CN110083173B - Optimization method for unmanned aerial vehicle formation inspection task allocation - Google Patents
Optimization method for unmanned aerial vehicle formation inspection task allocation Download PDFInfo
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Abstract
The invention relates to the technical field of unmanned aerial vehicle task allocation, and discloses an optimization method for unmanned aerial vehicle formation inspection task allocation. The optimization method comprises the following steps: acquiring information of potential point targets and information of unmanned aerial vehicle formation; building a UAVF-RTOP-RP model; acquiring an initial task allocation scheme set of the unmanned aerial vehicle formation execution inspection tasks; and optimizing the initial task allocation scheme set by adopting an improved hybrid particle swarm simulated annealing algorithm to obtain an optimal task allocation scheme for unmanned aerial vehicle formation. The optimization method optimizes the task allocation scheme of unmanned aerial vehicle formation under the condition of considering various constraint conditions such as the weight of a potential target, the cruising ability of the unmanned aerial vehicle, the error of a sensor and the like, and exerts the utility of the unmanned aerial vehicle to the maximum extent, so that the effectiveness of the inspection task is improved.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle task allocation, in particular to an optimization method for unmanned aerial vehicle formation inspection task allocation.
Background
The unmanned aerial vehicle formation is adopted to patrol the expressway, the disaster area or the electric power iron tower, and is the future development direction. However, in the prior art, when the unmanned aerial vehicles form a formation to execute the routing inspection task, multiple constraint conditions such as the weight of a potential target, the cruising ability of the unmanned aerial vehicles, the error of a sensor and the like are not considered, and the influence of the constraint conditions on the distribution scheme of the routing inspection tasks of the multiple unmanned aerial vehicles, such as the number of times of access to the potential target, is not considered, so that the utility of the unmanned aerial vehicles is not exerted to the maximum extent, and the completion quality of the routing inspection tasks is poor.
Disclosure of Invention
The invention aims to provide an optimization method for unmanned aerial vehicle formation routing inspection task allocation, which optimizes a task allocation scheme of unmanned aerial vehicle formation under the condition of considering various constraint conditions such as weight of a potential point target, endurance capacity of the unmanned aerial vehicle, sensor error and the like, and furthest exerts the utility of the unmanned aerial vehicle, thereby improving the effectiveness of routing inspection tasks.
In order to achieve the purpose, the invention provides an optimization method for unmanned aerial vehicle formation inspection task allocation, which comprises the following steps: determining a task range of formation and inspection of the unmanned aerial vehicles; determining a target object to be inspected in a task range, wherein the target object comprises buildings in an earthquake-stricken area, an accident point of a highway and an electric power iron tower; abstracting a target object into a potential point target, and acquiring coordinate information of the potential point target; determining the weight of the potential point target according to the importance of the potential point target; acquiring information of formation of unmanned aerial vehicles, wherein the information of the formation of the unmanned aerial vehicles comprises starting points and end points of the unmanned aerial vehicles, the number of each unmanned aerial vehicle, the maximum safe endurance time and detection errors of carried sensors; establishing a UAVF-RTOP-RP model with income probability for revisiable team orientation problems of unmanned aerial vehicle formation; acquiring an initial task allocation scheme set of an unmanned aerial vehicle formation inspection task according to coordinate information and weight of a potential point target and information of unmanned aerial vehicle formation by adopting a UAVF-RTOP-RP model, wherein the initial task allocation scheme set comprises a plurality of task allocation schemes, the task allocation schemes are defined as a task execution path of each unmanned aerial vehicle in the unmanned aerial vehicle formation and a corresponding unmanned aerial vehicle number, and the task execution path comprises potential point targets which are sequentially passed by the unmanned aerial vehicles; and obtaining a current optimal task allocation scheme of the initial task allocation scheme set by adopting an improved hybrid particle swarm simulated annealing algorithm, and then optimizing the current optimal task allocation scheme to obtain an optimal task allocation scheme of unmanned aerial vehicle formation.
Preferably, abstracting the object as a potential point object, and acquiring the coordinate information of the potential point object specifically includes: acquiring longitude coordinates and latitude coordinates of all vertexes of the target object; selecting a minimum longitude coordinate and a maximum longitude coordinate from all longitude coordinates, and selecting a minimum latitude coordinate and a maximum latitude coordinate from all latitude coordinates; obtaining a rectangular target range according to the minimum longitude coordinate, the maximum longitude coordinate, the minimum latitude coordinate and the maximum latitude coordinate, wherein the coordinates of four vertexes of the rectangular target range can be expressed as:
(minimum longitude coordinate, minimum latitude coordinate), (minimum longitude coordinate, maximum latitude coordinate),
(maximum longitude coordinate, minimum latitude coordinate), (maximum longitude coordinate, maximum latitude coordinate); and acquiring coordinate information of the intersection point of two diagonal lines of the rectangular target range as coordinate information of the potential point target.
Preferably, the objective function of the UAVF-RTOP-RP model is represented by formula (1):
wherein, wiWeight for potential point targets, p is error of sensor carried by drone, yiThe number of times that the ith potential target is accessed is L the number of potential point targets, and Max is a maximum function;
the constraint conditions of the UAVF-RTOP-RP model are expressed by the formulas (2) to (6):
wherein the content of the first and second substances,
for the kth drone from the starting point to the ith potential point target,
for the kth unmanned aerial vehicle, from the ith potential point target to the destination, 0 represents the starting point of the unmanned aerial vehicle, L +1 represents the destination of the unmanned aerial vehicle, and U is the set of the unmanned aerial vehicles;
for the kth drone from the h potential point target to the ith potential point target,
for the kth unmanned aerial vehicle, from the ith potential point target to the jth potential point target, T is a set of potential point targets; y isiThe number of times the target is accessed for the ith potential point; k is the number of unmanned aerial vehicles; t is tijFor the time, T, that the unmanned plane flies from the ith potential point target to the jth potential point targetmaxThe maximum safe endurance time of the unmanned aerial vehicle is set; the formula (6) is the value of the binary decision variable when
A value of 1 indicates that the kth drone has selected a path from target i to target j, when
A value of 0 indicates that the kth drone has not selected this path.
