CN114488784B - Method and device for resolving human-machine decision conflict - Google Patents
Method and device for resolving human-machine decision conflict Download PDFInfo
- Publication number
- CN114488784B CN114488784B CN202011167216.9A CN202011167216A CN114488784B CN 114488784 B CN114488784 B CN 114488784B CN 202011167216 A CN202011167216 A CN 202011167216A CN 114488784 B CN114488784 B CN 114488784B
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- vehicle cluster
- follower
- guidance law
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000004138 cluster model Methods 0.000 claims abstract description 81
- 238000004891 communication Methods 0.000 claims abstract description 54
- 239000011159 matrix material Substances 0.000 claims abstract description 27
- 230000001133 acceleration Effects 0.000 claims description 21
- 238000005070 sampling Methods 0.000 claims description 13
- 230000036039 immunity Effects 0.000 claims description 5
- 238000013461 design Methods 0.000 description 10
- 230000009286 beneficial effect Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000006073 displacement reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005251 gamma ray Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
-
- 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
- G05D1/0088—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
-
- 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
-
- 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
- G05D1/106—Change initiated in response to external conditions, e.g. avoidance of elevated terrain or of no-fly zones
- G05D1/1064—Change initiated in response to external conditions, e.g. avoidance of elevated terrain or of no-fly zones specially adapted for avoiding collisions with other aircraft
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Software Systems (AREA)
- Medical Informatics (AREA)
- Remote Sensing (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Game Theory and Decision Science (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a method and a device for resolving human-machine decision conflicts, belongs to the technical field of unmanned aerial vehicles, and solves the problem that an existing human instruction conflicts with an instruction being executed by the unmanned aerial vehicle. The method comprises the steps of generating a machine decision rule based on a consistency protocol of the unmanned aerial vehicle cluster, and inputting a generated first relative state variable and a first communication topology matrix as system variables into an unmanned aerial vehicle cluster model; generating a human decision rule by improving the machine decision rule, and inputting the generated second relative state change and the second communication topology as disturbance into the unmanned aerial vehicle cluster model; judging whether the system variable and disturbance in the unmanned aerial vehicle cluster model can cause conflict; and when the conflict is caused, correcting the unmanned aerial vehicle cluster guidance law by using the anti-rule interference observer and feeding the corrected unmanned aerial vehicle cluster guidance law back to the unmanned aerial vehicle cluster model and the consistency protocol. And resolving conflict between the instruction of the person and the instruction being executed by the unmanned aerial vehicle.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a method and a device for resolving man-machine decision conflicts.
Background
Unmanned aerial vehicles, i.e., unmanned aerial vehicles (UAV, unmanned Aerial Vehicle), are unmanned aerial vehicles that are operated using radio equipment and on-board programming. With the continuous development of unmanned aerial vehicle technology, unmanned aerial vehicles play an increasingly important role in both civil and military fields. Compared with the unmanned plane, the unmanned plane has the characteristics of low cost, small volume, strong survivability and the like, and the characteristics lead the unmanned plane to have wide prospects in the aspects of emergency rescue, data acquisition, information investigation and the like.
Because the task being executed by the unmanned aerial vehicle contains relative position constraint inside the cluster, the relative communication distance between the unmanned aerial vehicles required by the communication network construction cannot be changed rapidly and frequently, so that the instructions of people collide with the instructions being executed by the unmanned aerial vehicle, the unmanned aerial vehicle cluster is unstable, various accidents are easy to occur, and for example, the contradiction of the instructions causes the collision of members inside the unmanned aerial vehicle cluster.
Disclosure of Invention
In view of the above analysis, the embodiment of the invention aims to provide a method and a device for resolving man-machine decision conflicts, which are used for solving the problem that the instructions of people are in conflict with the instructions being executed by an unmanned aerial vehicle due to the constraint of the relative positions inside clusters in the task being executed by the existing unmanned aerial vehicle.
In one aspect, an embodiment of the present invention provides a method for resolving a man-machine decision conflict, including: generating a machine decision rule based on a consistency protocol of an unmanned aerial vehicle group, and inputting a first relative state variable and a first communication topology matrix, which are generated by the machine decision rule and are to be executed by the unmanned aerial vehicle, into an unmanned aerial vehicle cluster model as system variables, wherein the machine decision rule comprises an unmanned aerial vehicle cluster guidance law; generating a human decision rule by improving the machine decision rule, and inputting a second relative state change and a second communication topology, which are generated by the human decision rule and are to be executed by the unmanned aerial vehicle, into the unmanned aerial vehicle cluster model as disturbance; judging whether the system variable and the disturbance in the unmanned aerial vehicle cluster model can cause conflict or not; and when the conflict is caused, correcting the unmanned aerial vehicle cluster guidance law by using an anti-rule interference observer and feeding the corrected unmanned aerial vehicle cluster guidance law back to the unmanned aerial vehicle cluster model and the consistency protocol.
The beneficial effects of the technical scheme are as follows: when the conflict can be caused by system variables and disturbance in the unmanned aerial vehicle cluster model, the anti-rule interference observer is utilized to correct the unmanned aerial vehicle cluster guidance law and feed the corrected unmanned aerial vehicle cluster guidance law back to the unmanned aerial vehicle cluster model and the consistency protocol, so that the conflict between the instruction of the person and the instruction being executed by the unmanned aerial vehicle due to the constraint of the relative position inside the cluster in the task being executed by the unmanned aerial vehicle can be resolved.
Based on a further improvement of the method, correcting the unmanned aerial vehicle cluster guidance law by using the anti-regular interference observer further comprises: inputting a human decision output result and a machine decision output result into the anti-rule interference observer; the anti-regular-interference observer simulates a change item of the unmanned aerial vehicle cluster model after receiving the disturbance; and using the change term to assist in correcting the unmanned aerial vehicle cluster guidance law to counteract the effect of the disturbance on the unmanned aerial vehicle cluster model.
