CN117539290B - Processing method for damaged outer-line-of-sight cluster unmanned aerial vehicle - Google Patents
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Abstract
The invention discloses a processing method for a damaged unmanned aerial vehicle of an out-of-sight cluster. The method is suitable for the cluster unmanned aerial vehicle adopting the distributed formation control mode, when the unmanned aerial vehicle detects that the unmanned aerial vehicle has faults, if the unmanned aerial vehicle still keeps the communication capability, the current position coordinates and the fault information are respectively sent to adjacent unmanned aerial vehicles in the cluster, then a task rescheduling request is sent to other healthy unmanned aerial vehicles in the cluster, and the original task process is terminated; if the adjacent unmanned aerial vehicle cannot be in communication link with the fault unmanned aerial vehicle within the time threshold, judging that the adjacent unmanned aerial vehicle completely loses the communication capability and is in a damaged state, and sending a task rescheduling request to other healthy unmanned aerial vehicles in the cluster by the adjacent unmanned aerial vehicle of the fault unmanned aerial vehicle. The invention can automatically trigger task re-planning behavior, can ensure that the original task targets are all completed, ensures that all task targets are successfully completed, and simultaneously ensures that the distribution result is as reasonable as possible and the final income is maximized.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle control, in particular to a processing method for a damaged unmanned aerial vehicle of a video outer cluster.
Background
Along with the progress of artificial intelligence technology, unmanned aerial vehicle field development also is the moon, and unmanned aerial vehicle uses more and more extensively in military and civilian field at present, but single unmanned aerial vehicle has single structure, the limited shortcoming of function at present, in order to make unmanned aerial vehicle function better, need adopt many unmanned aerial vehicle formation flight control to realize tasks such as collaborative investigation, agriculture and forestry plant protection, city survey, urban traffic, police security protection, electric power inspection, waters commodity circulation and forest fire control. The task often needs the unmanned aerial vehicle cluster with the cooperation of multiple unmanned aerial vehicles to autonomously complete the operation under the condition of beyond-line-of-sight, which leads to the possibility that unmanned aerial vehicles in the cluster fail outside the line-of-sight and are damaged. In order to ensure normal operation of the other unmanned aerial vehicles in the clusters except damage, a decentralization full-distributed formation control method is needed, but the method also has the problem that frequent information interaction and controller updating are needed among the unmanned aerial vehicles, and in order to treat the damaged unmanned aerial vehicles in the outer-line-of-sight clusters, the situation of the damaged unmanned aerial vehicles is needed to be acquired through a remote real-time image return means of the other unmanned aerial vehicles in the clusters, so that the communication load capacity in the task process is further increased. Therefore, the energy loss in links such as communication and position calculation in the unmanned aerial vehicle cluster cooperative task process is effectively reduced, and the problems of long-distance limited bandwidth and large communication traffic, and the problems of damage processing method and guarantee of the unmanned aerial vehicle outside the sight distance cluster become the problems to be solved urgently.
The patent mainly broadcasts own data resources to other unmanned aerial vehicles through each unmanned aerial vehicle in the cluster, so that the unmanned aerial vehicles share the data resources of the other unmanned aerial vehicles in the neighborhood. However, since each unmanned aerial vehicle in the cluster needs to broadcast and only the unmanned aerial vehicles in the shared neighborhood receive broadcast resources, the data traffic among the unmanned aerial vehicles is too huge, and the actual communication use requirement of the multi-unmanned aerial vehicle cluster outside the long-distance vision distance cannot be met.
According to the unmanned aerial vehicle cluster-oriented safety level prediction method and system disclosed by the publication No. CN111487991A, the method and system mainly calculate the performance decay orbit of each unmanned aerial vehicle individual in the cluster based on the unmanned aerial vehicle individual, calculate the cumulative failure probability prediction value according to the decay orbit of each unmanned aerial vehicle individual, and finally subscribe the prediction values of all the individual unmanned aerial vehicles by a pilot and calculate the safety level of the unmanned aerial vehicle cluster. However, the method needs to subscribe the unmanned aerial vehicle in the cluster with the cumulative failure probability prediction value, the communication traffic is large, the actual use requirement of the unmanned aerial vehicles in the cluster for a long distance cannot be met, and the method sets the unmanned aerial vehicle in the cluster and does not meet the condition of decentralizing full-distributed unmanned aerial vehicle cluster formation implementation.
