Distributed multi-target hunting interception method with lowest unmanned aerial vehicle cluster energy

Abstract

The invention discloses a distributed multi-target hunting interception method with lowest unmanned aerial vehicle cluster energy, in the method, a specific operation process for obtaining interception energy consumption is provided, and dynamic MPT distribution can be designed by minimizing the total control cost of a pursuit group through the obtained analysis and meaningful energy consumption. Compared with a serial distribution and guidance method, the method has the advantages that optimization indexes of two stages are well unified, so that unnecessary energy consumption is avoided, and the synergistic effect is improved; in addition, the invention further provides a distributed allocation algorithm with allowable task set constraints. Due to the fact that the detection capacity of each interceptor in engineering application is generally limited, and only part of target information can be used for each interceptor, the method can well solve the actual problems in engineering, and target hunting interception is achieved with low cost.

Description

Technical Field

[0001] The present invention belongs to the technical field of low-speed, slow, and small target interception control, and in particular relates to a distributed multi-target hunting and interception method with the lowest energy of a drone cluster. Background Art

[0002] The effect of coordinated attacks by drone swarms is becoming more and more significant. In order to defend against such swarm targets, it is crucial to design a method with high interception probability and low cost. Among them, multi-interceptor-multi-target allocation and guidance strategy are the two most critical factors for successful confrontation.

[0003] The current study conducts allocation and guidance methods separately, where MPT guidance is usually implemented by solving individual interception problems and many-to-one attack problems, using various methods such as optimal control theory and nonlinear methods. It is worth noting that the optimal guidance algorithm has specific and meaningful performance indicators, so it is more suitable for practical implementation.

[0004] As a prerequisite for cooperative interception, MPT allocation can be viewed as target assignment (WTA), which has been widely studied in the past few decades. Various allocation criteria, such as target-based WTA considering kill probability, miss distance and intercept probability, and methods considering relative motion information have been developed.

[0005] In addition, WTA based on own assets extends its research to minimize asset damage and resource consumption. However, most of the above methods are modeled by assuming that the optimization variables are known in advance rather than elaborating the corresponding analytical solutions, which greatly weakens the guiding significance in implementation; some articles study empirical fitting methods, but it is difficult to generalize; in addition, most existing MPT allocation methods are based on centralized topology design, which will impose a high burden on calculation and communication.

[0006] Based on the above problems, in order to reduce the communication burden between interceptors and allow the detectors on the interceptors to only obtain information about part of the target, thereby fully reducing the interception cost and improving the practical value of the scheme, the inventors have conducted in-depth research on the interception scheme in the hope of designing a new interception control method that can solve the above problems. Summary of the invention

[0007] In order to overcome the above problems, the inventor has conducted intensive research and designed a distributed multi-target hunting and interception method with the lowest energy of drone clusters. In this method, a specific operation process for obtaining interception energy consumption is proposed. Through the analysis and meaningful energy consumption obtained, dynamic MPT allocation can be designed by minimizing the total control cost of the pursuer group. Compared with the serial allocation and guidance method, this method well unifies the optimization indicators of the two stages to avoid unnecessary energy consumption and thus improve the synergy effect; in addition, a distributed allocation algorithm with an admissible task set constraint is proposed; since the detection capability of each interceptor in engineering applications is usually limited, and only part of the target information is available for each interceptor, this method can well solve practical problems in engineering and achieve target hunting and interception at a lower cost; thus completing the present invention.

[0008] Specifically, the purpose of the present invention is to provide a distributed multi-target hunting and interception method with the lowest energy of a drone cluster, in which each drone in the drone cluster performs the following steps:

[0009] Step 1: Obtain information about the target to be intercepted that can be detected by the detection equipment on the drone;

[0010] Step 2: Obtain the interception energy consumption of each detectable target, and select the target with the minimum energy consumption, which is called the local optimal target of the UAV;

[0011] Step 3, transmitting the obtained local optimal target information to a neighboring drone, and receiving the local optimal target information transmitted by the neighboring drone; the neighboring drone refers to a friendly drone that is connected by signal and can communicate and transmit information to each other to jointly perform the interception mission;

[0012] Step 4: Based on the information obtained, a target is confirmed as the current target by comparing the energy consumption;

[0013] Step 5: Based on the current target, obtain the expected acceleration instruction of the UAV, and control the UAV to fly to the current target based on the expected acceleration instruction.