The beneficial effects of the technical scheme are as follows: the anti-regular-interference observer simulates a change item after the unmanned aerial vehicle cluster model receives disturbance, and corrects the unmanned aerial vehicle cluster guidance law in an auxiliary mode by using the change item so as to counteract the influence of the disturbance on the unmanned aerial vehicle cluster model.
Based on a further improvement of the above method, the immunity observer z is expressed by the following formula:
wherein g is the unmanned aerial vehicle cluster guidance law of the machine decision rule,unmanned aerial vehicle cluster guidance law sigma for the human decision rule g The guidance law threshold value in the time delay delta t between every two adjacent sampling times is sigma g Δg is->Is a sign () function.
Based on the further improvement of the method, the method for resolving the human-computer decision conflict further comprises the following steps: when the conflict is not caused, improving unmanned aerial vehicle cluster guidance law by utilizing a human decision output result; and feeding back the improved unmanned aerial vehicle cluster guidance law to the unmanned aerial vehicle cluster model and the consistency protocol.
Based on a further development of the above method, the guidance law threshold is determined as σg by the following formula:
Δg＝|g(t k+1 )-g(t k )|≤σ g ，
wherein g (t) k+1 ) And g (t) k ) Time t of kth sample point respectively k A guidance law threshold at a time with the (k+1) th sampling point.
Based on a further improvement of the method, the consistency protocol of the unmanned aerial vehicle group is min V (x, A, g) (t), wherein x is a relative state variable x= [ r, lambda, V r ，V λ ]Where r is the relative distance between follower and leader, λ is the angle of view, V r Is the relative velocity along the line of sight between the follower and the leader, V λ Is the relative velocity between the follower and the leader perpendicular to the line of sight; a is a communication topology matrix a= [ a ] ij ]Wherein a is ij Is the ith row and the jth column element of the communication topology matrix A; g is guidance law g= [ A ] Fr ，A Fλ ]Wherein A is Fr For the component of the follower unmanned aerial vehicle acceleration along the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle, and A Fλ The component of the acceleration of the follower unmanned aerial vehicle perpendicular to the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle.
Based on a further improvement of the above method, the unmanned aerial vehicle cluster model further comprises: an unmanned aerial vehicle group dynamics model, an unmanned aerial vehicle group kinematics model and an unmanned aerial vehicle group communication topology matrix.
Based on the upper partA further improvement of the method, the unmanned aerial vehicle cluster model being F (x, A, g) (t), the state variable threshold sigma within the time delay delta t between every two adjacent sampling times being defined by the following formula F ：
ΔF＝|F(x，A，g)(t k+1 )-F(x，A，g)(t k )|≤σ F ，
Wherein x is a relative state variable x= [ r, λ, V r ，V λ ]Where r is the relative distance between follower and leader, λ is the angle of view, vr is the relative speed between the follower and the leader along the direction of view, V λ Is the relative velocity between the follower and the leader perpendicular to the line of sight; a is a communication topology matrix a= [ a ] ij ]Wherein a is ij Is the ith row and the jth column element of the communication topology matrix A; g is guidance law g= [ A ] Fr ，A Fλ ]Wherein A is Fr For the component of the follower unmanned aerial vehicle acceleration along the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle, and A Fλ The component of the acceleration of the follower unmanned aerial vehicle perpendicular to the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle.
In another aspect, an embodiment of the present invention provides a resolution apparatus for man-machine decision conflict, including: the consistency protocol module is used for generating a consistency protocol of the unmanned aerial vehicle group; the system comprises a machine decision rule and a system variable generation module, wherein the machine decision rule is generated based on a consistency protocol, and a first relative state variable and a first communication topology matrix which are generated by the machine decision rule and are executed by an unmanned aerial vehicle are used as system variables, and the machine decision rule comprises an unmanned aerial vehicle cluster guidance law; the human decision rule and disturbance generation module is used for receiving the machine decision rule, generating a human decision rule by improving the machine decision rule, and taking a second relative state change and a second communication topology to be executed by the unmanned aerial vehicle generated by the human decision rule as disturbance; the unmanned aerial vehicle cluster model module is used for receiving the system variable and the disturbance and inputting the unmanned aerial vehicle cluster model; the conflict judging module is used for judging whether the system variable and the disturbance in the unmanned aerial vehicle cluster model can cause conflict or not; and the anti-regular interference observer is used for correcting the unmanned aerial vehicle cluster guidance law and feeding the corrected unmanned aerial vehicle cluster guidance law back to the unmanned aerial vehicle cluster model module and the consistency protocol module when the conflict is caused.
Based on the further improvement of the device, the resolution device of the human-computer decision conflict further comprises: the human decision output module is connected with the conflict judging module and provides a human decision output result for the anti-rule interference observer; the machine decision output module is connected with the conflict judging module and provides a machine decision output result for the anti-rule interference observer; and the anti-regular-interference observer simulates a change item after the unmanned aerial vehicle cluster model receives the disturbance, and utilizes the change item to assist in correcting the unmanned aerial vehicle cluster guidance law so as to offset the influence of the disturbance on the unmanned aerial vehicle cluster model.
Compared with the prior art, the invention has at least one of the following beneficial effects:
1. when the conflict can be caused by system variables and disturbance in the unmanned aerial vehicle cluster model, the anti-rule interference observer is utilized to correct the unmanned aerial vehicle cluster guidance law and feed the corrected unmanned aerial vehicle cluster guidance law back to the unmanned aerial vehicle cluster model and the consistency protocol, so that the conflict between the instruction of the person and the instruction being executed by the unmanned aerial vehicle due to the constraint of the relative position inside the cluster in the task being executed by the unmanned aerial vehicle can be resolved.
2. The anti-regular-interference observer simulates a change item after the unmanned aerial vehicle cluster model receives disturbance, and corrects the unmanned aerial vehicle cluster guidance law in an auxiliary mode by using the change item so as to counteract the influence of the disturbance on the unmanned aerial vehicle cluster model.