The patent discloses a security level prediction method and a security level prediction system for an unmanned aerial vehicle cluster, wherein the security level prediction method and the security level prediction system are mainly used for designing a state estimator and a distributed security control protocol by establishing a network attack model, a system fault model and an unmanned aerial vehicle cluster model, and finally calculating the tolerable attack/fault range of the cluster system. However, the method only considers the fault range which can be tolerated by the cluster, and does not consider the processing method under the condition that the unmanned aerial vehicle in the cluster is damaged.
Disclosure of Invention
The invention aims to provide a processing method for a damaged unmanned aerial vehicle of an out-of-sight cluster, aiming at the defects in the prior art.
In order to achieve the above purpose, the present invention provides a method for processing a damaged outer-line-of-sight cluster unmanned aerial vehicle, which comprises:
when the unmanned aerial vehicle in the cluster is controlled based on the distributed formation control mode and faults occur, if the unmanned aerial vehicle with faults still keeps the communication capability, the current position coordinates and fault information are respectively sent to unmanned aerial vehicles with the closest distances in the cluster, then task re-planning requests are sent to other healthy unmanned aerial vehicles in the cluster, the original task process is terminated, and the current position coordinates and the fault information are sent to a remote control center through communication links of the other healthy unmanned aerial vehicles; if the failed unmanned aerial vehicle completely loses the communication capacity, after the failed unmanned aerial vehicle is disconnected from the adjacent unmanned aerial vehicle, the unmanned aerial vehicle which is closest to the position coordinates in the last communication data of the failed unmanned aerial vehicle sends a communication request, and if the failed unmanned aerial vehicle responds to the communication request, the failed unmanned aerial vehicle still keeps the communication capacity for processing; if the failed unmanned aerial vehicle does not respond to the communication request, judging that the failed unmanned aerial vehicle is in a damaged state, returning position coordinates and fault information before the failure by the adjacent unmanned aerial vehicle of the failed unmanned aerial vehicle, sending task rescheduling requests to other healthy unmanned aerial vehicles in the cluster, and terminating the original task process by the unmanned aerial vehicle in the damaged state;
after receiving the task re-planning request, other healthy unmanned aerial vehicles in the cluster perform task re-planning to continue executing tasks, wherein the task re-planning comprises:
initializing task information to be executed;
performing value division on task targets to be executed according to preset task priority to obtain low-value targets, medium-value targets and high-value targets, and calculating target profit coefficients of the tasks to be executed according to the target value, wherein the method comprises the following steps of:
wherein a is a low-value target profit coefficient, the weight multiple of the unmanned aerial vehicle is 1, b is a medium-value target profit coefficient, the weight multiple of the unmanned aerial vehicle is 2, c is a high-value target profit coefficient, the weight multiple of the unmanned aerial vehicle is 3, n 1 For low value target quantity, n 2 To the medium value target quantity, n 3 For high value target number, total number of tasks to be performed m=n 1 +n 2 +n 3；
And bidding tasks based on an auction algorithm and a predicted communication topology at the time t+td+delta, if all the tasks are bid, acquiring winning bid information and continuously executing the tasks until all the tasks are executed, otherwise, adjusting the profit value of the tasks according to the relation between the number N of healthy unmanned aerial vehicles in the cluster and the total number M of the tasks to be executed and the target profit coefficient so that all the tasks are bid and executed, wherein t is the initial time of task planning, td is the time consumption of the whole task re-planning process, and delta is the reserved margin value.
Further, if the number N of healthy unmanned aerial vehicles in the cluster is greater than or equal to the total number M of tasks to be executed, determining whether the number of unmanned aerial vehicles of the bidding task k is 1, if the number of unmanned aerial vehicles of the bidding task k is 1, keeping the profit value of the task k unchanged, if the number of unmanned aerial vehicles of the bidding task k is 0, increasing the profit value of the task k, and if the number of unmanned aerial vehicles of the bidding task k is greater than 1, decreasing the profit value of the task k, wherein k=1, 2, …, M.
