3.3, introducing a repulsive force field between unmanned aerial vehicles
Figure BDA0002515121560000132
By varying vij repelThereby changing the repulsive force field
Figure BDA0002515121560000133
Change the unmanned aerial vehicle U that probably collidesiSpeed of, adjust unmanned aerial vehicle UiThe course angle of the unmanned aerial vehicle enables the distance between the unmanned aerial vehicles to be recovered to be normal even if | rho |ij||≥||ρij||minSo as to avoid collision between unmanned aerial vehicles; according to the link weight aijThe priority of the unmanned aerial vehicle is to ensure that a pilot A in the formation of the unmanned aerial vehicles is not collided and then ensure that a follower B is not collided;
and 3.4, continuously monitoring the flight distance between the unmanned aerial vehicles in real time, and updating the information of the storage table in real time.
Referring to fig. 4, the formation of unmanned aerial vehicles in the airspace obstacle avoidance includes the following steps:
step 3.11, initializing the 5G network, recording each position node and link weight information of the unmanned aerial vehicle in a network connection table, and refreshing a current stored information value in real time;
step 3.12, when the unmanned aerial vehicle monitors the obstacle, the position and direction information of the unmanned aerial vehicle is recorded, and the distance | rho between the unmanned aerial vehicle and the obstacle is judgedioIf/| ρio||＜||ρio||minThen, proceed to step 3.13, when | | ρio||≥||ρio||minThen, step 3.14 is performed;
step 3.13, in unmanned plane UiAnd the barrier to introduce a repulsive force field
Figure BDA0002515121560000145
By varying the repulsive forceField(s)
Figure BDA0002515121560000146
Size of reducing unmanned aerial vehicle UiAcceleration, and then adjust unmanned aerial vehicle UiCourse angle of (1) to make unmanned aerial vehicle UiAway from the obstacle until the range of action of the obstacle potential field is exceeded;
step 3.14, make unmanned aerial vehicle UiAnd continuing flying, monitoring the distance between the unmanned aerial vehicle and the barrier in real time, and updating the storage information table.
The collision avoidance action is realized in the artificial potential field by adjusting the unmanned aerial vehicle UiRepulsive force field change speed and amplitude, so that the repulsive force field, the total potential field and the received virtual force are adjusted, and then the unmanned aerial vehicle U is adjusted according to the received virtual forceiOf the acceleration of which finally the velocity V is realizediAdjustment of course angle, speed ViIs a vector, wherein:
Figure BDA0002515121560000141
Fi(ρ)＝ma (14)
in the formula (I), the compound is shown in the specification,
Figure BDA0002515121560000142
express unmanned plane UiA denotes the unmanned plane UiAcceleration of (d), t represents unmanned plane UiTime of flight at acceleration a, m denotes the unmanned plane UiMass of (F)i(rho) is unmanned plane UiThe virtual force experienced;
unmanned plane UiIncluding a pitch angle
Figure BDA0002515121560000143
And yaw angle psiiWherein
Figure BDA0002515121560000144
Figure BDA0002515121560000151
Figure BDA0002515121560000152
In the formula, Vxi、Vyi、VziRespectively represent unmanned aerial vehicle UiVelocity ViVelocity components in the x, y, z directions.
According to the method, on the basis of communication between a 5G ultra-dense network and millimeter waves, a multi-unmanned aerial vehicle formation flight model suitable for the 5G network is designed, a corresponding potential field function is set on the basis of an improved artificial potential field method, and a multi-unmanned aerial vehicle formation anti-collision method based on the improved artificial potential field method in the 5G network is designed by combining the characteristics of the ultra-dense property of the 5G network and the improved artificial potential field method, so that the safety of the unmanned aerial vehicle in the driving process is improved.