3. The unmanned aerial vehicle cluster can cooperatively adjust the motion state and the relative position through all members in the unmanned aerial vehicle cluster in the allowed time delay, so that the conflict-free connection of the instruction being executed by the unmanned aerial vehicle and the new human instruction is achieved, the unmanned aerial vehicle task execution system is prevented from being unstable due to strong disturbance, and the unmanned aerial vehicle task execution system can be more suitable for complex task demands.
In the invention, the technical schemes can be mutually combined to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, like reference numerals being used to refer to like parts throughout the several views.
FIG. 1 is a flow chart of a method for resolving human-machine decision conflicts in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of an overall design method of a human-machine decision conflict resolution method according to an embodiment of the present invention;
FIG. 3 is a flowchart of a specific example of a human decision conflict resolution method; and
FIG. 4 is a block diagram of a resolution apparatus for man-machine decision conflicts in accordance with an embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and together with the description serve to explain the principles of the invention, and are not intended to limit the scope of the invention.
The invention discloses a resolution method of human-machine decision conflicts. Referring to fig. 1, a method for resolving human-machine decision conflicts includes: step S102, a machine decision rule is generated based on a consistency protocol of an unmanned aerial vehicle group, and a first relative state variable and a first communication topology matrix, which are generated by the machine decision rule and are to be executed by the unmanned aerial vehicle, are used as system variables to be input into an unmanned aerial vehicle cluster model, wherein the machine decision rule comprises an unmanned aerial vehicle cluster guidance law; step S104, generating a human decision rule by improving a machine decision rule, and inputting a second relative state change and a second communication topology, which are generated by the human decision rule and are to be executed by the unmanned aerial vehicle, into an unmanned aerial vehicle cluster model as disturbance; step S106, judging whether the system variable and disturbance in the unmanned aerial vehicle cluster model can cause conflict; and step S108, when collision is caused, correcting the unmanned aerial vehicle cluster guidance law by using the anti-rule interference observer and feeding the corrected unmanned aerial vehicle cluster guidance law back to the unmanned aerial vehicle cluster model and the consistency protocol.
Compared with the prior art, the method for resolving the human-computer decision conflict, provided by the embodiment, has the advantages that when the conflict is caused by the system variable and disturbance in the unmanned aerial vehicle cluster model, the anti-rule interference observer is utilized to correct the unmanned aerial vehicle cluster guidance law, the corrected unmanned aerial vehicle cluster guidance law is fed back to the unmanned aerial vehicle cluster model and the consistency protocol, and the conflict between the instruction of the person and the instruction being executed by the unmanned aerial vehicle due to the constraint of the relative position inside the cluster in the task being executed by the unmanned aerial vehicle can be resolved.
Hereinafter, a method for resolving human-machine decision conflicts will be described in detail with reference to fig. 1 and 3.
First, referring to fig. 1, the method for resolving human-machine decision conflicts includes: step S102, a machine decision rule is generated based on a consistency protocol of the unmanned aerial vehicle group, and a first relative state variable and a first communication topology matrix, which are generated by the machine decision rule and are to be executed by the unmanned aerial vehicle, are used as system variables to be input into an unmanned aerial vehicle cluster model, wherein the machine decision rule comprises an unmanned aerial vehicle cluster guidance law. Specifically, the consistency protocol of the unmanned aerial vehicle group is min V (x, A, g) (t),
wherein x is a relative state variable x= [ r, λ, V r ，V λ ]Where r is the relative distance between follower and leader, λ is the angle of view, V r Is the relative speed along the direction of the line of sight between the follower and the leader, V λ Is the relative velocity between the follower and the leader perpendicular to the line of sight;
a is a communication topology matrix a= [ a ] ij ]Wherein a is ij Is the ith row and the jth column element of the communication topology matrix A;
g is guidance law g= [ A ] Fr ，A Fλ ]Wherein A is Fr Unmanned aerial vehicle for followerComponent of acceleration along the direction of the follower and leader drones, and a Fλ The component of the acceleration of the follower unmanned aerial vehicle perpendicular to the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle.
Step S104, generating a human decision rule by improving the machine decision rule, and inputting a second relative state change and a second communication topology, which are generated by the human decision rule and are to be executed by the unmanned aerial vehicle, into the unmanned aerial vehicle cluster model as disturbance. In particular, the method comprises the steps of,as relative state variable
Wherein (1)>For the relative distance between follower-leader, +.>Is the angle of sight>Is the relative speed between follower and leader along the direction of line of sight, +.>Is the relative velocity between the follower and the leader perpendicular to the line of sight;
for communication topology matrix->Wherein (1)>Is a communication topology matrix->The ith row, the jth column element;
for guidance law->Wherein (1)>For the component of the acceleration of the follower unmanned aerial vehicle along the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle, and +.>The component of the acceleration of the follower unmanned aerial vehicle perpendicular to the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle.
And S106, judging whether the system variables and the disturbance in the unmanned aerial vehicle cluster model can cause conflict. Specifically, the unmanned aerial vehicle cluster model further includes: an unmanned aerial vehicle group dynamics model, an unmanned aerial vehicle group kinematics model and an unmanned aerial vehicle group communication topology matrix. The unmanned aerial vehicle cluster model is F (x, A, g) (t), and the state variable threshold sigma within the time delay delta t between every two adjacent sampling times is defined by the following formula F ：
ΔF＝|F(x，A，g)(t k+1 )-F(x，A，g)(t k )|≤σ F ，
Wherein x is a relative state variable x= [ r, λ, V r ，V λ ]Where r is the relative distance between follower and leader, λ is the angle of view, V r Is the relative speed along the direction of the line of sight between the follower and the leader, V λ Is the relative velocity between the follower and the leader perpendicular to the line of sight;
a is a communication topology matrix a= [ a ] ij ]Wherein a is ij Is the ith row and the jth column element of the communication topology matrix A;
g is guidance law g= [ A ] Fr ，A Fλ ]Wherein A is Fr For the component of the follower unmanned aerial vehicle acceleration along the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle, and A Fλ The component of the acceleration of the follower unmanned aerial vehicle perpendicular to the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle.