[0014] Wherein, in step 2, the interception energy consumption is obtained by the following formula (1):

[0015]

[0016] Among them, J ij represents the energy consumption required for the i-th UAV to intercept the j-th target;

[0017] N i represents the guidance parameters of the i-th UAV;

[0018] r ijrepresents the distance between the i-th UAV and the j-th target;

[0019] z ij It represents the miss distance of the i-th UAV intercepting the j-th target.

[0020] Among them, the miss distance z of the i-th UAV intercepting the j-th target is ij Obtained by the following formula (II):

[0021] z ij =r ij ·e Vij ×e Lij (two)

[0022] Among them, r ij represents the distance between the i-th UAV and the j-th target;

[0023] e Vij The unit direction vector representing the relative velocity between the i-th UAV and the j-th target;

[0024] e Lij The unit direction vector representing the direction of the line between the i-th UAV and the j-th target.

[0025] Among them, in step 4, when the local optimal target of the UAV and the local optimal target of the neighboring aircraft do not overlap, that is, they do not belong to the same target, the UAV confirms the target as the current target;

[0026] When the local optimal target of a drone overlaps with the local optimal target of its neighboring drone, that is, they belong to the same target, the energy consumption of the local optimal targets of the two drones is compared, and the drone with lower energy consumption confirms the local optimal target as the current target.

[0027] A UAV with high energy consumption will eliminate the current targets of its neighboring UAVs from the targets it detects, and then select the target with the lowest energy consumption as the current target.

[0028] Wherein, in step 5, the expected acceleration instruction is obtained by the following formula (III):

[0029] A Pij =N i ·V Rij ·Ω Lij ×e Vij +A Tj (three)

[0030] Among them, A Pij It represents the expected acceleration command when the i-th UAV takes the j-th target as the current target;

[0031] V Rijrepresents the relative speed between the i-th UAV and the j-th target;

[0032] Ω Lij represents the relative line-of-sight angular velocity between the i-th UAV and the j-th target;

[0033] A Tj represents the acceleration of the jth target.

[0034] Among them, the relative speed V between the i-th UAV and the j-th target is Rij Obtained by the following formula (IV):

[0035] V Rij =V Pi -V Tj (Four)

[0036] Among them, V Pi represents the speed of the i-th UAV;

[0037] V Tj represents the speed of the jth target.

[0038] Among them, the relative line of sight angular rate Ω between the i-th UAV and the j-th target Lij Obtained by the following formula (V):

[0039]

[0040] The method further comprises:

[0041] Step 6, repeat steps 1 to 5 in real time until all targets are intercepted.

[0042] Before the UAV executes step 1, the number of targets to be intercepted is obtained through fixed detection equipment, and then the same number of UAVs are commanded to form a cluster to hunt and intercept the targets.

[0043] The beneficial effects of the present invention include:

[0044] (1) The distributed multi-target hunting and interception method with the lowest energy for drone clusters provided by the present invention determines the optimal interception task allocation based on the screening scheme with the lowest energy consumption, realizes the optimal task allocation based on low-efficiency sensors, and improves the synergy effect by avoiding unnecessary energy consumption;

[0045] (2) According to the distributed multi-target hunting and interception method with the lowest energy of drone cluster provided by the present invention, after determining the current target, each drone is controlled to fly towards its own current target by obtaining acceleration instructions to eliminate the zero-control miss amount during collision and successfully complete the interception mission. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The overall logic diagram of the distributed multi-target hunting and interception method with the lowest energy of the drone cluster of the present application is shown;

[0047] Figure 2 The communication topology between our six drones in the embodiment is shown;

[0048] Figure 3 The interception trajectory of the aircraft in the embodiment is shown;

[0049] Figure 4 A graph showing the relative distance between each aircraft and its corresponding target UAV over time in the embodiment;

[0050] Figure 5 The acceleration variation curves of six interception drones in the embodiment are shown as a function of time;

[0051] Figure 6 The curves showing the variation of interception energy consumption over time for six interception drones in the embodiment are shown. DETAILED DESCRIPTION

[0052] The present invention will be further described in detail below through the accompanying drawings and embodiments. Through these descriptions, the characteristics and advantages of the present invention will become more clear and distinct.

[0053] The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration." Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise noted.