Preferably, an improved hybrid particle swarm simulated annealing algorithm is adopted to obtain a current optimal task allocation scheme of the initial task allocation scheme set, and then the current optimal task allocation scheme is optimized to obtain an optimal task allocation scheme for unmanned aerial vehicle formation, specifically comprising: determining simulated annealing parameters and disturbance parameters, wherein the simulated annealing parameters comprise initial temperature, termination temperature and cooling rate, and the disturbance parameters comprise disturbance times; defining an initial temperature as a current temperature; calculating the scheme income of each task allocation scheme in the initial task allocation scheme set, and defining the task allocation scheme with the maximum scheme income as the current optimal task allocation scheme; defining an initial task allocation scheme set as a task allocation scheme set to be updated; respectively adopting an updating strategy for each task allocation scheme in the task allocation scheme set to be updated so as to generate an updated task allocation scheme set; judging whether the updated task allocation scheme set comprises a task allocation scheme which fails to pass the path communication check and the endurance check; under the condition that the updated task allocation scheme set is judged to contain the task allocation scheme which cannot pass the path communication check and/or the endurance check, the task allocation scheme which cannot pass the path communication check and/or the endurance check is adjusted, so that all the task allocation schemes in the task allocation scheme set can pass the path communication check and the endurance check; calculating the scheme income of each task allocation scheme in the updated task allocation scheme set, and defining the task allocation scheme with the maximum scheme income in the updated task allocation scheme set as a local optimal task allocation scheme; judging whether the scheme income of the local optimal task allocation scheme is larger than or equal to the scheme income of the current optimal task allocation scheme; under the condition that the scheme benefit of the local optimal task allocation scheme is judged to be more than or equal to the scheme benefit of the current optimal task allocation scheme, replacing the current optimal task allocation scheme with the local optimal task allocation scheme to update the current optimal task allocation scheme; defining the current optimal task allocation scheme as the current optimal disturbance task allocation scheme; adopting a disturbance strategy for the current optimal disturbance task allocation scheme to generate a disturbance task allocation scheme; judging whether the disturbance task allocation scheme can pass the path communication check and the cruising ability check; under the condition that the disturbance task allocation scheme is judged to fail to pass the path communication verification and/or the endurance verification, the disturbance task allocation scheme is adjusted so as to enable the disturbance task allocation scheme to pass the path communication verification and the endurance verification; judging whether a disturbance task allocation scheme is accepted or not according to a Metropolis mechanism of a simulated annealing algorithm; under the condition of judging that the disturbance task allocation scheme is received, replacing the current optimal disturbance task allocation scheme with the disturbance task allocation scheme so as to update the current optimal disturbance task allocation scheme; judging whether the number of the disturbance operations is more than or equal to the disturbance number; under the condition that the number of times of disturbance operation which is already carried out is judged to be more than or equal to the number of times of disturbance, outputting a current optimal disturbance task allocation scheme; respectively calculating the scheme gains of the current optimal task allocation scheme and the current optimal disturbance task allocation scheme; judging whether the scheme income of the current optimal disturbance task allocation scheme is larger than the scheme income of the current optimal task allocation scheme; under the condition that the scheme benefit of the current optimal disturbance task allocation scheme is judged to be larger than the scheme benefit of the current optimal task allocation scheme, the current optimal disturbance task allocation scheme replaces the current optimal task allocation scheme so as to update the current optimal task allocation scheme; judging whether the current temperature is lower than the termination temperature or not; under the condition that the current temperature is judged to be more than or equal to the termination temperature, updating the current temperature according to the cooling rate, defining the updated task allocation scheme set as a new task allocation scheme set to be updated so as to regenerate the updated task allocation scheme set, and further updating or not updating the current optimal task allocation scheme; and under the condition that the current temperature is judged to be lower than the termination temperature, outputting the current optimal task allocation scheme to serve as the optimal task allocation scheme for the unmanned aerial vehicle formation.
Preferably, the adopting of the update strategy for the task allocation scheme in the task allocation scheme set to be updated specifically includes: randomly selecting two potential point targets from a task allocation scheme in the task allocation scheme to be updated as potential point targets to be replaced; randomly selecting two potential point targets from the current optimal task allocation scheme as replacement potential point targets; at least two potential point targets between the two potential point targets to be replaced are replaced by at least two potential point targets between the two replacement potential point targets, thereby obtaining an updated task allocation plan.
Preferably, defining the task allocation scheme in the update task allocation scheme set as an update task allocation scheme, and adjusting the update task allocation scheme or the disturbance task allocation scheme specifically includes: deleting repeated potential point targets in the updated task allocation scheme or the disturbed task allocation scheme under the condition that the updated task allocation scheme or the disturbed task allocation scheme is judged to fail to pass the path communication verification; and under the condition that the updated task allocation scheme or the disturbed task allocation scheme is judged to fail to pass the endurance verification, sequentially deleting at least one potential point target according to the weight of the potential point target, so that the disturbed task allocation scheme can pass the endurance verification.
