CN114779828A - Unmanned aerial vehicle cluster topological control and intelligent anti-collision method based on heterogeneous formation datum points - Google Patents

Unmanned aerial vehicle cluster topological control and intelligent anti-collision method based on heterogeneous formation datum points Download PDF

Info

Publication number
CN114779828A
CN114779828A CN202210709158.0A CN202210709158A CN114779828A CN 114779828 A CN114779828 A CN 114779828A CN 202210709158 A CN202210709158 A CN 202210709158A CN 114779828 A CN114779828 A CN 114779828A
Authority
CN
China
Prior art keywords
unmanned aerial
heterogeneous
aerial vehicle
formation
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.)
Granted
Application number
CN202210709158.0A
Other languages
Chinese (zh)
Other versions
CN114779828B (en
Inventor
席在杰
秦萌
周睿
赵政宁
曾勇
余炎
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.)
Sichuan Tengdun Technology Co Ltd
Original Assignee
Sichuan Tengdun Technology Co Ltd
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 Sichuan Tengdun Technology Co Ltd filed Critical Sichuan Tengdun Technology Co Ltd
Priority to CN202210709158.0A priority Critical patent/CN114779828B/en
Publication of CN114779828A publication Critical patent/CN114779828A/en
Application granted granted Critical
Publication of CN114779828B publication Critical patent/CN114779828B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention discloses an unmanned aerial vehicle cluster topological control and intelligent collision avoidance method based on heterogeneous formation datum points, which comprises the following steps: s1, predicting the position track of the surrounding nodes and the next moment of the nodes by an over-track prediction algorithm; s2, according to the predicted position track, adopting a speed self-adaptive adjustment anti-collision algorithm based on collision risk factors and track prediction to complete intelligent anti-collision processing among the heterogeneous unmanned aerial vehicles; and S3, on the basis of intelligent anti-collision processing, maintaining a virtual potential field by constructing an improved dynamic potential field and a formation based on heterogeneous formation datum points, and completing formation control among heterogeneous unmanned aerial vehicles by adopting a consistency group topology control algorithm of heterogeneous unmanned aerial vehicle cluster formation based on an improved dynamic strengthening potential field. The invention relates to a cluster cooperative obstacle avoidance group topology control method study by comprehensively considering a large/small heterogeneous unmanned aerial vehicle cluster system.