Further, the manner of adjusting the profit value of the task is specifically as follows:
if the task k is a low-value target, the adjustment mode of the profit value is as follows:
wherein,for the adjusted benefit value of task k divided into low value targets, +.>For the profit value of task k before tuning, which is divided into low-value targets,/>Amplitude-regulating control factor for the benefit value of task k divided into low-value targets, +.>The number of unmanned aerial vehicles for task k, which is currently bidded as a low value target, +.>For the number of healthy unmanned aerial vehicles within the cluster, < >>For the set upper limit of the profit adjustment of task k divided into low value targets,/>For the set communication loss proportionality coefficient of the healthy unmanned aerial vehicle in the cluster, the weight of the healthy unmanned aerial vehicle is +.>The number of times of information transfer between unmanned aerial vehicles required by the task k;
if the task k is a medium value target, the adjustment mode of the profit value is as follows:
wherein,for the adjusted benefit value of task k divided into medium value targets,/>For the profit value of task k before adjustment, which is divided into medium value targets,/>Adjusting the control coefficient for the magnitude of the medium value target profit value,/->For the number of unmanned aerial vehicles currently bidding on task k divided into medium value targets, +.>Adjusting an upper limit for the set medium value target profit;
if the task k is a high-value target, the adjustment mode of the profit value is as follows:
wherein,for any of the adjusted partitions into high-value targetsRevenue value for transaction k->For the profit value of task k before tuning, which is divided into high-value targets,/>Adjusting the control coefficient for the magnitude of the high value target profit value,/->The number of unmanned aerial vehicles for task k, which is currently bidded as a high-value target, +.>The upper limit is adjusted for the set high value target revenue.
Further, if the number N of healthy unmanned aerial vehicles in the cluster is smaller than the total number M of tasks to be executed, the task k only allocates 1 unmanned aerial vehicle to execute, the task target with a preset priority is preferably bid, when a certain task is bid for multiple times, the unmanned aerial vehicle with higher actual benefit is preferably bid, if the actual benefit grades are the same, the bid-winning task and the unmanned aerial vehicle with the largest distance difference between the next highest benefit value tasks are preferably bid, and when a certain task is not bid, the benefit value is increased until all the tasks in the queue to be allocated are bid and executed.
Further, the manner of increasing the benefit value is specifically as follows:
if task k is a low value target, the way to increase the profit value is:
wherein,for the increased benefit value of task k divided into low value targets +.>To improve the yield of task k previously divided into low value targetsValue of->Amplitude-regulating control factor for the benefit value of task k divided into low-value targets, +.>For the number of healthy unmanned aerial vehicles within the cluster, < >>For the set upper limit of the profit adjustment of task k divided into low value targets,/>For the set communication loss proportionality coefficient of the healthy unmanned aerial vehicle in the cluster, the weight of the healthy unmanned aerial vehicle is +.>The number of times of information transfer between unmanned aerial vehicles required by the task k;
if the task k is a medium value target, the way to increase the profit value is:
wherein,for the improved profit value of task k divided into medium value targets +.>To increase the value of the return of task k previously divided into medium value targets,/>Amplitude-regulating control factor for the profit value of task k divided into medium-value targets, +.>Adjusting an upper limit for the set income of the task k divided into medium value targets;
wherein,for the adjusted benefit value of task k divided into high value targets, +.>For the profit value of task k before tuning, which is divided into high-value targets,/>Adjusting the control coefficient for the magnitude of the high value target profit value,/->The upper limit is adjusted for the set high value target revenue.
Further, after the unmanned aerial vehicle is judged to be in the damage state, the unmanned aerial vehicle closest to the unmanned aerial vehicle in the damage state in the cluster exits from the task process, the unmanned aerial vehicle in the damage state is searched based on a visual sensor carried by the unmanned aerial vehicle, and if the unmanned aerial vehicle in the damage state is found, the position coordinates and the image information of the unmanned aerial vehicle in the damage state are sent to a remote control center.
The beneficial effects are that: according to the invention, when the unmanned aerial vehicle fails, the landing position of the unmanned aerial vehicle is timely positioned and destroyed by the positioning and remote image return functions of the adjacent unmanned aerial vehicle in the unmanned aerial vehicle or the cluster, so that the subsequent recovery work is convenient, and the loss is reduced; and the task re-planning behavior can be automatically triggered, the complete completion of the original task targets can be ensured, the distribution result is as reasonable as possible while the successful completion of all the task targets is ensured, and the final benefit is maximized.