Preferably, the perturbation strategy is specifically: one of the following four disturbance measures is taken for each unmanned aerial vehicle in the multiple unmanned aerial vehicles in sequence: a first perturbation measure, which randomly exchanges the sequence of two potential point targets on a feasible task path of the unmanned aerial vehicle; a second disturbance measure, wherein the sequence of at least three adjacent potential point targets passing through the feasible task path of the unmanned aerial vehicle is randomly inverted; a third disturbance measure, namely randomly deleting a potential point target on a feasible task path of the unmanned aerial vehicle; a fourth perturbation measure, if all potential point targets in the task execution range of the unmanned aerial vehicle are not included in the feasible task path of the unmanned aerial vehicle, randomly selecting one potential point target which is not included in the feasible task path from the task execution range and inserting the potential point target into the feasible task path; and if the feasible task path of the unmanned aerial vehicle contains all the potential point targets in the task execution range of the unmanned aerial vehicle, randomly selecting one potential point target with the maximum weight from the task execution range to insert into the feasible task path.
Preferably, the scheme yield of the current optimal task allocation scheme, the local optimal task allocation scheme or the current optimal disturbance task allocation scheme is calculated by formula (7):
the Fit is the scheme benefit of the current optimal task allocation scheme, the local optimal task allocation scheme or the current optimal disturbance task allocation scheme, and the L is the number of potential point targets contained in the current optimal task allocation scheme, the local optimal task allocation scheme or the current optimal disturbance task allocation scheme.
Through the technical scheme, the task allocation method optimizes the task allocation scheme of unmanned aerial vehicle formation under the condition that multiple constraint conditions such as the weight of a potential target, the cruising ability of the unmanned aerial vehicle, the error of a sensor and the like are considered, the utility of the unmanned aerial vehicle is exerted to the maximum extent, and therefore the effectiveness of the routing inspection task is improved.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of an optimization method for unmanned aerial vehicle formation patrol task allocation according to an embodiment of the present invention;
FIG. 2 shows a schematic diagram of an abstraction of an object into a potential point object;
FIG. 3 illustrates a diagram of a target revisitable patrol task allocation strategy;
FIG. 4 is a graph illustrating the trend of expected revenue as a function of number of visits;
FIG. 5 is a flow chart of an optimized task allocation scheme using a modified hybrid particle swarm simulated annealing algorithm according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating a process for updating a task allocation scheme;
FIG. 7 shows a distribution diagram of potential point targets within the scope of a routing inspection task;
FIG. 8 shows a schematic diagram of an optimal task allocation scheme.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
Fig. 1 is a flowchart of an optimization method for unmanned aerial vehicle formation patrol inspection task allocation according to an embodiment of the present invention. As shown in fig. 1, in an embodiment of the present invention, an optimization method for unmanned aerial vehicle formation patrol task allocation is provided, where the optimization method may include:
in step S101, determining a task range of formation and inspection of the unmanned aerial vehicles;
in step S102, determining a target object to be inspected in a task range, where the target object may include, for example, a building in an earthquake-stricken area, an accident point of a highway, and an electric power tower;
in step S103, abstracting the target object into a potential point target, and acquiring coordinate information of the potential point target;
in step S104, determining the weight of the potential point target according to the importance of the potential point target;
in step S105, acquiring information of formation of unmanned aerial vehicles, where the information of formation of unmanned aerial vehicles includes starting points and ending points of the unmanned aerial vehicles, the number of each unmanned aerial vehicle, the maximum safe cruising time, and detection errors of the sensors;
in step S106, a revisitable team orientation problem UAVF-RTOP-RP model with income probability of unmanned aerial vehicle formation is established;
in step S107, a UAVF-RTOP-RP model is adopted, and an initial task allocation scheme set of the unmanned aerial vehicle formation for executing the routing inspection task is obtained according to the coordinate information and the weight of the potential point target and the information of the unmanned aerial vehicle formation, wherein the initial task allocation scheme set comprises a plurality of task allocation schemes, the task allocation schemes are defined as a task execution path and a corresponding unmanned aerial vehicle number of each unmanned aerial vehicle in the unmanned aerial vehicle formation, and the task execution path comprises the potential point targets sequentially passed by the unmanned aerial vehicles;
in step S108, an improved hybrid particle swarm simulated annealing algorithm is adopted to obtain a current optimal task allocation scheme of the initial task allocation scheme set, and then the current optimal task allocation scheme is optimized to obtain an optimal task allocation scheme for the formation of the unmanned aerial vehicles.
In an embodiment of the present invention, the task allocation scheme for formation of drones can be represented by the following table, for example:
| 3 | 4 | 1 |
| 1 | 1 | 2 |
wherein, the number of the potential point target is acted as the first behavior, the number of the unmanned aerial vehicle is acted as the second behavior, and the task allocation scheme represents that: the number of the unmanned aerial vehicles is 1 and 2 respectively; starting from the starting point, the No. 1 unmanned aerial vehicle sequentially passes through the No. 3 potential point target and the No. 4 potential point target and then returns to the end point; no. 2 unmanned aerial vehicle starts from the starting point, and returns to the terminal point after passing through No. 2 potential point targets. The start and end points may for example be located at the same position.
The importance of the potential point targets abstracted from the building may be determined, for example, by attributes such as population within the building, distance to epicenter, and type of building; the importance of the potential point target abstracted from the expressway accident point can be determined according to the congestion degree of the congestion point; the importance of the potential point target abstracted from the power tower can be determined according to the attributes of the tower type, the geographical position (mountain or plain) and the like.
The weight of the potential point target may be set by rank. For example, the potential point target may be weighted by an integer value from 1 to 10, where 1 represents the least significant potential point target and 10 represents the most significant potential point target.