And S108, when collision is caused, correcting the unmanned aerial vehicle cluster guidance law by using the anti-rule interference observer and feeding the corrected unmanned aerial vehicle cluster guidance law back to the unmanned aerial vehicle cluster model and the consistency protocol. Specifically, correcting the unmanned aerial vehicle cluster guidance law using the anti-regular interference observer further includes: inputting the human decision output result and the machine decision output result into an anti-rule interference observer; the anti-rule interference observer simulates a change item of the unmanned aerial vehicle cluster model after receiving disturbance; and correcting the unmanned aerial vehicle cluster guidance law by using the change item in an auxiliary way so as to counteract the influence of disturbance on the unmanned aerial vehicle cluster model. The immunity observer z is expressed by the following formula:
wherein g is unmanned aerial vehicle cluster guidance law of machine decision rule,unmanned aerial vehicle cluster guidance law sigma for human decision rule g The guidance law threshold value in the time delay delta t between every two adjacent sampling times is sigma g Δg is->Is a sign () function. Determining the guidance law threshold value as sigma by the following formula g ：
Δg＝|g(t k+1 )-g(t k )|≤σ g ，
Wherein g (t) k+1 ) And g (t) k ) Time t of kth sample point respectively k A guidance law threshold at a time with the (k+1) th sampling point.
Compared with the prior art, the method for resolving the human-computer decision conflict provided by the embodiment has the advantages that the anti-rule interference observer simulates the change item after the unmanned aerial vehicle cluster model receives the disturbance, and the change item is used for assisting in correcting the unmanned aerial vehicle cluster guidance law so as to offset the influence of the disturbance on the unmanned aerial vehicle cluster model.
In addition, when no conflict is caused, the human decision output result is utilized to improve the unmanned aerial vehicle cluster guidance law; and feeding the improved unmanned aerial vehicle cluster guidance law back to the unmanned aerial vehicle cluster model and the consistency protocol.
Aiming at the problem that instructions of people conflict with instructions being executed by an unmanned aerial vehicle, the method designs a consistency protocol of cluster anti-rule disturbance based on conflict resolution purposes, adds an anti-rule disturbance observer in a control law of the unmanned aerial vehicle, and plays roles in fine adjustment and correction on the control law (also called guidance law) of the unmanned aerial vehicle, so that the unmanned aerial vehicle cluster can cooperatively adjust a motion state and a relative position through all members in the interior in an allowable time delay (namely, a time delay upper limit can be set in the consistency protocol design and is approximately a multiple of a time interval between 2 time sampling points). Specifically, the anti-regular disturbance observer is to enable the motion state and the position of the unmanned aerial vehicle cluster to be close to the motion state and the position of the unmanned aerial vehicle cluster required by the human new instruction in the time delay upper limit time, so that the human new instruction can be realized, the conflict-free connection of the instruction being executed by the unmanned aerial vehicle and the human new instruction is achieved, and the unmanned aerial vehicle task execution system is prevented from being unstable due to strong disturbance.
Hereinafter, a method for resolving a man-machine decision conflict will be described in detail by way of specific example with reference to fig. 2 and 3.
The overall design method of the man-machine decision conflict resolution technology comprises the following specific steps:
and S1, generating a machine strategy by the consistency protocol.
Referring to fig. 2, a computer performs a consistency protocol design of an unmanned aerial vehicle group, and takes an unmanned aerial vehicle guidance law and a motion policy corresponding to the unmanned aerial vehicle guidance law generated by the consistency protocol design as a machine decision rule. The machine decision rule is the guidance law and the corresponding motion parameters. And generating relative state variables and communication topology to be executed by the unmanned aerial vehicle, taking the relative state variables and the communication topology generated by the machine decision rule as system variables, inputting the system variables into an unmanned aerial vehicle cluster model, and designing an unmanned aerial vehicle cluster control law by utilizing the relative state variables and the communication topology generated by the machine decision rule. The updated relative state variables of the members in the cluster and the updated communication topology parameters are generated through a cluster model (kinematics, mechanics and communication topology).
S2, inputting model disturbance by a human strategy.
After the human selects and improves the machine decision rule, the human decision rule is generated, and the human decision rule, namely the guidance law formulated by human and the corresponding motion parameters thereof, is hoped to be executed by the unmanned plane. The human decision rule generates relative state variables and communication topology to be executed by the unmanned aerial vehicle, and takes the relative state variables and communication topology generated by the human decision rule as disturbance to input the unmanned aerial vehicle cluster model.
The unmanned cluster model includes unmanned cluster kinematics and unmanned cluster communication topology. The unmanned aerial vehicle cluster generates machine decision rules by designing a consistency protocol, and takes the rules as candidate rules for operators to choose and improve, so as to generate human decision rules, wherein the human decision rules cause disturbance of relative state variables and communication topology connection links in the task being executed by the unmanned aerial vehicle cluster. Meanwhile, the machine decision rule designs a cluster guidance law, changes relative state variables and communication topology connection links, and enters an unmanned aerial vehicle cluster model (kinematics, dynamics and communication topology) together with disturbance caused by the human decision rule to obtain a human decision output result and a machine decision output result.
Unmanned aerial vehicle cluster kinematics model:
γ M ＝θ F +λ，
γ T ＝θ L +λ，
wherein, subscripts F and L represent follower unmanned aerial vehicle and leader unmanned aerial vehicle, respectively. Herein bold represents vectors. r is the relative distance between follower and leader, V F And V L Representing the speeds of the follower and the leader, respectively, where V L To construct the formula, a vector form is written. Lambda is the angle of view. Gamma ray F And gamma L Included angle θ between the speed direction of follower and leader and X-axis, respectively F And theta L The speed direction of the follower and leader, respectively, is at an angle to the Line-of-sight (LOS). a, a F And a L Acceleration of the follower and the leader, respectively.