Drawings
Fig. 1 is a schematic flow chart of a method for processing a damaged outer-line-of-sight cluster unmanned aerial vehicle according to an embodiment of the invention;
fig. 2 is a schematic diagram of information transfer times calculation between unmanned aerial vehicles in a cluster;
FIG. 3 is a flow diagram of task reassignment based on an auction algorithm;
FIG. 4 is a schematic flow chart of adjusting a task benefit value when the number of healthy unmanned aerial vehicles is greater than or equal to the number of tasks to be performed;
fig. 5 is a schematic flow chart of adjusting the task profit value when the number of healthy unmanned aerial vehicles is smaller than the number of tasks to be executed.
Detailed Description
The invention will be further illustrated by the following drawings and specific examples, which are carried out on the basis of the technical solutions of the invention, it being understood that these examples are only intended to illustrate the invention and are not intended to limit the scope of the invention.
As shown in fig. 1 to 5, an embodiment of the present invention provides a method for processing a damaged outer-line-of-sight cluster unmanned aerial vehicle, including:
and controlling the unmanned aerial vehicles in the cluster based on a distributed formation control mode, so that each unmanned aerial vehicle in the cluster keeps communication with the unmanned aerial vehicles adjacent to each unmanned aerial vehicle. When the unmanned aerial vehicle detects that the unmanned aerial vehicle fails, if the failed unmanned aerial vehicle still keeps the communication capability, the failed unmanned aerial vehicle directly returns damage information, then a task rescheduling request is sent to other healthy unmanned aerial vehicles in the cluster, and the original task process is terminated. The damage information comprises the current position coordinates and fault information of the unmanned aerial vehicle, and the specific mode of returning the damage information is as follows: and respectively sending the current position coordinates and fault information to adjacent unmanned aerial vehicles in the cluster, and sending the current position coordinates and the fault information to a remote control center through communication links of other healthy unmanned aerial vehicles. If the failed unmanned aerial vehicle completely loses the communication capability, the nearby unmanned aerial vehicle assists in handling and returning damage information, and the method specifically comprises the following steps: after the failed unmanned aerial vehicle is disconnected from the adjacent unmanned aerial vehicle by communication connection, the unmanned aerial vehicle which is closest to the position coordinate in the last communication data of the failed unmanned aerial vehicle sends a communication request, and if the failed unmanned aerial vehicle responds to the communication request, the failed unmanned aerial vehicle still keeps the communication capability for processing; if the failed unmanned aerial vehicle does not respond to the communication request, the failed unmanned aerial vehicle is judged to be in a damaged state, the position coordinates and the fault information before the fault are returned by the adjacent unmanned aerial vehicles of the failed unmanned aerial vehicle, a task rescheduling request is sent to other healthy unmanned aerial vehicles in the cluster, and the unmanned aerial vehicle in the damaged state terminates the original task process. Specifically, the current gesture can be detected based on the gesture carried by the unmanned aerial vehicle, and when the current pitching or rolling angle of the unmanned aerial vehicle is greater than a preset threshold (for example, 45 degrees), the unmanned aerial vehicle is judged to be faulty. When the communication request is sent, the position coordinate in the last communication data of the unmanned aerial vehicle with the fault can be sent twice at intervals of the unmanned aerial vehicle with the closest position coordinate in the last communication data of the unmanned aerial vehicle with the fault, if the two communication requests are not responded, the position coordinate in the last communication data of the unmanned aerial vehicle with the fault is sent twice at intervals of the unmanned aerial vehicle with the closest position coordinate, and if the two communication requests are not responded, the unmanned aerial vehicle with the fault is judged to be in a damage state. After judging that the unmanned aerial vehicle is in the damage state, the unmanned aerial vehicle closest to the unmanned aerial vehicle in the damage state in the cluster can be led out of the task process, the unmanned aerial vehicle does not participate in the task reassignment process any more, the unmanned aerial vehicle in the damage state is searched based on a visual sensor carried by the unmanned aerial vehicle, and if the unmanned aerial vehicle in the damage state is found, the position coordinates and the image information of the unmanned aerial vehicle in the damage state are sent to a remote control center. The subsequent recovery work is convenient, and the loss and recovery of task data are reduced. And after receiving the task re-planning request, other healthy unmanned aerial vehicles in the cluster carry out task re-planning so as to continue to execute the task, otherwise, continue to execute the original task. Specifically, the mission re-planning is to adjust the autonomous mission re-allocation based on the dynamic income, and then each healthy unmanned aerial vehicle performs the track re-planning according to the mission.