Fig. 2 shows a schematic drawing of an object abstract as a potential point object. As shown in fig. 2, in an embodiment of the present invention, abstracting the object as a potential point object, and acquiring the coordinate information of the potential point object may specifically include:
acquiring longitude coordinates and latitude coordinates of all vertexes of the target object;
selecting the minimum longitude coordinate x from all the longitude coordinatesminAnd a maximum longitude coordinate xmaxSelecting the minimum latitude coordinate y from all latitude coordinatesminAnd a maximum latitude coordinate ymax；
According to the minimum longitude coordinate xminMaximum longitude coordinate xmaxMinimum latitude coordinate yminAnd a maximum latitude coordinate ymaxObtaining a rectangular target range, and the coordinates of the four vertices of the rectangular target range can be expressed as: (x)min，ymin)、(xmin，ymax)、(xmax，ymin)、(xmax，ymax)；
And acquiring coordinate information of the intersection point of two diagonal lines of the rectangular target range as coordinate information of the potential point target.
The unmanned aerial vehicle formation execution polling task is to quickly evaluate the disaster condition of the earthquake-stricken area; rapidly surveying accident points of the highway; and evaluating the health condition of the electric tower. When the unmanned aerial vehicle executes the routing inspection task, noise and errors inevitably exist in a sensor carried by the unmanned aerial vehicle, so that the credibility of the information of the potential point target collected by the unmanned aerial vehicle is influenced, and further the effectiveness of a quick evaluation result is influenced. Therefore, in one embodiment of the present invention, a quantitative calculation method is proposed to describe the effectiveness of rapid evaluation with expected revenue. The expected benefit is related to the weight of the potential point targets and the detection error of the drone sensor. For example, the expected profit of the drone for a fast assessment of the ith potential point target may be calculated using equation (1):
Ri＝wiequation (1-p) (1)
Wherein R isiExpected profit, w, for one quick evaluation of the i-th potential point target by the droneiFor the weight of the ith potential point target, p is the detection error of the drone sensor, and in one embodiment of the invention, for example, the detection errors of the sensors carried by all drones in the formation of drones may be considered to be the same.
In addition, it can be considered that potential point targets in the drone formation patrol task allocation scheme can be repeatedly accessed (as shown in fig. 3). That is to say, after information in a certain potential point target is successfully detected by one unmanned aerial vehicle, the probability of successful detection of the information of the potential point target by other unmanned aerial vehicles is not influenced, and therefore, y is carried out on the ith potential point targetiThe total expected revenue after the secondary visit may be calculated using equation (2):
wherein R isi' go y for ith potential Point targetiTotal expected revenue after secondary visit.
FIG. 4 shows that the weight of the target at the ith potential point is wiThe expected yield is shown as the trend of the accessed times in the case that the detection errors p of the sensors are respectively 0.1, 03, 0.5, 0.7 and 0.9 which are 10. From FIG. 3See that the expected revenue for the ith potential point objective increases with the number of visits. In the conventional task allocation scheme, each potential point target is accessed once, and the situation of repeated access does not exist, so that the expected benefit of each potential point target after being accessed is a fixed value.
Therefore, in view of the above proposed method of expected profitability of potential point targets, in one embodiment of the present invention, the objective function of the UAVF-RTOP-RP model can be represented by equation (3):
wherein, wiWeight for potential point targets, p is error of sensor carried by drone, yiAnd L is the number of times the ith potential target is accessed, and Max is a maximum function. The objective function of formula (3) represents that the expected profit of the unmanned aerial vehicle formation patrol task allocation scheme is maximized, and the expected profit of the task allocation scheme is the sum of the expected profits of all potential point targets.
The constraint conditions of the UAVF-RTOP-RP model are expressed by the formulas (4) to (8):
wherein the content of the first and second substances,
for the kth drone from the starting point to the ith potential point target,
for the kth unmanned aerial vehicle, from the ith potential point target to the destination, 0 represents the starting point of the unmanned aerial vehicle, L +1 represents the destination of the unmanned aerial vehicle, and U is the set of the unmanned aerial vehicles;
for the kth drone from the h potential point target to the ith potential point target,
for the kth unmanned aerial vehicle, from the ith potential point target to the jth potential point target, T is a set of potential point targets; y isiThe number of times the target is accessed for the ith potential point; k is the number of unmanned aerial vehicles; t is tijFor the time, T, that the unmanned plane flies from the ith potential point target to the jth potential point targetmaxThe maximum safe endurance time of the unmanned aerial vehicle is set; the formula (8) is the value of the binary decision variable when
A value of 1 indicates that the kth drone has selected a path from target i to target j, when
A value of 0 indicates that the kth drone has not selected this path.
The constraint conditions of the formula (4) can ensure that all unmanned aerial vehicles start from the starting point and finally return to the end point; the constraint condition of the formula (5) can ensure that the number of unmanned aerial vehicles flying into the ith potential point target is equal to the number of unmanned aerial vehicles flying out of the ith potential point target and is equal to the number of times that the target i is visited; formula (7) represents the endurance constraint of the drone.