Claims (10)

1. The anti-collision method for the formation of the 5G internet unmanned aerial vehicles based on the improved artificial potential field is characterized by comprising the steps of obtaining the real-time position, the real-time course angle and the acceleration of each unmanned aerial vehicle in the formation of the unmanned aerial vehicles based on the 5G network, obtaining the distance between the unmanned aerial vehicles and barriers in an airspace, adjusting the acceleration and the course angle of each unmanned aerial vehicle by changing the artificial potential field between the unmanned aerial vehicles and the potential field of the barriers in the airspace, and finally sending the information of the adjusted acceleration and the course angle of each unmanned aerial vehicle to an unmanned aerial vehicle control terminal through the 5G network to prevent the unmanned aerial vehicles from colliding.
2. The 5G networked unmanned aerial vehicle formation anti-collision method based on the improved artificial potential field is characterized by comprising the following steps:
step 1, constructing a formation flight model based on multiple unmanned aerial vehicles in a 5G network;
step 2, establishing an unmanned aerial vehicle formation anti-collision potential field aiming at the multi-unmanned aerial vehicle formation flight model established in the step 1, wherein the establishing comprises establishing an artificial potential field between unmanned aerial vehicles and a potential field of obstacles in an airspace;
and 3, adjusting the acceleration and the course angle of the unmanned aerial vehicle which is likely to collide according to the anti-collision potential field of the formation of the unmanned aerial vehicle so as to prevent the unmanned aerial vehicle from colliding, monitoring the flying distance between the unmanned aerial vehicle and the obstacle in real time, and updating the information of the storage table in real time.
3. The 5G networked unmanned aerial vehicle formation anti-collision method based on the improved artificial potential field according to claim 2, wherein in the step 1, a multi-unmanned aerial vehicle formation flight model in the 5G network is composed of a network communication system and a multi-unmanned aerial vehicle formation (3), the network communication system comprises a 5G signal base station (1) and a centralized controller (2), and the 5G signal base station (1) is used for carrying out network coverage on an area where the unmanned aerial vehicle formation (3) is located.
4. The 5G networked unmanned aerial vehicle formation anti-collision method based on the improved artificial potential field according to claim 3, wherein the centralized controller (2) is used for acquiring real-time positions, real-time heading angles, driving speeds and accelerations of all unmanned aerial vehicles in the multi-unmanned aerial vehicle formation, calculating distances between unmanned aerial vehicles, distances between obstacles in an airspace and all unmanned aerial vehicles, artificial potential fields between unmanned aerial vehicles and potential fields of obstacles in the airspace, and adjusting the accelerations and heading angles of the unmanned aerial vehicles.
5. The anti-collision method for the formation of the 5G networked unmanned aerial vehicles based on the improved artificial potential field according to claim 4, wherein a pilot A is arranged in the formation (3) of the unmanned aerial vehicles, the pilot A is in signal connection with the 5G signal base station (1) to enable the insides of the unmanned aerial vehicles to be communicated with each other, the pilot A makes a corresponding flight instruction according to the received signal, and transmits the flight instruction to the other unmanned aerial vehicles in the formation (3) of the unmanned aerial vehicles, namely, the followers B, so that a certain flight distance is constantly kept between the adjacent unmanned aerial vehicles.
6. The 5G networked unmanned aerial vehicle formation anti-collision method based on the improved artificial potential field according to claim 2, wherein in the step 2, the artificial potential field between the unmanned aerial vehicles comprises a gravitational field and a repulsive field of the unmanned aerial vehicles, and the specific process of establishing the artificial potential field between the unmanned aerial vehicles is as follows:
step 2.1, establishing unmanned aerial vehicle U in the modeliAnd the other unmanned aerial vehicles UjThe gravitational field and the repulsive field between the two fields are as follows:
Figure FDA0002515121550000021
Figure FDA0002515121550000022
in the formula (I), the compound is shown in the specification,
Figure FDA0002515121550000023
express unmanned plane UiAnd UjThe gravitational field in between the two magnetic fields,
Figure FDA0002515121550000024
express unmanned plane UiAnd UjRepulsive force field of between, kijIs the gain coefficient of the gravitational field, | | ρijI denotes unmanned plane UiAnd UjRelative distance between them, | ρij||＝||ρi-ρj||；||ρij||minFor unmanned plane UiAnd UjA minimum safe distance therebetween; daRepresenting the extent of action of the gravitational field, DbFor the scope of action of the repulsive field, c denotes unmanned plane UiAnd UjThe coefficient of the change speed of the repulsive force field between the two, d represents the unmanned plane UiAnd UjThe variation amplitude coefficient of the repulsive force field;
Figure FDA0002515121550000031
Figure FDA0002515121550000032
in the formula, vij repelTo indicate nobodyMachine UiAnd UjThe repulsive force field between the two electrodes changes the speed in real time,
Figure FDA0002515121550000033
express unmanned plane UiAnd UjPredetermined variation speed of repulsive force field between fij repelExpress unmanned plane UiAnd UjThe repulsive force field between the two electrodes changes the amplitude in real time,
Figure FDA0002515121550000034
express unmanned plane UiAnd UjThe repulsive force field between the two electrodes is preset to change in amplitude;