First, the general conditions in both cases are clarified, assuming that M task targets are performed by N healthy unmanned aerial vehicles within the cluster. Because the distributed formation control mode is adopted, and the unmanned aerial vehicle performs tasks outside the sight distance, the unmanned aerial vehicle with the centralized central node does not exist, and the unmanned aerial vehicle can directly send information to all unmanned aerial vehicles. Assuming that the communication radius of the unmanned aerial vehicle is R, if the distance between the two unmanned aerial vehicles is smaller than the radius R, the two unmanned aerial vehicles can be regarded as being capable of directly communicating, otherwise, information transmission is required to be carried out through the relay unmanned aerial vehicle, and particularly, see fig. 2, in fig. 2, the unmanned aerial vehiclesCommunication radius is R, so unmanned plane U 1 Unmanned plane U 2 Can directly communicate with unmanned plane U 1 And U 3 Because of too far distance, unmanned aerial vehicle U needs to pass through relay node 2 Information transfer is performed, and at this time, the number of times of information transfer H is 1, and unmanned aerial vehicle U capable of direct communication is provided 1 And U 2 The number of times of information transfer H between them is 0.
The task re-planning includes:
initializing task information to be executed;
performing value division on task targets to be executed according to preset task priority to obtain low-value targets, medium-value targets and high-value targets, and calculating target profit coefficients of the tasks to be executed according to the target value, wherein the method comprises the following steps of:
wherein a is a low-value target profit coefficient, the weight multiple of the unmanned aerial vehicle is 1, b is a medium-value target profit coefficient, the weight multiple of the unmanned aerial vehicle is 2, c is a high-value target profit coefficient, the weight multiple of the unmanned aerial vehicle is 3, n 1 For low value target quantity, n 2 To the medium value target quantity, n 3 For high value target number, total number of tasks to be performed m=n 1 +n 2 +n 3 . The specific table is shown below:
referring to fig. 3, bidding of tasks is performed based on an auction algorithm and a communication topology estimated at time t+td+delta, if all tasks are bid, winning bid information is obtained and tasks continue to be executed until all tasks are executed, otherwise, the profit value of the tasks is adjusted according to the relation between the number N of healthy unmanned aerial vehicles in a cluster and the total number M of tasks to be executed and a target profit coefficient, so that all tasks are bid and executed, wherein t is the initial time of task planning, td is the time consumed in the whole task re-planning process (including the total time consumed by communication between unmanned aerial vehicles, the total time consumed by a task re-allocation algorithm and the like), and delta is a reserved margin value. Considering that the unmanned aerial vehicle is still in a dynamic position in the dynamic adjustment process, the communication topology changes along with the position of the unmanned aerial vehicle, each unmanned aerial vehicle starts to communicate at the time t, the position of the unmanned aerial vehicle at the time t+td+delta is estimated according to the initial track of the unmanned aerial vehicle, and accordingly the communication topology at the time t+td+delta can be determined, and the situation of communication topology mutation in the process of rescheduling can be avoided in advance.
The technical scheme of task reassignment is described in detail below for two different cases that the number N of healthy unmanned aerial vehicles in the cluster is greater than or equal to the total number M of tasks to be executed and the number N of healthy unmanned aerial vehicles in the rest clusters is smaller than the total number M of tasks to be executed.