The relative dynamics model can be written as follows:
wherein V is r ＝V L cosθ L -V F cosθ F ，V λ ＝V L sinθ L -V F sinθ F ，V r Is the relative speed along the direction of the line of sight between the follower and the leader, V λ Is the relative velocity between the follower and the leader perpendicular to the line of sight. A is that Lr Is the acceleration component of the leader along the line of sight, A Lλ Is the acceleration component of the leader perpendicular to the line of sight, A Fr Is the acceleration component of the follower along the sight line direction, A Fλ The acceleration component of the follower perpendicular to the direction of the line of sight is noted as the guidance laws along the direction of the line of sight and perpendicular to the direction of the line of sight, which are expressed as:
where subscript i represents the ith follower (i=1, n.), a ij Is the ith row, the jth column element and the K of the communication topology matrix A 1 ，K 2 ，k 1 ρ, μεR is the adaptive parameter, K 3 ，k 2 Is a positive constant, c 1 ，c 2 ，c 3 ∈R N Is preset of r, V r ，V λ When this desired state is reached, the formation is completed (see fig. 3).
Referring to fig. 3, the consistency protocol is:
min V(x，A，g)(t)
wherein, the guidance law is:
g＝[A Fr ，A Fλ ]
the relative state variables are:
x＝[r，λ，V r ，V λ ]
the communication topology matrix is as follows:
A＝[a ij ]
the time sampling points are sampled for M times, and each time interval is as follows:
Δt＝t k+1 -t k ，k＝0，1，2，…，M-1，
the cluster model is F (x, A, g) (t) and exists in the following iterative formula
(x，A，g)(t k+1 )＝F(x，A，g)(t k )，
The state variable threshold within the time delay delta t is sigma F ：
ΔF＝|F(x，A，g)(t k+1 )-F(x，A，g)(t k )|≤σ F ，
The guidance law threshold within the time delay delta t is sigma g ：
Δg＝|g(t k+1 )-g(t k )|≤σ g 。
The above variables are all obtained by machine decision, and the artificial decision variables are all added with upper drawn lines to obtain
And S3, judging whether the conflict exists or not and resolving the conflict.
And judging whether the system variables and the disturbance in the unmanned aerial vehicle cluster model cause conflict. If the human-computer decision conflicts, the human decision output result and the machine decision output result are reflected in errors, namely, the relative position variable or the communication topology change of the human decision output cannot be completed by the unmanned aerial vehicle cluster within a certain time, so that the errors exceed a threshold value, and at the moment, the two results are input into an anti-rule interference observer. The anti-rule interference observer simulates a change term after the cluster model is disturbed (wherein the change term is a difference value of a human decision output result and a machine decision output result), and corrects the cluster control law by using a negative value of the change term to offset the influence of disturbance on the cluster model. The corrected cluster control law is fed back to the cluster model, and the obtained relative motion state and communication topology information are fed back to the consistency protocol design module to form a closed loop for man-machine conflict resolution, so that the man-machine decision conflict result can be resolved.
Referring to fig. 3, the immunity observer is z:
wherein sign (-) is a sign function, i.e.)
If the human-machine decision does not conflict, for example, the relative position variable or the communication topology change of the human decision output can be completed by the unmanned aerial vehicle cluster within a certain time, namely, the error does not exceed the threshold value, and the cluster control law is improved by utilizing the human decision output result. Specifically, the threshold is a maximum value at which the unmanned plane motion state and the communication topology parameters can change within the upper limit of the time delay, such as a maximum value of displacement change within a certain time, and the like.
And S4, feeding back a conflict resolution result to form a closed loop.
And the corrected or improved unmanned aerial vehicle cluster control law feeds back the unmanned aerial vehicle cluster model to generate an output result and feeds back the output result to the consistency protocol design module so as to update the consistency protocol.
The utility model provides a human-computer decision conflict resolution technology overall design method to solve unmanned aerial vehicle and carry out the inside relative position constraint of cluster and lead to the instruction of people and unmanned aerial vehicle to carry out the instruction conflict problem that carries out in the task, make unmanned aerial vehicle cluster can be through inside all members cooperation regulation motion state and relative position in the time delay of permission, reach unmanned aerial vehicle and carry out the conflict-free linking of instruction and human new instruction, thereby avoid unmanned aerial vehicle task execution system to be unstable because of receiving strong disturbance, more can adapt to complicated task demand.
In another embodiment of the invention, a resolution device for man-machine decision conflicts is disclosed. Referring to fig. 4, the resolution device for man-machine decision conflict comprises: a consistency protocol module 402, a machine decision rule and system variable generation module 404, a human decision rule and disturbance generation module 406, an unmanned aerial vehicle cluster model module 408, a collision judgment module 410, and an anti-rule interference observer 412.
In an embodiment, the coherence protocol module 402 is configured to generate a coherence protocol for a group of drones. The machine decision rule and system variable generation module 404 is configured to generate a machine decision rule based on a consistency protocol, and take a first relative state variable and a first communication topology matrix to be executed by the unmanned aerial vehicle generated by the machine decision rule as system variables, where the machine decision rule includes an unmanned aerial vehicle cluster guidance law. The human decision rule and disturbance generation module 406 is configured to receive the machine decision rule and generate a human decision rule by modifying the machine decision rule, and take a second relative state change and a second communication topology to be executed by the unmanned aerial vehicle generated by the human decision rule as a disturbance; the unmanned cluster model module 408 is configured to receive system variables and disturbances and input the unmanned cluster model. The conflict determination module 410 is configured to determine whether a conflict is caused by a system variable and a disturbance in the unmanned aerial vehicle cluster model. The anti-rule interference observer 412 is configured to correct the unmanned aerial vehicle cluster guidance law and feed back the corrected unmanned aerial vehicle cluster guidance law to the unmanned aerial vehicle cluster model module and the consistency protocol module when the collision is caused.