Description

Unmanned aerial vehicle cluster topological control and intelligent anti-collision method based on heterogeneous formation datum points
Technical Field
The invention relates to the technical field of cluster communication of heterogeneous unmanned aerial vehicles, in particular to an unmanned aerial vehicle cluster topological control and intelligent anti-collision method based on heterogeneous formation datum points.
Background
A heterogeneous unmanned aerial vehicle cluster formation system is characterized in that a plurality of heterogeneous unmanned aerial vehicles (large/small unmanned aerial vehicles and the like) form a multi-node cluster communication network through a specific cluster formation networking protocol, and network nodes interact various service messages through the cluster network under various preset formation cluster topologies, so that autonomous intelligent collision avoidance is completed, and specified tasks are cooperatively executed.
The formation configuration and the group topology change condition of the heterogeneous unmanned aerial vehicle cluster formation system have important influence on the task execution condition of the whole cluster formation, and the final task success or failure is directly influenced. At present, the traditional unmanned aerial vehicle cluster topology control research mainly aims at the small unmanned aerial vehicle cluster (non-heterogeneous) topology control, solves the problem of formation consistency, and does not aim at the cluster topology control and self-adaptive collision avoidance research of heterogeneous unmanned aerial vehicles (large/small unmanned aerial vehicles and other mixed clusters). For a heterogeneous unmanned aerial vehicle hybrid cluster networking scene, consistency group topology optimization and autonomous intelligent anti-collision capability among heterogeneous unmanned aerial vehicle cluster members need to be optimized and improved.
Disclosure of Invention
Aiming at the defects in the prior art, the unmanned aerial vehicle cluster topological control and intelligent anti-collision method based on the heterogeneous formation datum points solves the problem that the cluster topological control and adaptive anti-collision research aiming at the heterogeneous unmanned aerial vehicles is unavailable.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: an unmanned aerial vehicle cluster topological control and intelligent collision avoidance method based on heterogeneous formation datum points comprises the following steps:
s1, the heterogeneous unmanned aerial vehicle nodes maintain information through an interactive network, the position, speed, course and safety radius information of the surrounding heterogeneous nodes are obtained, and the position tracks of the surrounding nodes and the next moment of the nodes are predicted through a track prediction algorithm;
s2, according to the predicted position track, adopting a speed self-adaptive adjustment anti-collision algorithm based on collision risk factors and track prediction to finish intelligent anti-collision processing among the heterogeneous unmanned aerial vehicles;
and S3, on the basis of intelligent anti-collision processing, maintaining a virtual potential field by constructing an improved dynamic potential field and a formation based on heterogeneous formation datum points, and completing formation control among heterogeneous unmanned aerial vehicles by adopting a consistency group topology control algorithm of heterogeneous unmanned aerial vehicle cluster formation based on an improved dynamic strengthening potential field.
Further, the method comprises the following steps: the step S1 specifically includes: the heterogeneous unmanned aerial vehicle node receives network maintenance messages sent by other surrounding nodes, measures message arrival time, predicts the position and the track of the surrounding nodes at the next moment through a Kalman filtering algorithm, predicts the position and the track of the heterogeneous unmanned aerial vehicle node at the next moment, and obtains the relative position, the speed and the safety distance between the heterogeneous unmanned aerial vehicle node and the surrounding unmanned aerial vehicles at the next moment according to all prediction results.
Further, the method comprises the following steps: the network maintenance message comprises the address of the node, the heterogeneous type, the sending time, the position of the local machine, the speed, the course and the anti-collision safety radius information.
Further, the method comprises the following steps: the calculation method of the collision risk factor comprises the following steps:
order to
Figure 100002_DEST_PATH_IMAGE001
In which
Figure 954549DEST_PATH_IMAGE002
In order to be a safe distance from the user,dis the relative distance, i.e. the difference between the position of the machine and the other unmanned aerial vehicles, r1Is the safe radius of the machine, r2Other unmanned aerial vehicle safe radiuses;
when the temperature is higher than the set temperature
Figure 100002_DEST_PATH_IMAGE003
In the "safe" state, the collision risk factor
Figure 822142DEST_PATH_IMAGE004
Wherein, in the process,
Figure 100002_DEST_PATH_IMAGE005
for safety factor, the value is 0.3;
when in use
Figure 720827DEST_PATH_IMAGE006
In the "light danger" state, the collision danger factor
Figure 100002_DEST_PATH_IMAGE007
Wherein, in the step (A),
Figure 290349DEST_PATH_IMAGE008
the mild risk coefficient is 1, the value is 0.4,
Figure 100002_DEST_PATH_IMAGE009
the mild risk coefficient is 2, and the value is 0.3;
when in use
Figure 909680DEST_PATH_IMAGE010
In the "severe danger" state, the collision danger factor
Figure 100002_DEST_PATH_IMAGE011
Wherein, in the process,
Figure 21993DEST_PATH_IMAGE012
the serious danger coefficient is 1, the value is 0.3,
Figure 100002_DEST_PATH_IMAGE013
the serious danger coefficient is 2, and the value is 0.7.
Further: the self-adaptive adjustment anti-collision algorithm specifically comprises the following steps:
when the collision risk factor value is in a 'safe' state, no modulation is needed;
when the collision risk factor value is in a state of light danger and severe danger, the speed and the course of the self-adaptive adjusting machine are adjusted; the adjusted speed is as follows:
Figure 950635DEST_PATH_IMAGE014
wherein, in the step (A),
Figure 100002_DEST_PATH_IMAGE015
is the current speed; the adjusted course is as follows:
Figure 148398DEST_PATH_IMAGE016
wherein, in the step (A),
Figure 100002_DEST_PATH_IMAGE017
is the current course angle and is the current course angle,
Figure 571420DEST_PATH_IMAGE018
the course direction of the unmanned aerial vehicle is taken as the initial direction, the connecting line between the unmanned aerial vehicle and the opposite unmanned aerial vehicle is taken as the ending direction, and the included angle between the initial direction and the ending direction is formed.