FIG. 5 is a flow chart of an optimized task allocation scheme using a modified hybrid particle swarm simulated annealing algorithm according to an embodiment of the invention. As shown in fig. 5, in an embodiment of the present invention, an improved hybrid particle swarm simulated annealing algorithm is adopted to obtain a current optimal task allocation scheme of an initial task allocation scheme set, and then the current optimal task allocation scheme is optimized to obtain an optimal task allocation scheme for formation of unmanned aerial vehicles, which specifically includes:
in step S201, simulated annealing parameters and disturbance parameters are determined, wherein the simulated annealing parameters comprise an initial temperature T0End temperature TendAnd a cooling rate RtThe disturbance parameters comprise disturbance times M;
in step S202, the initial temperature T is set0Is defined as the current temperature TiterI.e. let Titer＝T0；
In step S203, calculating a scheme benefit of each task allocation scheme in the initial task allocation scheme set, and defining the task allocation scheme with the maximum scheme benefit as a current optimal task allocation scheme;
in step S204, defining the initial task allocation plan set as a task allocation plan set to be updated;
in step S205, respectively adopting an update policy for each task allocation scheme in the set of task allocation schemes to be updated, so as to generate an update set of task allocation schemes;
in step S206, it is determined whether a task allocation plan failing to pass the path connectivity check and the endurance check is included in the updated task allocation plan set by formula (5) and formula (7), respectively;
in step S207, in a case that it is determined that the updated task allocation scheme set includes the task allocation scheme that fails to pass the path connectivity check and/or the endurance check, the task allocation scheme that fails to pass the path connectivity check and/or the endurance check is adjusted, so that all task allocation schemes in the task allocation scheme set can pass the path connectivity check and the endurance check;
in step S208, calculating a plan benefit of each task allocation plan in the updated task allocation plan set, and defining the task allocation plan with the largest plan benefit in the updated task allocation plan set as a local optimal task allocation plan;
in step S209, it is determined whether the plan benefit of the local optimal task allocation plan is greater than or equal to the plan benefit of the current optimal task allocation plan;
in step S210, under the condition that it is determined that the scheme benefit of the local optimal task allocation scheme is greater than or equal to the scheme benefit of the current optimal task allocation scheme, replacing the current optimal task allocation scheme with the local optimal task allocation scheme to update the current optimal task allocation scheme;
in step S211, defining the current optimal task allocation scheme as the current optimal perturbation task allocation scheme;
in step S212, a perturbation strategy is adopted for the current optimal perturbation task allocation scheme to generate a perturbation task allocation scheme;
in step S213, whether the disturbance task allocation scheme can pass the path connectivity check and the endurance check is determined by formula (5) and formula (7), respectively;
in step S214, under the condition that it is determined that the disturbance task allocation scheme fails to pass the path connectivity check and/or the endurance check, the disturbance task allocation scheme is adjusted so that the disturbance task allocation scheme can pass the path connectivity check and the endurance check;
in step S215, whether to accept the disturbance task allocation scheme is determined according to the Metropolis mechanism of the simulated annealing algorithm;
in step S216, under the condition that it is determined that the disturbance task allocation scheme is received, replacing the current optimal disturbance task allocation scheme with the disturbance task allocation scheme to update the current optimal disturbance task allocation scheme;
in step S217, it is determined whether or not the number M of times of disturbance operation that has been performed is equal to or greater than the number M of times of disturbance;
in step S218, in the case that it is determined that the number of times of the disturbance operation that has been performed is greater than or equal to the number of times of disturbance, outputting a current optimal disturbance task allocation scheme;
in step S219, calculating a scheme yield of the current optimal task allocation scheme and the current optimal disturbance task allocation scheme, respectively;
in step S220, it is determined whether the scheme benefit of the current optimal perturbation task allocation scheme is greater than the scheme benefit of the current optimal task allocation scheme;
in step S221, under the condition that it is determined that the scheme benefit of the current optimal perturbation task allocation scheme is greater than the scheme benefit of the current optimal task allocation scheme, the current optimal perturbation task allocation scheme replaces the current optimal task allocation scheme to update the current optimal task allocation scheme;
in step S222, the current temperature T is judgediterWhether or not less than the termination temperature Tend；
Under the condition that the current temperature is judged to be more than or equal to the termination temperature, updating the current temperature according to the cooling rate, defining the updated task allocation scheme set as a new task allocation scheme set to be updated so as to regenerate the updated task allocation scheme set, and further updating or not updating the current optimal task allocation scheme;
in step S223, under the condition that the current temperature is determined to be less than the termination temperature, the current optimal task allocation scheme is output as the optimal task allocation scheme for the formation of the unmanned aerial vehicles.
FIG. 6 shows a process diagram for updating a task allocation scheme. As shown in fig. 6, in an embodiment of the present invention, the applying an update policy to a task allocation scheme in a set of task allocation schemes to be updated may specifically include:
randomly selecting two potential point targets from the task allocation schemes in the task allocation scheme set to be updated as potential point targets to be replaced;
randomly selecting two potential point targets from the current optimal task allocation scheme as replacement potential point targets;
at least two potential point targets (including the potential point target to be replaced) between the two potential point targets to be replaced are replaced by at least two potential point targets (including the potential point target to be replaced) between the two replacement potential point targets, thereby obtaining an updated task allocation plan.
Of course, when replacing the potential point target or replacing the number of the potential point target, the corresponding drone or the drone number is also replaced accordingly. The selected potential point targets to be replaced or the replacement potential point targets to be replaced may or may not be adjacent in the task allocation scheme, and in the case that the selected potential point targets to be replaced or the replacement potential point targets to be replaced may or may not be adjacent in the task allocation scheme, two potential point targets (that is, two potential point targets to be replaced or two replacement potential point targets themselves) are included between the two potential point targets to be replaced or the replacement potential point targets to be replaced, and in the case that the selected potential point targets to be replaced or the replacement potential point targets to be replaced may or may not be adjacent in the task allocation scheme, more than two potential point targets are included between the two potential point targets to be replaced or the replacement potential point targets to be replaced.