In addition, the resolution device of the human-computer decision conflict comprises: a human decision output module 414 and a machine decision output module 416. Specifically, the human decision output module 414 is connected to the collision determination module and provides the human decision output result to the anti-rule interference observer. The machine decision output module 416 is coupled to the conflict determination module and provides machine decision output results to the anti-rule interference observer. The anti-regular-interference observer 412 simulates the change term after the unmanned aerial vehicle cluster model receives the disturbance, and utilizes the change term to assist in correcting the unmanned aerial vehicle cluster guidance law to counteract the influence of the disturbance on the unmanned aerial vehicle cluster model.
Compared with the prior art, the invention has at least one of the following beneficial effects:
1. when the conflict can be caused by system variables and disturbance in the unmanned aerial vehicle cluster model, the anti-rule interference observer is utilized to correct the unmanned aerial vehicle cluster guidance law and feed the corrected unmanned aerial vehicle cluster guidance law back to the unmanned aerial vehicle cluster model and the consistency protocol, so that the conflict between the instruction of the person and the instruction being executed by the unmanned aerial vehicle due to the constraint of the relative position inside the cluster in the task being executed by the unmanned aerial vehicle can be resolved.
2. The anti-regular-interference observer simulates a change item after the unmanned aerial vehicle cluster model receives disturbance, and corrects the unmanned aerial vehicle cluster guidance law in an auxiliary mode by using the change item so as to counteract the influence of the disturbance on the unmanned aerial vehicle cluster model.
3. The unmanned aerial vehicle cluster can cooperatively adjust the motion state and the relative position through all members in the unmanned aerial vehicle cluster in the allowed time delay, so that the conflict-free connection of the instruction being executed by the unmanned aerial vehicle and the new human instruction is achieved, the unmanned aerial vehicle task execution system is prevented from being unstable due to strong disturbance, and the unmanned aerial vehicle task execution system can be more suitable for complex task demands.
Those skilled in the art will appreciate that all or part of the flow of the methods of the embodiments described above may be accomplished by way of a computer program to instruct associated hardware, where the program may be stored on a computer readable storage medium. Wherein the computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory, etc.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
Claims (5)
1. The method for resolving the human-machine decision conflict is characterized by comprising the following steps of:
generating a machine decision rule based on a consistency protocol of an unmanned aerial vehicle group, and inputting a first relative state variable and a first communication topology matrix, which are generated by the machine decision rule and are to be executed by the unmanned aerial vehicle, into an unmanned aerial vehicle cluster model as system variables, wherein the machine decision rule comprises an unmanned aerial vehicle cluster guidance law;
generating a human decision rule by improving the machine decision rule, and inputting a second relative state change and a second communication topology, which are generated by the human decision rule and are to be executed by the unmanned aerial vehicle, into the unmanned aerial vehicle cluster model as disturbance;
judging whether the system variable and the disturbance in the unmanned aerial vehicle cluster model can cause conflict or not; and
when the conflict is caused, correcting the unmanned aerial vehicle cluster guidance law by using an anti-rule interference observer and feeding the corrected unmanned aerial vehicle cluster guidance law back to the unmanned aerial vehicle cluster model and the consistency protocol;
wherein correcting the unmanned aerial vehicle cluster guidance law by using the anti-regular interference observer further comprises: inputting a human decision output result and a machine decision output result into the anti-rule interference observer;
the anti-regular-interference observer simulates a change item of the unmanned aerial vehicle cluster model after receiving the disturbance; and
using the change item to assist in correcting the unmanned aerial vehicle cluster guidance law to counteract the influence of the disturbance on the unmanned aerial vehicle cluster model;
the immunity observer z is expressed by the following formula:
wherein g is the unmanned aerial vehicle cluster guidance law of the machine decision rule,unmanned aerial vehicle cluster guidance law sigma for the human decision rule g The guidance law threshold value in the time delay delta t between every two adjacent sampling times is sigma g Δg is->Is sign ()A number function;
determining the guidance law threshold value as sigma by the following formula g ：
Δg＝|g(t k+1 )-g(t k )|≤σ g ，
Wherein g (t) k+1 ) And g (t) k ) Time t of kth sample point respectively k A guidance law threshold at a time with the (k+1) th sampling point;
the consistency protocol of the unmanned aerial vehicle group is minV (x, a, g) (t),
wherein x is a relative state variable x= [ r, λ, V r ,V λ ]Where r is the relative distance between follower and leader, λ is the angle of view, V r Is the relative velocity along the line of sight between the follower and the leader, V λ Is the relative velocity between the follower and the leader perpendicular to the line of sight;
a is a communication topology matrix a= [ a ] ij ]Wherein a is ij Is the ith row and the jth column element of the communication topology matrix A; and
g is guidance law g= [ A ] Fr ,A Fλ ]Wherein A is Fr For the component of the follower unmanned aerial vehicle acceleration along the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle, and A Fλ The component of the acceleration of the follower unmanned aerial vehicle perpendicular to the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle.
2. The method for resolving human-machine decision conflicts according to claim 1, further comprising:
when the conflict is not caused, improving unmanned aerial vehicle cluster guidance law by utilizing a human decision output result; and
and feeding the improved unmanned aerial vehicle cluster guidance law back to the unmanned aerial vehicle cluster model and the consistency protocol.
3. The method of claim 1, wherein the unmanned aerial vehicle cluster model further comprises: an unmanned aerial vehicle group dynamics model, an unmanned aerial vehicle group kinematics model and an unmanned aerial vehicle group communication topology matrix.