Further: the specific steps of step S3 are:
s31, matching different virtual potential field functions for the heterogeneous unmanned aerial vehicle, so that potential field vectors correspondingly change according to the position and speed information of the heterogeneous unmanned aerial vehicle from the obstacle, and the obtained improved potential field vectors are as follows:
Figure 100002_DEST_PATH_IMAGE019
in the above-mentioned formula, the compound has the following structure,
Figure 538239DEST_PATH_IMAGE020
in order to improve the potential field vector of the type,ris the distance between the unmanned aerial vehicle and the obstacle,vin order to achieve the adjusted speed, the speed of the motor is adjusted,
Figure 100002_DEST_PATH_IMAGE021
and
Figure 637782DEST_PATH_IMAGE022
is composed ofvIn thatxAndythe component in the direction of the light beam,iandjnumbering heterogeneous unmanned aerial vehicles;
s32, introducing communication network topology and weight information of the heterogeneous unmanned aerial vehicle cluster networking into the potential field, wherein the introduced improved potential field is as follows:
the potential field between every two heterogeneous unmanned aerial vehicles is:
Figure 100002_DEST_PATH_IMAGE023
in the above formula, the first and second carbon atoms are,
Figure 57262DEST_PATH_IMAGE024
for heterogeneous unmanned aerial vehicleiAnd withjThe potential field in between the two electrodes,
Figure 100002_DEST_PATH_IMAGE025
for heterogeneous unmanned aerial vehicleiAnd withjThe difference in the relative position of the two or more,
Figure 286905DEST_PATH_IMAGE026
for heterogeneous unmanned aerial vehicleiAndjthe difference in the relative speeds between them,
Figure 100002_DEST_PATH_IMAGE027
、
Figure 108230DEST_PATH_IMAGE028
is the potential field adjusting coefficient of a large unmanned aerial vehicle,
Figure 100002_DEST_PATH_IMAGE029
、
Figure 644254DEST_PATH_IMAGE030
adjusting coefficients for the potential field of the small unmanned aerial vehicle;
s33, according to heterogeneous unmanned aerial vehicleiAnd withjCommunication topology weight a betweenijBuild unmanned aerial vehicleiAverage potential fields with other heterogeneous unmanned aerial vehicles;
Figure 100002_DEST_PATH_IMAGE031
in the above formula, the first and second carbon atoms are,v i for unmanned aerial vehiclesiWith other heterogeneous unmanned aerial vehiclesThe average potential field of the magnetic field between the two,N i is the sum of the communication topology weights of all nodes,jnumbering heterogeneous unmanned aerial vehicles;
s34, establishing a formation maintenance virtual potential field based on the heterogeneous formation datum point;
Figure 551030DEST_PATH_IMAGE032
in the above formula, the first and second carbon atoms are,
Figure 100002_DEST_PATH_IMAGE033
a virtual potential field is maintained for the formation,
Figure 315855DEST_PATH_IMAGE034
the virtual potential field gain factor is maintained for the formation,
Figure 100002_DEST_PATH_IMAGE035
the radius is perceived for the potential field of the drone,
Figure 257266DEST_PATH_IMAGE036
is the potential field sensing radius of a large unmanned plane,
Figure 100002_DEST_PATH_IMAGE037
is the potential field sensing radius of the small unmanned plane,
Figure 698612DEST_PATH_IMAGE038
for unmanned aerial vehiclesiThe arm length of the formation reference point of (a),
Figure 100002_DEST_PATH_IMAGE039
、
Figure 702471DEST_PATH_IMAGE040
are all a function of the sign of the signal,
Figure 100002_DEST_PATH_IMAGE041
the sensing radius between the unmanned aerial vehicle and the obstacle;
according to different structure unmanned aerial vehicleiAndjthe formation communication topological connection relation between the two, the formation formed by the formation reference points keeps the virtual potential field as follows:
Figure 395620DEST_PATH_IMAGE042
in the above-mentioned formula, the compound has the following structure,v kall a virtual potential field is maintained for the formation,ithe numbers of the heterogeneous unmanned aerial vehicles are shown,nthe number of formation reference points for the heterogeneous unmanned aerial vehicles;
s35, each unmanned aerial vehicle overlaps formation based on heterogeneous formation shape reference points according to the heterogeneous cluster improved dynamic potential field to keep the sum of virtual potential fields, meanwhile, the total potential field value of other members subjected to formation under the improved dynamic strengthening potential field is obtained by combining the cluster networking communication topology and the weight relation among the heterogeneous unmanned aerial vehicles, and the speed and the course are correspondingly adjusted according to the total potential field value through a consistency group topology control algorithm, so that the heterogeneous unmanned aerial vehicles complete formation construction according to the expected formation.
Further: said sign function
Figure 722696DEST_PATH_IMAGE039
、
Figure 741468DEST_PATH_IMAGE040
The values of (A) are as follows:
when in use
Figure 100002_DEST_PATH_IMAGE043
When the temperature of the water is higher than the set temperature,
Figure 481891DEST_PATH_IMAGE044
otherwise, otherwise
Figure 100002_DEST_PATH_IMAGE045
；
When the temperature is higher than the set temperature
Figure 588518DEST_PATH_IMAGE046
When the utility model is used, the water is discharged,
Figure 100002_DEST_PATH_IMAGE047
otherwise, otherwise
Figure 238943DEST_PATH_IMAGE048
。
The beneficial effects of the invention are as follows:
1. the existing research mainly focuses on topology control of small unmanned aerial vehicles and solves the problem of formation control, and does not focus on group topology control research of heterogeneous unmanned aerial vehicles (large/small unmanned aerial vehicle hybrid clusters). The invention comprehensively considers the large/small heterogeneous unmanned aerial vehicle cluster system to research the cluster cooperation obstacle avoidance cluster topology control method;
2. the unmanned aerial vehicle node adopts the Kalman filtering algorithm to predict the position track of other surrounding nodes, can acquire track information such as position, speed, course and the like at the next moment in the future in advance, considers the anti-collision safety radius of the self-body and other unmanned aerial vehicles, adopts the speed self-adaptive adjustment anti-collision algorithm based on collision risk factors and track prediction, performs self-adaptive adjustment of the speed and the course of the self-body in advance, and can effectively improve the anti-collision effect through advanced anti-collision treatment.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined by the appended claims, and all changes that can be made by the invention using the inventive concept are intended to be protected.