For example, one task allocation scheme in the set of task allocation schemes to be updated is represented as:
selecting a No. 4 potential point target through which a No. 1 unmanned aerial vehicle passes and a No. 2 potential point target (adjacent) through which a No. 2 unmanned aerial vehicle passes as potential point targets to be replaced, wherein the current optimal task allocation scheme is represented as follows:
selecting a potential point target No. 4 through which the unmanned aerial vehicle No. 1 passes and a potential point target No. 4 through which the unmanned aerial vehicle No. 2 passes as replacement potential point targets (which are not adjacent), and using the potential point targets between the potential point target No. 4 through which the unmanned aerial vehicle No. 1 passes and the potential point target No. 4 through which the unmanned aerial vehicle No. 2 passes in the current optimal task allocation scheme and corresponding unmanned aerial vehicle numbers (namely, the potential point targets are the unmanned aerial vehicle numbers) (namely
) To replace the potential point target and the corresponding unmanned aerial vehicle number between the potential point target No. 4 passed by the unmanned aerial vehicle No. 1 and the potential point target No. 4 passed by the unmanned aerial vehicle No. 2 in the task allocation scheme to be updated (namely, the potential point target and the corresponding unmanned aerial vehicle number are the same)
) And thus obtaining an updated task allocation scheme:
for example, the plan benefit of the current optimal task allocation plan, the local optimal task allocation plan, or the current optimal perturbation task allocation plan can be calculated by using the formula (9):
wherein Fit is the scheme gain, w, of the current optimal task allocation scheme, the local optimal task allocation scheme or the current optimal disturbance task allocation schemeiWeight for potential point targets, p is error of sensor carried by drone, yiAnd L is the number of potential point targets contained in the current optimal task allocation scheme, the local optimal task allocation scheme or the current optimal disturbance task allocation scheme for the number of times the ith potential target is accessed.
The solution benefit of updating the task allocation solution in the set of task allocation solutions and the task allocation solution in the initial set of task allocation solutions can also be calculated using equation (9).
In an embodiment of the present invention, the perturbation policy may specifically be, for example:
one of the following four disturbance measures is taken for each unmanned aerial vehicle in the multiple unmanned aerial vehicles in sequence:
a first perturbation measure, which randomly exchanges the sequence of two potential point targets on a feasible task path of the unmanned aerial vehicle;
a second disturbance measure, wherein the sequence of at least three adjacent potential point targets passing through the feasible task path of the unmanned aerial vehicle is randomly inverted;
a third disturbance measure, namely randomly deleting a potential point target on a feasible task path of the unmanned aerial vehicle;
a fourth perturbation measure, if all potential point targets in the task execution range of the unmanned aerial vehicle are not included in the feasible task path of the unmanned aerial vehicle, randomly selecting one potential point target which is not included in the feasible task path from the task execution range and inserting the potential point target into the feasible task path; and if the feasible task path of the unmanned aerial vehicle contains all the potential point targets in the task execution range of the unmanned aerial vehicle, randomly selecting one potential point target with the maximum weight from the task execution range to insert into the feasible task path.
In an embodiment of the present invention, defining a task allocation scheme in an update task allocation scheme as an update task allocation scheme, and adjusting the update task allocation scheme or a disturbance task allocation scheme may specifically include:
deleting repeated potential point targets in the updated task allocation scheme or the disturbed task allocation scheme under the condition that the updated task allocation scheme or the disturbed task allocation scheme is judged to fail to pass the path communication verification;
and under the condition that the updated task allocation scheme or the disturbed task allocation scheme is judged to fail to pass the endurance verification, sequentially deleting at least one potential point target according to the weight of the potential point target, so that the disturbed task allocation scheme can pass the endurance verification.
In order to verify the effectiveness of the optimization method for the unmanned aerial vehicle formation patrol task allocation, the invention also provides an embodiment.
The data set shown in fig. 7 is constructed according to the distribution characteristics of the potential point targets in the unmanned aerial vehicle routing inspection task. The data set shown in fig. 7 contains 50 potential point targets, and the inspection mission described above is performed using a fixed wing drone model F-1000, which has a cruising speed of 70 km/h, assuming an average speed of 60 km/h for the mission to be performed by the drone, in view of take-off and landing; the maximum safe endurance time of the unmanned aerial vehicle is 90 minutes, the influence of uncertain factors such as wind speed and wind direction on the endurance time of the unmanned aerial vehicle is considered, and the maximum endurance time of the unmanned aerial vehicle for executing tasks is assumed to be 80 minutes; the detection error of the sensor carried by the unmanned aerial vehicle is 20 percent; the number of unmanned aerial vehicles which can be used for executing inspection tasks is the largestThe number is 5. Consider the number of drones K being 3, 4 and 5, respectively, and TmaxSet to 60min, 70min and 80min, respectively, by K and TmaxThe two combinations of (A) and (B) can be combined to obtain 9 cases, namely (K, T)max) The name of each case is shown, as represented by (3, 60) for a mission range represented by the data set shown in fig. 7, which was patrolled using 3 drones with a duration of 60 min. The results of the inspection of the task range shown in fig. 7 using the improved hybrid particle swarm simulated annealing algorithm of the present invention are shown in table 1. Wherein N isVTTotal number of potential point targets, N, for all drone visitsRTThe number of potential point targets revisited by the unmanned aerial vehicle, R is the scheme benefit of the unmanned aerial vehicle formation task allocation scheme, and the CPU is the running time, T, of the unmanned aerial vehicle formation optimal task allocation scheme obtained by the optimization method provided by the inventionETotal task execution time, R, for unmanned aerial vehicle formation to execute inspection tasksUFor unmanned aerial vehicle duration utilization ratio.