step 2.2, establishing an unmanned aerial vehicle UiGravitational field of
Figure FDA0002515121550000035
And repulsive force field
Figure FDA0002515121550000036
Figure FDA0002515121550000037
Wherein N isiExpress to unmanned aerial vehicle UiSet of all drones with a potential field effect, aijExpress unmanned plane UiAnd UjThe link weight between.
7. The method according to claim 6, wherein in the step 2, the potential field of the obstacle in the airspace is a three-dimensional space vector field composed of two vertical potential fields of X-Y and Y-Z, and the unmanned aerial vehicle U is a U-shaped space vector fieldiAnd an obstacle in a repulsive force field of
Figure FDA0002515121550000038
Figure FDA0002515121550000039
Figure FDA00025151215500000310
Figure FDA00025151215500000311
Wherein, | | ρioI denotes unmanned plane UiDistance from obstacle, | ρio||minExpress unmanned plane UiMinimum safety distance to obstacle, vi repelExpress unmanned plane UiThe repulsive force field between the magnetic field and the obstacle changes the speed in real time,
Figure FDA0002515121550000041
express unmanned plane UiPredetermined speed of change of repulsive force field with obstacle, fi repelExpress unmanned plane UiThe amplitude of the repulsive force field between the magnetic field and the obstacle changes in real time,
Figure FDA0002515121550000042
express unmanned plane UiThe repulsive force field between the obstacle and the obstacle is preset to change in amplitude; c. C0Express unmanned plane UiCoefficient of speed of change of repulsive force field with obstacle, d0Express unmanned plane UiAnd the repulsive force field between the obstacle changes by an amplitude coefficient.
8. The improved artificial potential field-based 5G networked unmanned aerial vehicle formation anti-collision method according to claim 7, wherein the unmanned aerial vehicles U are connected in a formation modeiTotal potential field J ofiComprises the following steps:
Figure FDA0002515121550000043
Figure FDA0002515121550000044
Figure FDA0002515121550000045
wherein, JijFor unmanned plane UiAnd UjArtificial potential field in between, i.e. unmanned aerial vehicle UiAnd UjRelative distance | | ρ betweenijThe potential field produced by | l;
then unmanned plane UiThe virtual force at rho is Fi(ρ)：
Figure FDA0002515121550000046
In the formula (I), the compound is shown in the specification,
Figure FDA0002515121550000047
represents the potential field JiThe gradient at p is a vector, the direction being the direction along which the rate of change of the potential field at position p is greatest; when | | | ρijI | → 0, Jij→ infinity, unmanned plane UiThe virtual force is expressed as radial repulsive force; when | | | ρijI | → + ∞ time, Jij→ 0, unmanned plane UiThe virtual force is expressed as radial attraction.
9. The 5G networked unmanned aerial vehicle formation anti-collision method based on the improved artificial potential field according to claim 8, wherein the step 3 specifically comprises the following steps:
step 3.1, initializing a 5G network, recording each position node and link weight information of the unmanned aerial vehicle in a network connection table, and refreshing a current stored information value in real time;
step 3.2, when the distance between the unmanned aerial vehicle and the unmanned aerial vehicle is | | | rhoij||<||ρij||minThen, proceed to step 3.3, when | | ρij||≥||ρij||minThen, step 3.4 is carried out, and step 3.5 is carried out when an obstacle appears in the monitoring range of the unmanned aerial vehicle;
step 3.3, introducing a repulsive force field between unmanned aerial vehicles
Figure FDA0002515121550000051
According to the link weight aijBy varying the repulsive force field
Figure FDA0002515121550000052
Unmanned aerial vehicle U with possibility of collision during size adjustmentiAcceleration and course angle of (1), make | | ρij||≥||ρij||min；
Step 3.4, continuously monitoring the flight distance between the unmanned aerial vehicles in real time, and updating the information of the storage table in real time;
step 3.5, judging the distance | rho between the unmanned aerial vehicle and the obstacleioIf/| ρio||<||ρio||minThen, proceed to step 3.6, when | | ρio||≥||ρio||minThen, step 3.7 is carried out;
step 3.6, in unmanned aerial vehicle UiAnd the barrier to introduce a repulsive force field
Figure FDA0002515121550000053
By varying repulsive force fields
Figure FDA0002515121550000054
Size adjustment unmanned aerial vehicle UiAcceleration, and then adjust unmanned aerial vehicle UiCourse angle of (1) to make unmanned aerial vehicle UiAway from the obstacle until the range of action of the obstacle potential field is exceeded;
step 3.7, make unmanned aerial vehicle UiAnd continuing flying, monitoring the distance between the unmanned aerial vehicle and the barrier in real time, and updating the storage information table.
10. The improved artificial potential field-based 5G networked unmanned aerial vehicle formation anti-collision method according to claim 9, wherein the unmanned aerial vehicles U are connected in a formation modeiHas a velocity of Vi：
Figure FDA0002515121550000061
Fi(ρ)＝ma (14)
In the formula (I), the compound is shown in the specification,
Figure FDA0002515121550000062
express unmanned plane UiA denotes the unmanned plane UiAcceleration of (d), t represents unmanned plane UiTime of flight at acceleration a, m denotes the unmanned plane UiMass of (F)i(rho) is unmanned plane UiThe virtual force experienced;
unmanned aerial vehicle UiIncluding a pitch angle thetaiAnd yaw angle psiiWherein
Figure FDA0002515121550000063
Figure FDA0002515121550000064
Figure FDA0002515121550000065
In the formula, Vxi、Vyi、VziRespectively represent unmanned aerial vehicle UiVelocity ViVelocity components in the x, y, z directions.
CN202010473587.3A 2020-05-29 2020-05-29 5G networked unmanned aerial vehicle formation anti-collision method based on improved artificial potential field Pending CN111650961A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010473587.3A CN111650961A (en) 2020-05-29 2020-05-29 5G networked unmanned aerial vehicle formation anti-collision method based on improved artificial potential field