In a large/small heterogeneous unmanned aerial vehicle hybrid cluster networking system, network nodes jointly complete specified tasks through cluster network interactive service messages under a specific cluster topology. In order to better maintain the configuration and topological stability of unmanned aerial vehicle cluster formation and avoid collision among unmanned aerial vehicles, and to execute various formation tasks more safely and efficiently, group topology optimization control needs to be performed on heterogeneous unmanned aerial vehicle hybrid cluster formation.
As shown in fig. 1, an unmanned aerial vehicle cluster topology control and intelligent collision avoidance method based on heterogeneous formation datum points includes the following steps:
s1, the heterogeneous unmanned aerial vehicle nodes maintain information through an interactive network, information such as positions, speeds, courses and safe radiuses of surrounding heterogeneous nodes is obtained, and position tracks of the surrounding nodes and the nodes at the next moment are predicted through a track prediction algorithm;
and when receiving network maintenance messages sent by other surrounding nodes, the unmanned aerial vehicle node measures the arrival time of the messages, and predicts the position and the track of the surrounding nodes at the next moment through a Kalman filtering algorithm. And then according to the track prediction results (including information such as positions, speeds, courses and the like) of a plurality of surrounding unmanned aerial vehicles, combining the track prediction condition of the self-machine, considering the anti-collision safety radius of the self-machine and other unmanned aerial vehicles, and adopting a speed self-adaptive adjustment anti-collision algorithm based on collision risk factors and track prediction to self-adaptively adjust the speed and the courses of the self-machine, so that the self-adaptive anti-collision processing of the plurality of surrounding unmanned aerial vehicles is realized.
Predicting the position track of the surrounding nodes at the next moment: the unmanned aerial vehicle node receives network maintenance messages (containing information such as local node address, heterogeneous type, sending time, local position, speed, course, collision avoidance safety radius and the like) sent by other surrounding nodes, measures message arrival time, predicts the position and track of the surrounding nodes at the next moment through a Kalman filtering algorithm, predicts the position and track of the unmanned aerial vehicle node at the same time, and obtains the relative position, speed, safety distance and the like between the local machine and the surrounding unmanned aerial vehicles at the next moment according to all prediction results.
S2, according to the predicted position track, adopting a speed self-adaptive adjustment anti-collision algorithm based on collision risk factors and track prediction to complete intelligent anti-collision processing among the heterogeneous unmanned aerial vehicles;
resolving collision risk factors: the unmanned aerial vehicle calculates collision risk factors (with the value range of 0 to 1) corresponding to different unmanned aerial vehicle nodes around according to the track prediction result +9 (containing information such as position, speed and course) of a plurality of surrounding unmanned aerial vehicles and the track prediction condition of the unmanned aerial vehicle, and simultaneously considers the collision-proof safety radius of the unmanned aerial vehicle and other heterogeneous unmanned aerial vehicles, and reflects the collision risk degree (including the heavy risk value of 0.7-1, the light risk value of 0.3-0.7 and the safety of 0-0.3). The specific calculation method is as follows:
calculating according to the predicted positions, speeds, courses and safe radiuses of the two parties as input factors in the following mode:
order to
Figure 428615DEST_PATH_IMAGE001
In which
Figure 187493DEST_PATH_IMAGE002
In order to be a safe distance away from the vehicle,dis the relative distance, i.e. the difference between the position of the machine and the other unmanned aerial vehicles, r1Is the safe radius of the machine, r2Safe radiuses for other unmanned aerial vehicles;
when in use
Figure 488024DEST_PATH_IMAGE003
In the "safe" state, the collision risk factor
Figure 258534DEST_PATH_IMAGE004
Wherein, in the step (A),
Figure 619108DEST_PATH_IMAGE005
for safety factor, the value is 0.3;
when in use
Figure 613085DEST_PATH_IMAGE006
In the "light danger" state, the collision danger factor
Figure 186148DEST_PATH_IMAGE007
Wherein, in the step (A),
Figure 76744DEST_PATH_IMAGE008
the mild risk coefficient is 1, the value is 0.4,
Figure 732853DEST_PATH_IMAGE009
the mild risk coefficient is 2, and the value is 0.3;
when in use
Figure 76110DEST_PATH_IMAGE010
At the time of the collision, the state is a 'severe danger' state, and the collision danger factor is generated at the time
Figure 984023DEST_PATH_IMAGE011
Wherein, in the process,
Figure 729125DEST_PATH_IMAGE012
the serious danger coefficient is 1, the value is 0.3,
Figure 306868DEST_PATH_IMAGE013
the serious danger coefficient is 2, and the value is 0.7.
Self-adaptive adjustment anti-collision treatment: unmanned aerial vehicle carries out following processing to local speed, course according to collision danger factor, realizes independently intelligent anticollision to a plurality of unmanned aerial vehicles around handling:
when the collision risk factor value is in a 'safe' state, no modulation is needed;
when the collision risk factor value is in a state of light danger and severe danger, the speed and the course of the self-adaptive adjusting machine are adjusted; the adjusted speed is as follows:
Figure DEST_PATH_IMAGE049
wherein, in the step (A),
Figure 871842DEST_PATH_IMAGE050
is the current speed; the adjusted course is as follows:
Figure DEST_PATH_IMAGE051
wherein, in the step (A),
Figure 911342DEST_PATH_IMAGE052
is the current course angle and is the current course angle,
Figure DEST_PATH_IMAGE053
the course direction of the unmanned aerial vehicle is taken as the initial direction, the connecting line between the unmanned aerial vehicle and the opposite unmanned aerial vehicle is taken as the ending direction, and the included angle between the initial direction and the ending direction is formed.
And S3, on the basis of intelligent anti-collision processing, maintaining a virtual potential field by constructing an improved dynamic potential field and a formation based on heterogeneous formation datum points, and completing formation control among heterogeneous unmanned aerial vehicles by adopting a consistency group topology control algorithm of heterogeneous unmanned aerial vehicle cluster formation based on an improved dynamic strengthening potential field.