Referring to fig. 4, if the number N of healthy unmanned aerial vehicles in the cluster is greater than or equal to the total number M of tasks to be executed, the mapping relationship between the unmanned aerial vehicles and the tasks is many-to-one, so that the tasks must be bid by multiple unmanned aerial vehicles at each moment, but the more one task is bid, the less other tasks are bid, and the situation that the tasks are not bid occurs. At this time, a dynamic profit adjusting rule is introduced, and on the basis of the initial profit value of the task, the updated profit value is dynamically adjusted according to the number of times the task is bidding. In the dynamic adjustment of the profit value, the amplitude of the profit value should be related to the task priority, and the higher the priority, the larger the amplitude of the task to be improved when the task is not bid, and the smaller the amplitude of the task to be reduced when the task is bid for a plurality of times. When the bidding task k is adjusted, whether the number of unmanned aerial vehicles of the bidding task k is 1 is judged first, if the number of unmanned aerial vehicles of the bidding task k is 1, the profit value of the task k is kept unchanged, if the number of unmanned aerial vehicles of the bidding task k is 0, the profit value of the task k is increased, and if the number of unmanned aerial vehicles of the bidding task k is greater than 1, the profit value of the task k is reduced, wherein k=1, 2, … and M.
Specifically, the manner of adjusting the profit value of the task is as follows:
if the task k is a low-value target, the adjustment mode of the profit value is as follows:
wherein,for the adjusted benefit value of task k divided into low value targets, +.>For the profit value of task k before tuning, which is divided into low-value targets,/>Amplitude-regulating control factor for the benefit value of task k divided into low-value targets, +.>The number of unmanned aerial vehicles for task k, which is currently bidded as a low value target, +.>For the number of healthy unmanned aerial vehicles within the cluster, < >>For the set upper limit of the profit adjustment of task k divided into low value targets,/>For the set communication loss proportionality coefficient of the healthy unmanned aerial vehicle in the cluster, the weight of the healthy unmanned aerial vehicle is +.>Number of information transfer between unmanned aerial vehicles required for task k, +.>And->Multiplication results in an information transfer loss value.
If the task k is a medium value target, the adjustment mode of the profit value is as follows:
wherein,for the adjusted benefit value of task k divided into medium value targets,/>For the profit value of task k before adjustment, which is divided into medium value targets,/>Adjusting the control coefficient for the magnitude of the medium value target profit value,/->For the number of unmanned aerial vehicles currently bidding on task k divided into medium value targets, +.>And adjusting the upper limit for the set medium value target income.
If the task k is a high-value target, the adjustment mode of the profit value is as follows:
wherein,for the adjusted benefit value of task k divided into high value targets, +.>For the profit value of task k before tuning, which is divided into high-value targets,/>Adjusting the control coefficient for the magnitude of the high value target profit value,/->The number of unmanned aerial vehicles for task k, which is currently bidded as a high-value target, +.>The upper limit is adjusted for the set high value target revenue. The amplitude adjustment control coefficient ∈>、/>、/>The size is proportional to the task priority, and the higher the priority, the greater the task should be when not bidding, and the smaller the magnitude should be when bidding multiple times.
Referring to fig. 5, if the number N of healthy unmanned aerial vehicles in the cluster is smaller than the total number M of tasks to be executed, at this time, the mapping relationship between the unmanned aerial vehicles and the tasks is one-to-many, and the one-to-many mapping relationship means that each unmanned aerial vehicle needs to execute a plurality of tasks, each task needs and only needs 1 unmanned aerial vehicle to execute, and the task target with high preset priority is preferably marked. Specifically, when a certain task is bid for multiple times, the unmanned aerial vehicle with higher actual benefit is bid for the first time, if the actual benefit grade is the same, the unmanned aerial vehicle with the largest distance difference between the bid-winning task and the next highest benefit value task is bid for the first time, and when the certain task is not bid for, the benefit value is increased. The actual benefit=the set target value-cost, and the cost is related to the distance between the unmanned plane and the current distance task k, the information transmission loss value, the calculated amount of flight path planning and time. In actual operation, task k is placed in the queue if it has been allocatedThe method comprises the steps of carrying out a first treatment on the surface of the If not, put into queue +.>Then raise the queue +.>And (3) the profit value of the task k until all the tasks are marked in the unmanned aerial vehicle, namely, the tasks are distributed.
Specifically, the manner of increasing the benefit value is as follows:
if task k is a low value target, the way to increase the profit value is:
wherein,for the increased benefit value of task k divided into low value targets +.>To increase the benefit value of task k previously classified as low value target,/>Amplitude-regulating control factor for the benefit value of task k divided into low-value targets, +.>For the number of healthy unmanned aerial vehicles within the cluster, < >>For the set upper limit of the profit adjustment of task k divided into low value targets,/>For the set communication loss proportionality coefficient of the healthy unmanned aerial vehicle in the cluster, the weight of the healthy unmanned aerial vehicle is +.>And the number of times of information transfer between unmanned aerial vehicles required by the task k.