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010473587.3A CN111650961A (en) 2020-05-29 2020-05-29 5G networked unmanned aerial vehicle formation anti-collision method based on improved artificial potential field

Publications (1)

Publication Number Publication Date
CN111650961A true CN111650961A (en) 2020-09-11

Family

ID=72343446

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010473587.3A Pending CN111650961A (en) 2020-05-29 2020-05-29 5G networked unmanned aerial vehicle formation anti-collision method based on improved artificial potential field

Country Status (1)

Country Link
CN (1) CN111650961A (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113093767A (en) * 2021-03-10 2021-07-09 中国人民解放军海军潜艇学院 Formation control method for formation of underwater unmanned aircraft
CN113296497A (en) * 2021-04-07 2021-08-24 西安电子科技大学 Real-time distributed multi-robot formation control method and system
CN113341956A (en) * 2021-05-20 2021-09-03 西安交通大学 Multi-agent master-slave formation control method based on improved artificial potential field method
CN113485454A (en) * 2021-08-23 2021-10-08 南京航空航天大学 Unmanned aerial vehicle obstacle avoidance maneuvering method and system for geometrically improved artificial potential field
CN116679757A (en) * 2023-07-31 2023-09-01 深圳高度创新技术有限公司 Obstacle avoidance method of unmanned aerial vehicle and unmanned aerial vehicle