By constructing an improved dynamic potential field, different virtual potential field functions are adopted for matching processing aiming at the heterogeneous unmanned aerial vehicle, so that potential field vectors are changed correspondingly according to the position and speed information of the heterogeneous unmanned aerial vehicle from a barrier, the heterogeneous formation of a Z-direction dimension (vertical height direction) is increased, and information such as communication topology and weight of the heterogeneous unmanned aerial vehicle cluster networking is introduced into the potential field. On the basis of the dynamic potential field method, a formation maintenance virtual potential field based on heterogeneous formation datum points is established, so that each unmanned aerial vehicle has a tendency of maintaining the relative position of the unmanned aerial vehicle and surrounding heterogeneous unmanned aerial vehicles in the obstacle avoidance process; when a drone leaves its position in the formation queue, there is a tendency for the drone to move to that position. And the formation of the heterogeneous unmanned aerial vehicles is finished by adopting a consistency group topology control algorithm based on an improved dynamic strengthening potential field for heterogeneous unmanned aerial vehicle cluster formation. The specific treatment process comprises the following steps:
the specific steps of step S3 are:
s31, matching different virtual potential field functions for the heterogeneous unmanned aerial vehicle, and enabling the potential field vectors to correspondingly change according to the position and speed information of the heterogeneous unmanned aerial vehicle from the obstacle, so as to obtain improved potential field vectors as follows:
Figure 510951DEST_PATH_IMAGE019
in the above formula, the first and second carbon atoms are,
Figure 384229DEST_PATH_IMAGE020
in order to improve the potential field vector,ris the distance between the unmanned aerial vehicle and the obstacle,vin order to achieve the adjusted speed, the speed of the motor is adjusted,
Figure 577444DEST_PATH_IMAGE021
and
Figure 296001DEST_PATH_IMAGE022
is composed ofvIn thatxAndythe component in the direction of the light beam,iandjnumbering heterogeneous unmanned aerial vehicles;
s32, introducing communication network topology and weight information of the heterogeneous unmanned aerial vehicle cluster networking into the potential field, wherein the introduced improved potential field is as follows:
the potential field between every two heterogeneous unmanned aerial vehicles is:
Figure 15695DEST_PATH_IMAGE023
in the above formula, the first and second carbon atoms are,
Figure 184508DEST_PATH_IMAGE024
for heterogeneous unmanned aerial vehicleiAndjthe potential field in between the two,
Figure 989653DEST_PATH_IMAGE025
for heterogeneous unmanned aerial vehiclesiAnd withjThe difference in the relative position of the two or more,
Figure 777481DEST_PATH_IMAGE026
for heterogeneous unmanned aerial vehiclesiAndjthe difference in the relative speeds between them,
Figure 351682DEST_PATH_IMAGE027
、
Figure 442129DEST_PATH_IMAGE028
is the potential field adjusting coefficient of a large unmanned aerial vehicle,
Figure 468990DEST_PATH_IMAGE029
、
Figure 60509DEST_PATH_IMAGE030
adjusting coefficients for the potential field of the small unmanned aerial vehicle;
s33, according to heterogeneous unmanned aerial vehicleiAndjcommunication topology weight a betweenijTo construct an unmanned planeiThe average potential field with other heterogeneous unmanned aerial vehicles;
Figure 348271DEST_PATH_IMAGE031
in the above-mentioned formula, the compound has the following structure,v i for unmanned aerial vehiclesiThe average potential field with other heterogeneous drones,N i is the sum of the communication topology weights of all nodes,jnumbering the heterogeneous unmanned aerial vehicles;
s34, establishing a formation maintenance virtual potential field based on the heterogeneous formation datum point;
Figure 999832DEST_PATH_IMAGE032
in the above formula, the first and second carbon atoms are,
Figure 779569DEST_PATH_IMAGE033
a virtual potential field is maintained for the formation,
Figure 909199DEST_PATH_IMAGE034
the virtual potential field gain factor is maintained for the formation,
Figure 192413DEST_PATH_IMAGE035
the radius is perceived for the potential field of the drone,
Figure 615873DEST_PATH_IMAGE036
is the potential field sensing radius of a large unmanned plane,
Figure 617327DEST_PATH_IMAGE037
is the potential field sensing radius of the small unmanned plane,
Figure 285069DEST_PATH_IMAGE038
for unmanned aerial vehiclesiThe arm length of the formation reference point of (a),
Figure 813002DEST_PATH_IMAGE039
、
Figure 806366DEST_PATH_IMAGE040
are all a function of the sign of the signal,
Figure 29537DEST_PATH_IMAGE041
the sensing radius between the unmanned aerial vehicle and the obstacle;
said sign function
Figure 766549DEST_PATH_IMAGE039
、
Figure 899721DEST_PATH_IMAGE040
The values of (A) are as follows:
when the temperature is higher than the set temperature
Figure 798407DEST_PATH_IMAGE043
When the utility model is used, the water is discharged,
Figure 774453DEST_PATH_IMAGE044
otherwise, otherwise
Figure 908631DEST_PATH_IMAGE045
；
When the temperature is higher than the set temperature
Figure 20944DEST_PATH_IMAGE046
When the utility model is used, the water is discharged,
Figure 356110DEST_PATH_IMAGE047
otherwise, otherwise
Figure 553873DEST_PATH_IMAGE048
。
According to different structures unmanned aerial vehicleiAndjthe formation communication topological connection relation between the two, the formation formed by the formation reference points keeps the virtual potential field as follows:
Figure 508054DEST_PATH_IMAGE042
in the above formula, the first and second carbon atoms are,v kall a virtual potential field is maintained for the formation,ithe numbers of the heterogeneous unmanned aerial vehicles are shown,nthe number of formation reference points for the heterogeneous unmanned aerial vehicles;
s35, each unmanned aerial vehicle overlaps formation based on heterogeneous formation shape reference points according to the heterogeneous cluster improved dynamic potential field to keep the sum of virtual potential fields, meanwhile, the total potential field value of other members subjected to formation under the improved dynamic strengthening potential field is obtained by combining the cluster networking communication topology and the weight relation among the heterogeneous unmanned aerial vehicles, and the speed and the course are correspondingly adjusted according to the total potential field value through a consistency group topology control algorithm, so that the heterogeneous unmanned aerial vehicles complete formation construction according to the expected formation.