4. The method for resolving human-machine decision conflicts according to claim 1, characterized in that,
the unmanned aerial vehicle cluster model is F (x, a, g) (t), and the state variable threshold sigma within the time delay delta t between every two adjacent sampling times is defined by the following formula F ：
ΔF＝|F(x,A,g)(t k+1 )-F(x,a,g)(t k )|≤σ F ，
Wherein x is a relative state variable x= [ r, λ, V r ,V λ ]Where r is the relative distance between follower and leader, λ is the angle of view, V r Is the relative velocity along the line of sight between the follower and the leader, V λ Is the relative velocity between the follower and the leader perpendicular to the line of sight;
a is a communication topology matrix a= [ a ] ij ]Wherein a is ij Is the ith row and the jth column element of the communication topology matrix A; and
g is guidance law g= [ A ] Fr ,A Fλ ]Wherein A is Fr For the component of the follower unmanned aerial vehicle acceleration along the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle, and A Fλ The component of the acceleration of the follower unmanned aerial vehicle perpendicular to the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle.
5. A human-machine decision conflict resolution device, comprising:
the consistency protocol module is used for generating a consistency protocol of the unmanned aerial vehicle group;
the system comprises a machine decision rule and a system variable generation module, wherein the machine decision rule is generated based on a consistency protocol, and a first relative state variable and a first communication topology matrix which are generated by the machine decision rule and are executed by an unmanned aerial vehicle are used as system variables, and the machine decision rule comprises an unmanned aerial vehicle cluster guidance law;
the human decision rule and disturbance generation module is used for receiving the machine decision rule, generating a human decision rule by improving the machine decision rule, and taking a second relative state change and a second communication topology to be executed by the unmanned aerial vehicle generated by the human decision rule as disturbance;
the unmanned aerial vehicle cluster model module is used for receiving the system variable and the disturbance and inputting the unmanned aerial vehicle cluster model;
the conflict judging module is used for judging whether the system variable and the disturbance in the unmanned aerial vehicle cluster model can cause conflict or not;
the anti-regular interference observer is used for correcting the unmanned aerial vehicle cluster guidance law and feeding the corrected unmanned aerial vehicle cluster guidance law back to the unmanned aerial vehicle cluster model module and the consistency protocol module when the conflict is caused;
the human decision output module is connected with the conflict judging module and provides a human decision output result for the anti-rule interference observer;
the machine decision output module is connected with the conflict judging module and provides a machine decision output result for the anti-rule interference observer; and
the anti-regular interference observer simulates a change item of the unmanned aerial vehicle cluster model after receiving the disturbance, and utilizes the change item to assist in correcting the unmanned aerial vehicle cluster guidance law to counteract the influence of the disturbance on the unmanned aerial vehicle cluster model,
the immunity observer z is expressed by the following formula:
wherein g is the unmanned aerial vehicle cluster guidance law of the machine decision rule,unmanned aerial vehicle cluster guidance law sigma for the human decision rule g For a time delay delta t between every two adjacent sampling timesGuidance law threshold value sigma g Δg is->Is a sign ();
determining the guidance law threshold value as sigma by the following formula g ：
Δg＝|g(t k+1 )-g(t k )|≤σ g ，
Wherein g (t) k+1 ) And g (t) k ) Time t of kth sample point respectively k A guidance law threshold at a time with the (k+1) th sampling point;
the consistency protocol of the unmanned aerial vehicle group is min V (x, A, g) (t),
wherein x is a relative state variable x= [ r, λ, V r ,V λ ]Where r is the relative distance between follower and leader, λ is the angle of view, V r Is the relative velocity along the line of sight between the follower and the leader, V λ Is the relative velocity between the follower and the leader perpendicular to the line of sight;
a is a communication topology matrix a= [ a ] ij ]Wherein a is ij Is the ith row and the jth column element of the communication topology matrix A; and
g is guidance law g= [ A ] Fr ,A Fλ ]Wherein A is Fλ For the component of the follower unmanned aerial vehicle acceleration along the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle, and A Fλ The component of the acceleration of the follower unmanned aerial vehicle perpendicular to the connecting line direction of the follower unmanned aerial vehicle and the leader unmanned aerial vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011167216.9A CN114488784B (en) | 2020-10-26 | 2020-10-26 | Method and device for resolving human-machine decision conflict |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011167216.9A CN114488784B (en) | 2020-10-26 | 2020-10-26 | Method and device for resolving human-machine decision conflict |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114488784A CN114488784A (en) | 2022-05-13 |
| CN114488784B true CN114488784B (en) | 2024-03-22 |
Family
ID=81490214
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011167216.9A Active CN114488784B (en) | 2020-10-26 | 2020-10-26 | Method and device for resolving human-machine decision conflict |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114488784B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117930871B (en) * | 2024-03-21 | 2024-05-28 | 中国民用航空飞行学院 | Rotor flow unmanned aerial vehicle group real-time conflict resolution method |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106647810A (en) * | 2017-01-10 | 2017-05-10 | 山东科技大学 | UAV automatic collision avoidance method based on negative-proportion guiding |
| US9671789B1 (en) * | 2016-05-10 | 2017-06-06 | Beihang University | Method and system for anti-disturbance composite on-line guidance for atmosphere-entering phase of a Mars lander |
| CA3045181A1 (en) * | 2016-11-29 | 2018-06-07 | Guangzhou Xaircraft Technology Co., Ltd | Unmanned aerial vehicle control method and unmanned aerial vehicle control apparatus |