If the task k is a medium value target, the way to increase the profit value is:
wherein,for the improved profit value of task k divided into medium value targets +.>To increase the value of the return of task k previously divided into medium value targets,/>Amplitude-regulating control factor for the profit value of task k divided into medium-value targets, +.>The upper limit is adjusted for the set revenue of task k divided into medium value targets.
Wherein,for the adjusted benefit value of task k divided into high value targets, +.>For the profit value of task k before tuning, which is divided into high-value targets,/>Adjusting the control coefficient for the magnitude of the high value target profit value,/->The upper limit is adjusted for the set high value target revenue. It should be noted that->、/>And->The higher the task priority, the greater the task k-to-next highest benefit value task distance difference, and the greater the adjustment amplitude.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that other parts not specifically described are within the prior art or common general knowledge to a person of ordinary skill in the art. Modifications and alterations may be made without departing from the principles of this invention, and such modifications and alterations should also be considered as being within the scope of the invention.
Claims (2)
1. The processing method after the damage of the unmanned aerial vehicle of the outer cluster of the visual range is characterized by comprising the following steps:
when the unmanned aerial vehicle in the cluster is controlled based on the distributed formation control mode and faults occur, if the unmanned aerial vehicle with faults still keeps the communication capability, the current position coordinates and fault information are respectively sent to unmanned aerial vehicles with the closest distances in the cluster, then task re-planning requests are sent to other healthy unmanned aerial vehicles in the cluster, the original task process is terminated, and the current position coordinates and the fault information are sent to a remote control center through communication links of the other healthy unmanned aerial vehicles; if the failed unmanned aerial vehicle completely loses the communication capacity, after the failed unmanned aerial vehicle is disconnected from the adjacent unmanned aerial vehicle, the unmanned aerial vehicle which is closest to the position coordinates in the last communication data of the failed unmanned aerial vehicle sends a communication request, and if the failed unmanned aerial vehicle responds to the communication request, the failed unmanned aerial vehicle still keeps the communication capacity for processing; if the failed unmanned aerial vehicle does not respond to the communication request, judging that the failed unmanned aerial vehicle is in a damaged state, returning position coordinates and fault information before the failure by the adjacent unmanned aerial vehicle of the failed unmanned aerial vehicle, sending task rescheduling requests to other healthy unmanned aerial vehicles in the cluster, and terminating the original task process by the unmanned aerial vehicle in the damaged state;
after receiving the task re-planning request, other healthy unmanned aerial vehicles in the cluster perform task re-planning to continue executing tasks, wherein the task re-planning comprises:
initializing task information to be executed;
performing value division on task targets to be executed according to preset task priority to obtain low-value targets, medium-value targets and high-value targets, and calculating target profit coefficients of the tasks to be executed according to the target value, wherein the method comprises the following steps of:
；
wherein a is a low-value target profit coefficient, the weight multiple of the unmanned aerial vehicle is 1, b is a medium-value target profit coefficient, the weight multiple of the unmanned aerial vehicle is 2, c is a high-value target profit coefficient, the weight multiple of the unmanned aerial vehicle is 3, n 1 For low value target quantity, n 2 To the medium value target quantity, n 3 For high value target number, total number of tasks to be performed m=n 1 +n 2 +n 3；
Bidding tasks based on an auction algorithm and a predicted communication topology at the time t+td+delta, if all the tasks are bid, acquiring winning bid information and continuing to execute the tasks until all the tasks are executed, otherwise, adjusting the profit value of the tasks according to the size relation between the number N of healthy unmanned aerial vehicles in the cluster and the total number M of the tasks to be executed and the target profit coefficient so that all the tasks are bid and executed, wherein t is the initial time of task planning, td is the time spent in the whole task re-planning process, and delta is the reserved margin value;
if the number N of healthy unmanned aerial vehicles in the cluster is greater than or equal to the total number M of tasks to be executed, judging whether the number of unmanned aerial vehicles of the bidding task k is 1, if the number of unmanned aerial vehicles of the bidding task k is 1, keeping the profit value of the task k unchanged, if the number of unmanned aerial vehicles of the bidding task k is 0, increasing the profit value of the task k, and if the number of unmanned aerial vehicles of the bidding task k is greater than 1, reducing the profit value of the task k, wherein k=1, 2, … and M;