Claims (7)

1. An unmanned aerial vehicle cluster topological control and intelligent collision avoidance method based on heterogeneous formation datum points is characterized by comprising the following steps:
s1, the heterogeneous unmanned aerial vehicle nodes maintain information through an interactive network, the position, speed, course and safety radius information of the surrounding heterogeneous nodes are obtained, and the position tracks of the surrounding nodes and the next moment of the nodes are predicted through a track prediction algorithm;
s2, according to the predicted position track, adopting a speed self-adaptive adjustment anti-collision algorithm based on collision risk factors and track prediction to complete intelligent anti-collision processing among the heterogeneous unmanned aerial vehicles;
and S3, on the basis of intelligent anti-collision processing, maintaining a virtual potential field by constructing an improved dynamic potential field and a formation based on heterogeneous formation datum points, and completing formation control among heterogeneous unmanned aerial vehicles by adopting a consistency group topology control algorithm of heterogeneous unmanned aerial vehicle cluster formation based on an improved dynamic strengthening potential field.
2. The unmanned aerial vehicle cluster topological control and intelligent collision avoidance method based on the heterogeneous formation reference point according to claim 1, wherein the step S1 specifically comprises: the heterogeneous unmanned aerial vehicle node receives network maintenance messages sent by other surrounding nodes, measures the time of arrival of the messages, predicts the position and the track of the surrounding nodes at the next moment through a Kalman filtering algorithm, predicts the position and the track of the heterogeneous unmanned aerial vehicle node at the same time, and obtains the relative position, the speed and the safety distance between the heterogeneous unmanned aerial vehicle node and the surrounding unmanned aerial vehicles at the next moment according to all prediction results.
3. The unmanned aerial vehicle cluster topology control and intelligent collision avoidance method based on the heterogeneous formation reference points as claimed in claim 2, wherein the network maintenance message comprises local node address, heterogeneous type, sending time, local position, speed, course, collision avoidance safety radius information.
4. The unmanned aerial vehicle cluster topological control and intelligent collision avoidance method based on the heterogeneous formation reference point according to claim 1, wherein the collision risk factor is calculated by:
order to
Figure DEST_PATH_IMAGE001
In which
Figure 904399DEST_PATH_IMAGE002
In order to be a safe distance from the user,dis the relative distance, i.e. the difference between the position of the machine and the other unmanned aerial vehicles, r1Is the safe radius of the machine, r2Safe radiuses for other unmanned aerial vehicles;
when in use
Figure DEST_PATH_IMAGE003
At this time, the state is "safe", and the collision risk factor is at this time
Figure 469372DEST_PATH_IMAGE004
Wherein, in the step (A),
Figure DEST_PATH_IMAGE005
for safety factor, the value is 0.3;
when in use
Figure 525184DEST_PATH_IMAGE006
In the "light danger" state, the collision danger factor
Figure DEST_PATH_IMAGE007
Wherein, in the process,
Figure 718268DEST_PATH_IMAGE008
the mild risk coefficient is 1, the value is 0.4,
Figure DEST_PATH_IMAGE009
the mild risk coefficient is 2, and the value is 0.3;
when in use
Figure 325967DEST_PATH_IMAGE010
At the time of the collision, the state is a 'severe danger' state, and the collision danger factor is generated at the time
Figure DEST_PATH_IMAGE011
Wherein, in the step (A),
Figure 253603DEST_PATH_IMAGE012
the serious danger coefficient is 1, the value is 0.3,
Figure DEST_PATH_IMAGE013
the serious danger coefficient is 2, and the value is 0.7.
5. The unmanned aerial vehicle cluster topology control and intelligent collision avoidance method based on the heterogeneous formation reference points according to claim 4, wherein the adaptive adjustment collision avoidance algorithm specifically comprises:
when the value of the collision risk factor is in a 'safe' state, no modulation is needed;
when the collision risk factor value is in a state of light danger and severe danger, the speed and the course of the self-adaptive adjusting machine are adjusted; the adjusted speed is as follows:
Figure 706581DEST_PATH_IMAGE014
wherein, in the process,
Figure DEST_PATH_IMAGE015
is the current speed; the adjusted course is as follows:
Figure 285330DEST_PATH_IMAGE016
wherein, in the step (A),
Figure DEST_PATH_IMAGE017
the current course angle is the current course angle,
Figure 63930DEST_PATH_IMAGE018
the course direction of the unmanned aerial vehicle is taken as the initial direction, the connecting line between the unmanned aerial vehicle and the opposite unmanned aerial vehicle is taken as the ending direction, and the included angle between the initial direction and the ending direction is formed.
6. The unmanned aerial vehicle cluster topological control and intelligent collision avoidance method based on the heterogeneous formation reference points according to claim 1, wherein the specific steps of the step S3 are as follows:
s31, matching different virtual potential field functions for the heterogeneous unmanned aerial vehicle, so that potential field vectors correspondingly change according to the position and speed information of the heterogeneous unmanned aerial vehicle from the obstacle, and the obtained improved potential field vectors are as follows:
Figure DEST_PATH_IMAGE019
in the above-mentioned formula, the compound has the following structure,
Figure 470073DEST_PATH_IMAGE020
in order to improve the potential field vector,ris the distance between the unmanned aerial vehicle and the obstacle,vin order to achieve the adjusted speed, the speed of the motor is adjusted,
Figure DEST_PATH_IMAGE021
and
Figure 851375DEST_PATH_IMAGE022
is composed ofvIn thatxAndythe component in the direction of the light beam,iandjnumbering the heterogeneous unmanned aerial vehicles;
s32, introducing communication network topology and weight information of the heterogeneous unmanned aerial vehicle cluster networking into the potential field, wherein the introduced improved potential field is as follows:
the potential field between every two heterogeneous unmanned aerial vehicles is:
Figure DEST_PATH_IMAGE023
in the above formula, the first and second carbon atoms are,
Figure 159997DEST_PATH_IMAGE024
for heterogeneous unmanned aerial vehicleiAndjthe potential field in between the two electrodes,
Figure DEST_PATH_IMAGE025
for heterogeneous unmanned aerial vehicleiAndjthe difference in the relative position of the two or more,
Figure 984865DEST_PATH_IMAGE026
for heterogeneous unmanned aerial vehicleiAnd withjThe difference in the relative speed between the two,
Figure DEST_PATH_IMAGE027
、
Figure 746147DEST_PATH_IMAGE028
is the potential field adjusting coefficient of a large unmanned aerial vehicle,
Figure DEST_PATH_IMAGE029
、
Figure 931141DEST_PATH_IMAGE030
adjusting coefficients for the potential field of the small unmanned aerial vehicle;
s33, according to heterogeneous unmanned aerial vehicleiAnd withjCommunication topology weight a betweenijBuild unmanned aerial vehicleiAverage potential fields with other heterogeneous unmanned aerial vehicles;
Figure DEST_PATH_IMAGE031
in the above-mentioned formula, the compound has the following structure,v i for unmanned aerial vehiclesiThe average potential field with other heterogeneous drones,N i is the sum of the communication topology weights of all nodes,jnumbering the heterogeneous unmanned aerial vehicles;
s34, establishing a formation maintenance virtual potential field based on the heterogeneous formation datum point;
Figure 94269DEST_PATH_IMAGE032
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE033
a virtual potential field is maintained for the formation,
Figure 90038DEST_PATH_IMAGE034
the virtual potential field gain factor is maintained for the formation,
Figure DEST_PATH_IMAGE035
the radius is perceived for the potential field of the drone,
Figure 604196DEST_PATH_IMAGE036
is the potential field sensing radius of a large unmanned plane,
Figure DEST_PATH_IMAGE037
is the potential field sensing radius of the small unmanned plane,
Figure 327301DEST_PATH_IMAGE038
for unmanned aerial vehiclesiThe arm length of the formation reference point of (a),
Figure DEST_PATH_IMAGE039
、
Figure 954723DEST_PATH_IMAGE040
are all a function of the sign of the signal,
Figure DEST_PATH_IMAGE041
the sensing radius between the unmanned aerial vehicle and the obstacle;
according to different structures unmanned aerial vehicleiAnd withjThe formation communication topological connection relation is that the formation formed by the formation reference points keeps the virtual potential field as follows:
Figure 511606DEST_PATH_IMAGE042
in the above formula, the first and second carbon atoms are,v kall a virtual potential field is maintained for the formation,ithe numbers of the heterogeneous unmanned aerial vehicles are shown,nthe number of formation reference points for the heterogeneous unmanned aerial vehicles;
s35, each unmanned aerial vehicle superposes formation based on heterogeneous formation datum points according to the heterogeneous cluster improved dynamic potential field to keep the sum of virtual potential fields, meanwhile, the total potential field value of other members of the unmanned aerial vehicle subjected to formation under the improved dynamic strengthening potential field is obtained by combining the cluster networking communication topology and weight relation among the heterogeneous unmanned aerial vehicles, and the speed and the course are correspondingly adjusted according to the total potential field value through a consistency group topology control algorithm, so that the heterogeneous unmanned aerial vehicles complete formation construction according to the expected formation.
7. The heterogeneous formation reference point-based unmanned aerial vehicle cluster topology control and intelligent collision avoidance method according to claim 6, wherein the symbolic function is
Figure 637694DEST_PATH_IMAGE039
、
Figure 571015DEST_PATH_IMAGE040
The values of (A) are as follows:
when in use
Figure DEST_PATH_IMAGE043
When the utility model is used, the water is discharged,
Figure 443156DEST_PATH_IMAGE044
otherwise
Figure DEST_PATH_IMAGE045
；
When the temperature is higher than the set temperature
Figure 777798DEST_PATH_IMAGE046
When the temperature of the water is higher than the set temperature,
Figure DEST_PATH_IMAGE047
otherwise
Figure 125603DEST_PATH_IMAGE048
。
CN202210709158.0A 2022-06-22 2022-06-22 Unmanned aerial vehicle cluster topological control and intelligent anti-collision method based on heterogeneous formation datum points Active CN114779828B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210709158.0A CN114779828B (en) 2022-06-22 2022-06-22 Unmanned aerial vehicle cluster topological control and intelligent anti-collision method based on heterogeneous formation datum points