| CN110262555A (en) * | 2019-07-12 | 2019-09-20 | 南京航空航天大学 | Unmanned plane Real Time Obstacle Avoiding control method under continuous obstacle environment |
| CN110703804A (en) * | 2019-11-11 | 2020-01-17 | 中国人民解放军国防科技大学 | A layered anti-collision control method for fixed-wing UAV swarms |
| CN110852486A (en) * | 2019-10-16 | 2020-02-28 | 中国人民解放军国防科技大学 | A mission planning method for autonomous coordination of UAV swarms |
| CN111160631A (en) * | 2019-12-17 | 2020-05-15 | 西北工业大学 | A Conflict Detection and Resolution Method Based on 4D Track Operation |
| CN111290429A (en) * | 2019-12-16 | 2020-06-16 | 南京信息工程大学 | Unmanned aerial vehicle formation and obstacle avoidance control method thereof based on consistency algorithm and artificial potential field method |
| CN111522361A (en) * | 2020-05-27 | 2020-08-11 | 北京理工大学 | Multi-UAV formation consistency control method in master-slave mode |
| CN111580556A (en) * | 2020-05-22 | 2020-08-25 | 中国人民解放军国防科技大学 | Multi-unmanned aerial vehicle collaborative path planning and guidance method under space-time constraint |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7343232B2 (en) * | 2003-06-20 | 2008-03-11 | Geneva Aerospace | Vehicle control system including related methods and components |
| US8195345B2 (en) * | 2010-08-05 | 2012-06-05 | King Fahd University Of Petroleum & Minerals | Method of generating an integrated fuzzy-based guidance law for aerodynamic missiles |
-
2020
- 2020-10-26 CN CN202011167216.9A patent/CN114488784B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9671789B1 (en) * | 2016-05-10 | 2017-06-06 | Beihang University | Method and system for anti-disturbance composite on-line guidance for atmosphere-entering phase of a Mars lander |
| CA3045181A1 (en) * | 2016-11-29 | 2018-06-07 | Guangzhou Xaircraft Technology Co., Ltd | Unmanned aerial vehicle control method and unmanned aerial vehicle control apparatus |
| CN106647810A (en) * | 2017-01-10 | 2017-05-10 | 山东科技大学 | UAV automatic collision avoidance method based on negative-proportion guiding |
| CN110262555A (en) * | 2019-07-12 | 2019-09-20 | 南京航空航天大学 | Unmanned plane Real Time Obstacle Avoiding control method under continuous obstacle environment |
| CN110852486A (en) * | 2019-10-16 | 2020-02-28 | 中国人民解放军国防科技大学 | A mission planning method for autonomous coordination of UAV swarms |
| CN110703804A (en) * | 2019-11-11 | 2020-01-17 | 中国人民解放军国防科技大学 | A layered anti-collision control method for fixed-wing UAV swarms |
| CN111290429A (en) * | 2019-12-16 | 2020-06-16 | 南京信息工程大学 | Unmanned aerial vehicle formation and obstacle avoidance control method thereof based on consistency algorithm and artificial potential field method |
| CN111160631A (en) * | 2019-12-17 | 2020-05-15 | 西北工业大学 | A Conflict Detection and Resolution Method Based on 4D Track Operation |
| CN111580556A (en) * | 2020-05-22 | 2020-08-25 | 中国人民解放军国防科技大学 | Multi-unmanned aerial vehicle collaborative path planning and guidance method under space-time constraint |
| CN111522361A (en) * | 2020-05-27 | 2020-08-11 | 北京理工大学 | Multi-UAV formation consistency control method in master-slave mode |
Non-Patent Citations (3)
| Title |
|---|
| Zhang Ning.an active disturbance rejection control guidance law based collision avoidance for unmanned aerial vehicles.aerospace science and technology.2018,第77卷第658-669页. * |
| 张佳为 ; 许诺 ; 伍少雄 ; .欠驱动航天器飞轮控制方法.宇航学报.2016,(05),第553-561页. * |
| 魏扬，等.无人机自主编队制导律设计.飞行力学.2016,第34卷(第2期),第37-41页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114488784A (en) | 2022-05-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Li et al. | Adaptive backstepping-based flight control system using integral filters | |
| Liu et al. | Robust trajectory tracking control of marine surface vessels with uncertain disturbances and input saturations | |
| CN111781942B (en) | Fault-tolerant flight control method based on self-constructed fuzzy neural network | |
| CN108717265A (en) | A kind of unmanned vehicle cruise tracking control system and control method based on control variable parameter | |
| CN114488784B (en) | Method and device for resolving human-machine decision conflict | |
| CN107193210A (en) | A kind of adaptive learning default capabilities control method of nonlinear system | |
| Cheng et al. | Fixed-time and prescribed-time fault-tolerant optimal tracking control for heterogeneous multiagent systems | |
| CN117289709A (en) | High-ultrasonic-speed appearance-changing aircraft attitude control method based on deep reinforcement learning | |
| CN118024250A (en) | Humanoid robot control method based on high-order full-drive theory | |
| CN115718425B (en) | A virtual train formation control method, system, electronic equipment and medium | |
| CN114253288B (en) | Multi-spacecraft orbit distributed collaborative tracking optimal control method | |
| CN111045447A (en) | High-precision hypersonic vehicle trajectory optimization multi-scale optimal control system | |
| Liao et al. | Distributed fast F‐T control for UAV formation in the presence of unknown input disturbances | |
| CN118151527A (en) | Unmanned plane-unmanned ship heterogeneous cooperative obstacle avoidance formation control method based on preset performance control | |
| CN115793446B (en) | A quantitative preset performance control method for waverider aircraft | |
| Li et al. | Novel fuzzy approximation control scheme for flexible air-breathing hypersonic vehicles with non-affine dynamics and amplitude and rate constraints | |
| Xu et al. | Robust adaptive command-filtered backstepping synchronization navigation control with prescribed performance for supply ship during underway replenishment | |
| CN118192643B (en) | Unmanned aerial vehicle cluster flight method based on large-scale data model | |
| Wang et al. | Adaptive fixed‐time prescribed performance control of flexible‐joint manipulator with unmodeled dynamics and dead‐zone | |
| Zhao et al. | Event-triggered cooperative adaptive optimal output regulation for multiagent systems under switching network: an adaptive dynamic programming approach | |
| CN112987758A (en) | Multi-water-surface aircraft cooperative tracking formation control method | |
| Tomashevich et al. | High-order adaptive control in multi-agent quadrotor formation. | |
| Lu et al. | Distributed Cooperative Guidance Model-Free Control for a Cluster of Disk-type Autonomous Underwater Gliders | |
| He et al. | Optimal sensor placement for flexible wings using the greedy algorithm | |
| CN111026140B (en) | Constraint full-process satisfied hypersonic aircraft trajectory optimization self-adaptive optimal controller |
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 |