TABLE 1 unmanned aerial vehicle formation inspection task results
In the 9 cases, the proportion of the total task execution time to the endurance capacity of the unmanned aerial vehicle reaches more than 99.8%; and the running time of the algorithm does not exceed 12 seconds, namely, a high-quality multi-unmanned aerial vehicle task allocation scheme can be obtained in a short time. Therefore, as can be seen from table 1, the optimization method provided by the invention can exert the cruising ability of the unmanned aerial vehicle to the maximum extent.
Through the implementation mode, the optimization method optimizes the task allocation scheme of unmanned aerial vehicle formation under the condition that multiple constraint conditions such as the weight of a potential target, the cruising ability of the unmanned aerial vehicle, the sensor error and the like are considered, the utility of the unmanned aerial vehicle is exerted to the maximum extent, and therefore the effectiveness of the routing inspection task is improved.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (6)
1. An optimization method for unmanned aerial vehicle formation inspection task allocation is characterized by comprising the following steps:
determining a task range of formation and inspection of the unmanned aerial vehicles;
determining a target object to be inspected in the task range, wherein the target object comprises a building in an earthquake-stricken area, an expressway accident point and an electric power iron tower;
abstracting the target object into a potential point target, and acquiring coordinate information of the potential point target;
determining the weight of the potential point target according to the importance of the potential point target;
acquiring information of formation of unmanned aerial vehicles, wherein the information of the formation of the unmanned aerial vehicles comprises starting points and end points of the unmanned aerial vehicles, the number of each unmanned aerial vehicle, the maximum safe endurance time and detection errors of carried sensors;
establishing a UAVF-RTOP-RP model with income probability for the formation of the unmanned aerial vehicles for revisiable team orientation problems;
acquiring an initial task allocation scheme set of the unmanned aerial vehicle formation for executing the routing inspection task according to the coordinate information and weight of the potential point target and the information of the unmanned aerial vehicle formation by adopting a UAVF-RTOP-RP model, wherein the initial task allocation scheme set comprises a plurality of task allocation schemes, the task allocation schemes are defined as a task execution path of each unmanned aerial vehicle in the unmanned aerial vehicle formation and a corresponding unmanned aerial vehicle number, and the task execution path comprises potential point targets sequentially passed by the unmanned aerial vehicle;
acquiring a current optimal task allocation scheme of the initial task allocation scheme set by adopting an improved hybrid particle swarm simulated annealing algorithm, and then optimizing the current optimal task allocation scheme to obtain an optimal task allocation scheme of the unmanned aerial vehicle formation;
abstracting the target object into a potential point target, and acquiring coordinate information of the potential point target specifically includes:
acquiring longitude coordinates and latitude coordinates of all vertexes of the target object;
selecting a minimum longitude coordinate and a maximum longitude coordinate from all longitude coordinates, and selecting a minimum latitude coordinate and a maximum latitude coordinate from all latitude coordinates;
obtaining a rectangular target range according to the minimum longitude coordinate, the maximum longitude coordinate, the minimum latitude coordinate and the maximum latitude coordinate, where coordinates of four vertices of the rectangular target range may be expressed as:
(minimum longitude coordinate, minimum latitude coordinate), (minimum longitude coordinate, maximum latitude coordinate),
(maximum longitude coordinate, minimum latitude coordinate), (maximum longitude coordinate, maximum latitude coordinate);
acquiring coordinate information of an intersection point of two diagonal lines of the rectangular target range to serve as coordinate information of the potential point target;
the objective function of the UAVF-RTOP-RP model is expressed by the formula (1):
wherein, wiAs the weight of the potential point target, p is the error of the sensor carried by the drone, yiThe number of times that the ith potential target is accessed is L, the number of the potential point targets is L, and Max is a maximum function;
the constraint conditions of the UAVF-RTOP-RP model are expressed by formulas (2) to (6):
wherein the content of the first and second substances,
for the kth drone from the starting point to the ith potential point target,
for the kth unmanned aerial vehicle, from the ith potential point target to the destination, 0 represents the starting point of the unmanned aerial vehicle, L +1 represents the destination of the unmanned aerial vehicle, and U is the set of the unmanned aerial vehicles;
for the kth drone from the h potential point target to the ith potential point target,
for the kth unmanned aerial vehicle, from the ith potential point target to the jth potential point target, T is a set of potential point targets; y isiThe number of times the target is accessed for the ith potential point; k is the number of unmanned aerial vehicles; t is tijFor the time, T, that the unmanned plane flies from the ith potential point target to the jth potential point targetmaxThe maximum safe endurance time of the unmanned aerial vehicle is set; the formula (6) is the value of the binary decision variable when
A value of 1 indicates that the kth drone has selected a path from target i to target j, when
A value of 0 indicates that the kth drone has not selected this path.