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210709158.0A CN114779828B (en) 2022-06-22 2022-06-22 Unmanned aerial vehicle cluster topological control and intelligent anti-collision method based on heterogeneous formation datum points

Publications (2)

Publication Number Publication Date
CN114779828A true CN114779828A (en) 2022-07-22
CN114779828B CN114779828B (en) 2022-09-16

Family

ID=82422481

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210709158.0A Active CN114779828B (en) 2022-06-22 2022-06-22 Unmanned aerial vehicle cluster topological control and intelligent anti-collision method based on heterogeneous formation datum points

Country Status (1)

Country Link
CN (1) CN114779828B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116437372A (en) * 2023-06-13 2023-07-14 四川腾盾科技有限公司 Group topology control method for unconstrained random recombination of arbitrary task configuration
CN118102225A (en) * 2024-04-23 2024-05-28 四川腾盾科技有限公司 Unmanned aerial vehicle cluster navigation and topology control method based on distributed relative positioning

Citations (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102541070A (en) * 2012-01-20 2012-07-04 哈尔滨工业大学 Collision avoiding method for ground testing system of satellite formation flying
WO2014197026A2 (en) * 2013-03-08 2014-12-11 Colorado Seminary, Which Owns And Operates The University Of Denver Frequency shift keyed continuous wave radar
US20150116155A1 (en) * 2013-10-25 2015-04-30 The Charles Stark Draper Laboratory, Inc. Methods and systems for self-aligning high data rate communication networks
US20150284010A1 (en) * 2013-09-16 2015-10-08 Disney Enterprises, Inc. Shared control of semi-autonomous vehicles including collision avoidance in multi-agent scenarios
US20160209849A1 (en) * 2015-01-15 2016-07-21 William Dale Arbogast System and method for decentralized, multi-agent unmanned vehicle navigation and formation control
CN105867415A (en) * 2016-04-20 2016-08-17 沈阳航空航天大学 Cooperative control policy based on secure communication of multiple unmanned aerial vehicles
CN107102650A (en) * 2017-05-27 2017-08-29 河南科技大学 A kind of unmanned plane dynamic path planning method suitable for high velocity environment
CN107491086A (en) * 2017-08-03 2017-12-19 哈尔滨工业大学深圳研究生院 Unmanned plane formation obstacle avoidance and system under time-varying network topology
CN108459612A (en) * 2017-02-21 2018-08-28 北京航空航天大学 Unmanned plane formation control method based on Artificial Potential Field 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
CN109445459A (en) * 2018-10-25 2019-03-08 中国人民解放军战略支援部队航天工程大学 Unmanned plane cluster formation control method and its device
US20190180634A1 (en) * 2017-12-08 2019-06-13 Kitty Hawk Corporation Aircraft movement limits for safe flight
CN109901387A (en) * 2017-12-11 2019-06-18 上海航空电器有限公司 A kind of automatic near-earth anti-collision system Self-adaptive flight trajectory predictions method of aircraft
CN110244756A (en) * 2019-04-29 2019-09-17 福州大学 Unmanned plane fast track collaborative obstacle avoidance method
CN111553562A (en) * 2020-04-07 2020-08-18 哈尔滨工程大学 Ship collision risk degree estimation method based on improved BP neural network
CN111580545A (en) * 2020-03-25 2020-08-25 北京航空航天大学 Heterogeneous unmanned aerial vehicle cluster distributed space-time coordination control method
CN112684807A (en) * 2019-10-18 2021-04-20 成都凯天电子股份有限公司 Unmanned aerial vehicle cluster three-dimensional formation method
CN113157000A (en) * 2021-05-06 2021-07-23 西北工业大学 Flight formation cooperative obstacle avoidance self-adaptive control method based on virtual structure and artificial potential field
CN113238583A (en) * 2021-07-14 2021-08-10 四川腾盾科技有限公司 Intensive formation flying and anti-collision control method for fixed-wing unmanned aerial vehicles