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102096415A (en) * 2010-12-31 2011-06-15 重庆邮电大学 Multi-robot formation method based on Ad-Hoc network and leader-follower algorithm
CN105843255A (en) * 2016-05-12 2016-08-10 深圳市艾博航空模型技术有限公司 Unmanned aerial vehicle cluster automatic flight crashproof apparatus and crashproof method
US9417325B1 (en) * 2014-01-10 2016-08-16 Google Inc. Interface for accessing radar data
US20160309339A1 (en) * 2015-04-14 2016-10-20 ETAK Systems, LLC Unmanned aerial vehicle-based systems and methods associated with cell sites and cell towers
US20170344000A1 (en) * 2016-05-27 2017-11-30 Qualcomm Incorporated Unmanned Aerial Vehicle Charging Station Management
US20180074520A1 (en) * 2016-09-13 2018-03-15 Arrowonics Technologies Ltd. Formation flight path coordination of unmanned aerial vehicles
CN108549403A (en) * 2018-03-30 2018-09-18 北京润科通用技术有限公司 A kind of collaborative obstacle avoidance method and device
CN108549407A (en) * 2018-05-23 2018-09-18 哈尔滨工业大学（威海） A kind of control algolithm of multiple no-manned plane collaboration formation avoidance
CN109196947A (en) * 2018-08-30 2019-01-11 北京小米移动软件有限公司 Unmanned plane during flying path providing method, acquisition methods, apparatus and system
CN110413006A (en) * 2019-08-30 2019-11-05 杭州电子科技大学 A kind of unmanned plane formation group network system and method based on 5G communication
US20190355145A1 (en) * 2018-05-21 2019-11-21 Microsoft Technology Licensing, Llc Precision mapping using autonomous devices
CN110764531A (en) * 2019-11-12 2020-02-07 西北工业大学 Unmanned aerial vehicle formation flying obstacle avoidance method based on laser radar and artificial potential field method

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102096415A (en) * 2010-12-31 2011-06-15 重庆邮电大学 Multi-robot formation method based on Ad-Hoc network and leader-follower algorithm
US9417325B1 (en) * 2014-01-10 2016-08-16 Google Inc. Interface for accessing radar data
US20160309339A1 (en) * 2015-04-14 2016-10-20 ETAK Systems, LLC Unmanned aerial vehicle-based systems and methods associated with cell sites and cell towers
CN105843255A (en) * 2016-05-12 2016-08-10 深圳市艾博航空模型技术有限公司 Unmanned aerial vehicle cluster automatic flight crashproof apparatus and crashproof method
US20170344000A1 (en) * 2016-05-27 2017-11-30 Qualcomm Incorporated Unmanned Aerial Vehicle Charging Station Management
US20180074520A1 (en) * 2016-09-13 2018-03-15 Arrowonics Technologies Ltd. Formation flight path coordination of unmanned aerial vehicles
CN108549403A (en) * 2018-03-30 2018-09-18 北京润科通用技术有限公司 A kind of collaborative obstacle avoidance method and device
US20190355145A1 (en) * 2018-05-21 2019-11-21 Microsoft Technology Licensing, Llc Precision mapping using autonomous devices
CN108549407A (en) * 2018-05-23 2018-09-18 哈尔滨工业大学（威海） A kind of control algolithm of multiple no-manned plane collaboration formation avoidance
CN109196947A (en) * 2018-08-30 2019-01-11 北京小米移动软件有限公司 Unmanned plane during flying path providing method, acquisition methods, apparatus and system
CN110413006A (en) * 2019-08-30 2019-11-05 杭州电子科技大学 A kind of unmanned plane formation group network system and method based on 5G communication
CN110764531A (en) * 2019-11-12 2020-02-07 西北工业大学 Unmanned aerial vehicle formation flying obstacle avoidance method based on laser radar and artificial potential field method