2. The optimization method according to claim 1, wherein an improved hybrid particle swarm simulated annealing algorithm is adopted to obtain a current optimal task allocation scheme of the initial task allocation scheme set, and then the current optimal task allocation scheme is optimized to obtain an optimal task allocation scheme for the formation of the unmanned aerial vehicles, specifically comprising:
determining simulated annealing parameters and disturbance parameters, wherein the simulated annealing parameters comprise an initial temperature, a termination temperature and a cooling rate, and the disturbance parameters comprise disturbance times;
defining the initial temperature as a current temperature;
calculating the scheme income of each task allocation scheme in the initial task allocation scheme set, and defining the task allocation scheme with the maximum scheme income as the current optimal task allocation scheme;
defining the initial task allocation scheme set as a task allocation scheme set to be updated;
respectively adopting an updating strategy for each task allocation scheme in the task allocation scheme set to be updated so as to generate an updated task allocation scheme set;
judging whether the task allocation scheme which fails to pass the path communication check and the cruising ability check is included in the updated task allocation scheme set;
under the condition that the updated task allocation scheme set is judged to include the task allocation scheme which fails to pass the path communication check and/or the cruising ability check, adjusting the task allocation scheme which fails to pass the path communication check and/or the cruising ability check so that all the task allocation schemes in the task allocation scheme set can pass the path communication check and the cruising ability check;
calculating the scheme income of each task allocation scheme in the updated task allocation scheme set, and defining the task allocation scheme with the maximum scheme income in the updated task allocation scheme set as a local optimal task allocation scheme;
judging whether the scheme gain of the local optimal task allocation scheme is greater than or equal to the scheme gain of the current optimal task allocation scheme;
under the condition that the scheme benefit of the local optimal task allocation scheme is judged to be more than or equal to the scheme benefit of the current optimal task allocation scheme, replacing the current optimal task allocation scheme with the local optimal task allocation scheme so as to update the current optimal task allocation scheme;
defining the current optimal task allocation scheme as a current optimal disturbance task allocation scheme;
adopting a disturbance strategy for the current optimal disturbance task allocation scheme to generate a disturbance task allocation scheme;
judging whether the disturbance task allocation scheme can pass path communication verification and endurance verification;
under the condition that the disturbance task allocation scheme is judged to fail to pass the path communication verification and/or the cruising ability verification, adjusting the disturbance task allocation scheme so that the disturbance task allocation scheme can pass the path communication verification and the cruising ability verification;
judging whether to accept the disturbance task allocation scheme according to a Metropolis mechanism of the Metropolis of the simulated annealing algorithm; under the condition that the disturbance task allocation scheme is judged to be received, replacing the current optimal disturbance task allocation scheme with the disturbance task allocation scheme so as to update the current optimal disturbance task allocation scheme;
judging whether the number of the disturbance operations is greater than or equal to the disturbance number;
under the condition that the number of times of disturbance operation which is already carried out is judged to be more than or equal to the disturbance number of times, outputting the current optimal disturbance task allocation scheme;
respectively calculating the scheme gains of the current optimal task allocation scheme and the current optimal disturbance task allocation scheme;
judging whether the scheme gain of the current optimal disturbance task allocation scheme is larger than the scheme gain of the current optimal task allocation scheme;
under the condition that the scheme benefit of the current optimal disturbance task allocation scheme is judged to be larger than the scheme benefit of the current optimal task allocation scheme, the current optimal disturbance task allocation scheme replaces the current optimal task allocation scheme so as to update the current optimal task allocation scheme;
judging whether the current temperature is less than the termination temperature;
under the condition that the current temperature is judged to be more than or equal to the termination temperature, updating the current temperature according to the cooling rate, defining the updated task allocation scheme set as a new task allocation scheme set to be updated, so as to regenerate the updated task allocation scheme set, and further updating or not updating the current optimal task allocation scheme;
and under the condition that the current temperature is judged to be lower than the termination temperature, outputting the current optimal task allocation scheme to serve as the optimal task allocation scheme of the unmanned aerial vehicle formation.
3. The optimization method according to claim 2, wherein the step of adopting an update strategy for the task allocation schemes in the task allocation scheme set to be updated specifically comprises:
randomly selecting two potential point targets from the task allocation scheme in the task allocation scheme to be updated as potential point targets to be replaced;
randomly selecting two potential point targets from the current optimal task allocation scheme as replacement potential point targets;
replacing at least two potential point targets between two potential point targets to be replaced with at least two potential point targets between the two replacement potential point targets, thereby obtaining an updated task allocation plan.
4. The optimization method according to claim 3, wherein defining a task allocation scheme in the set of update task allocation schemes as an update task allocation scheme, and adjusting the update task allocation scheme or the perturbation task allocation scheme specifically includes:
deleting the repeated potential point target in the update task allocation scheme or the disturbance task allocation scheme under the condition that the update task allocation scheme or the disturbance task allocation scheme is judged to fail to pass the path communication check;
and under the condition that the updated task allocation scheme or the disturbed task allocation scheme is judged to fail to pass the endurance verification, sequentially deleting at least one potential point target according to the weight of the potential point target, so that the disturbed task allocation scheme can pass the endurance verification.
5. The optimization method according to claim 2, characterized in that the perturbation strategy is specifically:
one of the following four disturbance measures is taken for each unmanned aerial vehicle in the multiple unmanned aerial vehicles in sequence:
a first perturbation measure for randomly exchanging the order of the two potential point targets on the feasible task path of the unmanned aerial vehicle;
a second perturbation measure, randomly inverting the sequence of the unmanned aerial vehicle passing through at least three adjacent potential point targets on the feasible task path;
a third disturbance measure, namely randomly deleting a potential point target on a feasible task path of the unmanned aerial vehicle;
a fourth perturbation measure, if all potential point targets in a task execution range of the unmanned aerial vehicle are not included in a feasible task path of the unmanned aerial vehicle, randomly selecting one potential point target which is not included in the feasible task path from the task execution range and inserting the potential point target into the feasible task path; and if the feasible task path of the unmanned aerial vehicle contains all potential point targets in the task execution range of the unmanned aerial vehicle, randomly selecting a potential point target with the maximum weight from the task execution range to insert into the feasible task path.
6. The optimization method according to claim 2, wherein the solution benefit of the current optimal task allocation solution, the local optimal task allocation solution, or the current optimal perturbation task allocation solution is calculated using formula (7):
wherein Fit is a scheme gain of the current optimal task allocation scheme, the local optimal task allocation scheme, or the current optimal perturbation task allocation scheme, and L is the number of the potential point targets included in the current optimal task allocation scheme, the local optimal task allocation scheme, or the current optimal perturbation task allocation scheme.
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