Patent Citations (19)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102541070A (en) * 2012-01-20 2012-07-04 哈尔滨工业大学 Collision avoiding method for ground testing system of satellite formation flying
WO2014197026A2 (en) * 2013-03-08 2014-12-11 Colorado Seminary, Which Owns And Operates The University Of Denver Frequency shift keyed continuous wave radar
US20150284010A1 (en) * 2013-09-16 2015-10-08 Disney Enterprises, Inc. Shared control of semi-autonomous vehicles including collision avoidance in multi-agent scenarios
US20150116155A1 (en) * 2013-10-25 2015-04-30 The Charles Stark Draper Laboratory, Inc. Methods and systems for self-aligning high data rate communication networks
US20160209849A1 (en) * 2015-01-15 2016-07-21 William Dale Arbogast System and method for decentralized, multi-agent unmanned vehicle navigation and formation control
CN105867415A (en) * 2016-04-20 2016-08-17 沈阳航空航天大学 Cooperative control policy based on secure communication of multiple unmanned aerial vehicles
CN108459612A (en) * 2017-02-21 2018-08-28 北京航空航天大学 Unmanned plane formation control method based on Artificial Potential Field Method and device
CN107102650A (en) * 2017-05-27 2017-08-29 河南科技大学 A kind of unmanned plane dynamic path planning method suitable for high velocity environment
CN107491086A (en) * 2017-08-03 2017-12-19 哈尔滨工业大学深圳研究生院 Unmanned plane formation obstacle avoidance and system under time-varying network topology
US20190180634A1 (en) * 2017-12-08 2019-06-13 Kitty Hawk Corporation Aircraft movement limits for safe flight
CN109901387A (en) * 2017-12-11 2019-06-18 上海航空电器有限公司 A kind of automatic near-earth anti-collision system Self-adaptive flight trajectory predictions method of aircraft
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
CN109445459A (en) * 2018-10-25 2019-03-08 中国人民解放军战略支援部队航天工程大学 Unmanned plane cluster formation control method and its device
CN110244756A (en) * 2019-04-29 2019-09-17 福州大学 Unmanned plane fast track collaborative obstacle avoidance method
CN112684807A (en) * 2019-10-18 2021-04-20 成都凯天电子股份有限公司 Unmanned aerial vehicle cluster three-dimensional formation method
CN111580545A (en) * 2020-03-25 2020-08-25 北京航空航天大学 Heterogeneous unmanned aerial vehicle cluster distributed space-time coordination control method
CN111553562A (en) * 2020-04-07 2020-08-18 哈尔滨工程大学 Ship collision risk degree estimation method based on improved BP neural network
CN113157000A (en) * 2021-05-06 2021-07-23 西北工业大学 Flight formation cooperative obstacle avoidance self-adaptive control method based on virtual structure and artificial potential field
CN113238583A (en) * 2021-07-14 2021-08-10 四川腾盾科技有限公司 Intensive formation flying and anti-collision control method for fixed-wing unmanned aerial vehicles

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
KESHMIRI, S等: "Flight Test Validation of Collision and Obstacle Avoidance in Fixed-Wing UASs with High Speeds Using Morphing Potential Field", 《INTERNATIONAL CONFERENCE ON UNMANNED AIRCRAFT SYSTEMS》 *
SHIRLEY WANG等: "Fly-Crash-Recover: A Sensor-based Reactive Framework for Online Collision Recovery of UAVs", 《2020 SYSTEMS AND INFORMATION ENGINEERING DESIGN SYMPOSIUM》 *
贾向东等: "大规模无人机协助的多层异构网络设计及性能研究", 《电子与信息学报》 *
黄洋: "面向无人机集群的防撞方法研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116437372A (en) * 2023-06-13 2023-07-14 四川腾盾科技有限公司 Group topology control method for unconstrained random recombination of arbitrary task configuration
CN116437372B (en) * 2023-06-13 2023-08-29 四川腾盾科技有限公司 Group topology control method for unconstrained random recombination of arbitrary task configuration
CN118102225A (en) * 2024-04-23 2024-05-28 四川腾盾科技有限公司 Unmanned aerial vehicle cluster navigation and topology control method based on distributed relative positioning

Also Published As

Publication number Publication date
CN114779828B (en) 2022-09-16

Similar Documents

Publication Publication Date Title
CN114779828B (en) Unmanned aerial vehicle cluster topological control and intelligent anti-collision method based on heterogeneous formation datum points
CN110650039B (en) Multimodal optimization-based network cooperative communication model for unmanned aerial vehicle cluster auxiliary vehicle
CN107491086B (en) Unmanned aerial vehicle formation obstacle avoidance method and system under time-varying network topology
CN114779827B (en) Virtual potential field collaborative obstacle avoidance topological control method based on heterogeneous unmanned aerial vehicle formation
CN107426694B (en) Fuzzy clustering method for vehicle-mounted self-organizing network
CN110347181B (en) Energy consumption-based distributed formation control method for unmanned aerial vehicles
Syed et al. Coordinated vehicle platoon control: Weighted and constrained consensus and communication network topologies
CN110991972A (en) Cargo transportation system based on multi-agent reinforcement learning
CN110782650B (en) Traffic flow distributed cooperative formation control method based on self-adaptive event triggering
CN110703803A (en) Unmanned aerial vehicle group flight control method, unmanned aerial vehicle, system and medium
CN111142567B (en) Unmanned aerial vehicle target position exchange method and device in unmanned aerial vehicle system
CN111290429A (en) Unmanned aerial vehicle formation and obstacle avoidance control method thereof based on consistency algorithm and artificial potential field method
CN113552899B (en) Multi-unmanned-aerial-vehicle autonomous collaborative search method under communication link limitation
CN110865655B (en) Formation and obstacle avoidance control method for unmanned aerial vehicle in unmanned aerial vehicle system
CN111103881A (en) Multi-agent formation anti-collision control method and system
Sun et al. Cooperative strategy for pursuit-evasion problem in the presence of static and dynamic obstacles
CN117412267B (en) Communication method of unmanned aerial vehicle cluster network
CN112564766A (en) Unmanned aerial vehicle network communication restoration method
Kawakami et al. Virtual structure based target-enclosing strategies for nonholonomic agents
CN114637301B (en) Multi-robot dynamic obstacle avoidance device and method based on optimal affine formation transformation
CN111596688A (en) Multi-unmanned aerial vehicle formation consistency control method based on continuous communication
CN114545971A (en) Multi-agent distributed flyable path planning method, system, computer equipment and medium under communication constraint
Han Driving into future with reliable, secure, efficient and intelligent metavehicles
CN111240365A (en) Unmanned aerial vehicle formation self-adaptive actuator fault compensation method with designated performance
CN110737281A (en) distributed control method facing cluster unmanned aerial vehicle

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
GR01 Patent grant
GR01 Patent grant
PE01 Entry into force of the registration of the contract for pledge of patent right
PE01 Entry into force of the registration of the contract for pledge of patent right

Denomination of invention: UAV cluster topology control and intelligent anti-collision method based on heterogeneous formation benchmarks

Effective date of registration: 20230116

Granted publication date: 20220916

Pledgee: China Minsheng Banking Corp Chengdu branch

Pledgor: SICHUAN TENGDUN TECHNOLOGY Co.,Ltd.

Registration number: Y2023510000024