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
朱旭等: "基于改进人工势场的无人机编队防碰撞控制方法", 《哈尔滨工程大学学报》 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113093767A (en) * 2021-03-10 2021-07-09 中国人民解放军海军潜艇学院 Formation control method for formation of underwater unmanned aircraft
CN113296497A (en) * 2021-04-07 2021-08-24 西安电子科技大学 Real-time distributed multi-robot formation control method and system
CN113341956A (en) * 2021-05-20 2021-09-03 西安交通大学 Multi-agent master-slave formation control method based on improved artificial potential field method
CN113341956B (en) * 2021-05-20 2022-10-28 西安交通大学 Multi-agent master-slave formation control method based on improved artificial potential field method
CN113485454A (en) * 2021-08-23 2021-10-08 南京航空航天大学 Unmanned aerial vehicle obstacle avoidance maneuvering method and system for geometrically improved artificial potential field
CN116679757A (en) * 2023-07-31 2023-09-01 深圳高度创新技术有限公司 Obstacle avoidance method of unmanned aerial vehicle and unmanned aerial vehicle
CN116679757B (en) * 2023-07-31 2023-10-27 深圳高度创新技术有限公司 Obstacle avoidance method of unmanned aerial vehicle and unmanned aerial vehicle

Similar Documents

Publication Publication Date Title
CN111650961A (en) 5G networked unmanned aerial vehicle formation anti-collision method based on improved artificial potential field
CN108549407B (en) Control algorithm for multi-unmanned aerial vehicle cooperative formation obstacle avoidance
CN110119158B (en) Multi-machine cooperative formation control system and method for high subsonic speed unmanned aerial vehicle
CN108594853B (en) Unmanned aerial vehicle formation control method
CN111132258B (en) Unmanned aerial vehicle cluster collaborative opportunity routing method based on virtual potential field method
CN110414722B (en) Unmanned aerial vehicle cooperative reconnaissance path planning method based on energy consumption fairness
CN109286913B (en) Energy consumption optimization method of unmanned aerial vehicle mobile edge computing system based on cellular network connection
CN102096415B (en) Multi-robot formation method based on Ad-Hoc network and leader-follower algorithm
CN110730495A (en) Unmanned aerial vehicle data distribution optimization method under energy constraint
Na et al. UAV-based wide-area internet of things: an integrated deployment architecture
CN114389679B (en) Multi-antenna unmanned aerial vehicle sensing and transmission optimization method based on information age minimization
CN111953397B (en) Full-duplex unmanned aerial vehicle relay communication method facing free channel
CN115933644A (en) Multi-unmanned vehicle formation overall obstacle avoidance method based on improved artificial potential field method
CN108279704A (en) A kind of coordination control strategy for the multiple no-manned plane increasing communication link
CN116501094B (en) Unmanned aerial vehicle cluster control method based on self-organizing model
Misra et al. Dynamic leader selection in a master-slave architecture-based micro UAV swarm
CN114615759A (en) Unmanned aerial vehicle auxiliary communication method in non-orthogonal multiple access network
CN112882493A (en) Cluster cooperative deployment method based on distributed optimal energy MPC
CN116339321A (en) Global information driven distributed multi-robot reinforcement learning formation surrounding method based on 5G communication
CN114564044B (en) Unmanned aerial vehicle limited time formation control method triggered by input amplitude limiting event
CN112243239B (en) Unmanned aerial vehicle deployment method based on overpass and related device
CN114578851A (en) Unmanned aerial vehicle cluster fast steering method based on differential acceleration
CN115065976A (en) Efficient green stereo coverage scheme under global-oriented emergency communication scene
CN114779828A (en) Unmanned aerial vehicle cluster topological control and intelligent anti-collision method based on heterogeneous formation datum points
CN113784042A (en) Self-organizing reconnaissance monitoring method of unmanned aerial vehicle cluster in obstacle environment

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20200911