the manner of adjusting the benefit value of the task is specifically as follows:
if the task k is a low-value target, the adjustment mode of the profit value is as follows:
；
wherein,for the adjusted benefit value of task k divided into low value targets, +.>For the profit value of task k before tuning, which is divided into low-value targets,/>The control coefficients are adjusted for the magnitude of the benefit value of task k divided into low value targets,the number of unmanned aerial vehicles for task k, which is currently bidded as a low value target, +.>For the number of healthy unmanned aerial vehicles within the cluster, < >>For the set upper limit of the profit adjustment of task k divided into low value targets,/>For the set communication loss proportionality coefficient of the healthy unmanned aerial vehicle in the cluster, the weight of the healthy unmanned aerial vehicle is +.>The number of times of information transfer between unmanned aerial vehicles required by the task k;
if the task k is a medium value target, the adjustment mode of the profit value is as follows:
；
wherein,for the adjusted benefit value of task k divided into medium value targets,/>For the profit value of task k before adjustment, which is divided into medium value targets,/>Adjusting the control coefficient for the magnitude of the medium value target profit value,/->For the number of unmanned aerial vehicles currently bidding on task k divided into medium value targets, +.>Adjusting an upper limit for the set medium value target profit;
if the task k is a high-value target, the adjustment mode of the profit value is as follows:
；
wherein,for the adjusted benefit value of task k divided into high value targets, +.>For the profit value of task k before tuning, which is divided into high-value targets,/>Is high in priceAmplitude adjustment control coefficient of value target profit value, +.>The number of unmanned aerial vehicles for task k, which is currently bidded as a high-value target, +.>Adjusting an upper limit for the set high-value target profit;
if the number N of healthy unmanned aerial vehicles in the cluster is smaller than the total number M of tasks to be executed, the task k only allocates 1 unmanned aerial vehicle to execute, the task target with high preset priority is preferably bid, when a certain task is bid for multiple times, the unmanned aerial vehicle with higher actual benefit is preferably bid, if the actual benefit grade is the same, the unmanned aerial vehicle with the largest distance difference between the bid-winning task and the next highest benefit value is preferably bid, and when a certain task is not bid, the benefit value is increased until all the tasks in the queue to be allocated are bid and are executed;
the way to increase the benefit value is as follows:
if task k is a low value target, the way to increase the profit value is:
；
wherein,for the increased benefit value of task k divided into low value targets +.>To increase the benefit value of task k previously classified as low value target,/>Amplitude-regulating control factor for the benefit value of task k divided into low-value targets, +.>For the number of healthy unmanned aerial vehicles within the cluster, < >>For the set upper limit of the profit adjustment of task k divided into low value targets,/>For the set communication loss proportionality coefficient of the healthy unmanned aerial vehicle in the cluster, the weight of the healthy unmanned aerial vehicle is +.>The number of times of information transfer between unmanned aerial vehicles required by the task k;
if the task k is a medium value target, the way to increase the profit value is:
；
wherein,for the improved profit value of task k divided into medium value targets +.>To increase the value of the return of task k previously divided into medium value targets,/>Amplitude-regulating control factor for the profit value of task k divided into medium-value targets, +.>Adjusting an upper limit for the set income of the task k divided into medium value targets;
；
wherein,for the adjusted benefit value of task k divided into high value targets, +.>For the profit value of task k before tuning, which is divided into high-value targets,/>Adjusting the control coefficient for the magnitude of the high value target profit value,/->The upper limit is adjusted for the set high value target revenue.
2. The method for processing the damaged unmanned aerial vehicle of the out-of-sight cluster according to claim 1, wherein after the unmanned aerial vehicle is judged to be in a damaged state, the unmanned aerial vehicle in the cluster closest to the unmanned aerial vehicle in the damaged state exits from the task process, the unmanned aerial vehicle in the damaged state is searched based on a visual sensor carried by the unmanned aerial vehicle, and if the unmanned aerial vehicle in the damaged state is found, the position coordinates and the image information of the unmanned aerial vehicle in the damaged state are sent to a remote control center.
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