[0054] When intercepting enemy drone swarms or other flying targets through a drone swarm composed of multiple drones, the spatial range involved is large, and the detection range of the sensors on our drones is limited, making it difficult to cover and detect all targets. Only through a whole composed of multiple drones distributed equally widely can we obtain target information in all directions. In this case, it is necessary to set an appropriate task allocation plan and specifically control the drones to fly to the target. Based on this, the present invention provides a distributed multi-target hunting and interception method with the lowest energy of a drone swarm, such as Figure 1 As shown in , in this method, each drone in the drone cluster performs the following steps:

[0055] Step 1: Obtain information about the target to be intercepted that can be detected by the detection equipment on the drone;

[0056] The detection equipment includes equipment mounted on the UAV, and appropriate equipment can be selected for mounting according to specific mission requirements, such as an optoelectronic pod, a laser seeker, an active radar, etc.

[0057] The information of the target to be intercepted includes the position and speed information of the target, and may also include the acceleration information of the target.

[0058] When selecting detection equipment and interception drones, it is necessary to fully consider the detection range of the detection equipment and the distribution range of the target, as well as the number of interception drones dispatched to ensure that each target is ultimately assigned at least one drone and all targets can be intercepted.

[0059] Preferably, before the drone executes step 1, the number of targets to be intercepted is obtained through fixed detection equipment, and then the same number of drones are commanded to form a cluster to hunt and intercept the targets. The fixed detection equipment can be a large radar or other equipment fixed on the ground or on a carrier such as a ship, or a combination of other detection equipment that can perform large-scale target recognition.

[0060] Step 2: Obtain the interception energy consumption of each detectable target, and select the target with the minimum energy consumption, which is called the local optimal target of the UAV;

[0061] In step 2, the interception energy consumption is obtained by the following formula (1):

[0062]

[0063] Among them, J ij represents the energy consumption required for the i-th UAV to intercept the j-th target;

[0064] N i Represents the guidance parameters of the i-th UAV; it is required to satisfy N i ≥3, preferably 4;

[0065] r ij Indicates the distance between the i-th UAV and the j-th target; the target's location information is obtained through the UAV detection equipment, and the UAV's own location information is obtained through the sensors on the UAV itself, such as geomagnetic sensors, satellite receivers, etc., to obtain the distance;

[0066] z ij It represents the miss distance of the i-th UAV intercepting the j-th target.

[0067] Preferably, the miss distance z of the i-th UAV intercepting the j-th target is ij Obtained by the following formula (II):

[0068] z ij =r ij ·e Vij ×e Lij (two)

[0069] Among them, rij represents the distance between the i-th UAV and the j-th target;

[0070] e Vij Represents the unit direction vector of the relative speed between the i-th UAV and the j-th target, that is, V Rij The direction is obtained through the detection equipment on the drone;

[0071] e Lij The unit direction vector representing the direction of the line between the ith UAV and the jth target is obtained through the detection equipment on the UAV.

[0072] In this application, the symbol × represents the cross product operation symbol of two vectors.

[0073] Step 3, transmitting the obtained local optimal target information to a neighboring drone, and receiving the local optimal target information transmitted by the neighboring drone; the neighboring drone refers to a friendly drone that is connected by signal and can communicate and transmit information to each other to jointly perform the interception mission;

[0074] For each drone, its neighbors are determined by the communication topology, which is pre-set based on the hardware functions and specific working environment when the interception mission begins. However, as the mission progresses, the distance between drones will change. When the distance between two drones gets closer, two drones that originally had no communication connection can also establish contact on their own and become each other's new neighbors. As the neighbors gradually move away from each other, as the distance exceeds the communication range, the communication topology is interrupted and changed, and they are no longer neighbors.

[0075] Step 4: Based on the information obtained, a target is confirmed as the current target by comparing the energy consumption;

[0076] In step 4, when the local optimal target of the UAV does not overlap with the local optimal target of the neighboring UAV, that is, they do not belong to the same target, the UAV confirms the local optimal target as the current target;

[0077] When the local optimal target of the UAV overlaps with the local optimal target of its neighboring UAV, that is, they belong to the same target, the energy consumption of the two UAVs for the local optimal target is compared. The UAV with low energy consumption confirms the local optimal target as the current target. For the UAV with high energy consumption, after eliminating the current target of its neighboring UAV from the targets it detects, the target with the lowest energy consumption is selected as the current target.

[0078] The detection equipment on each drone can detect a certain area and lock multiple targets. Generally speaking, the number of targets that can be locked is greater than the number of neighboring drones, so as to ensure that each drone will eventually determine a current target. When an unexpected situation occurs, such as when all the targets that the drone can detect are determined as current targets by neighboring drones, the drone still determines the target identified at the last moment as the current target and continues to move towards the target.

[0079] Step 5: Based on the current target, obtain the expected acceleration instruction of the UAV, and control the UAV to fly to the current target based on the expected acceleration instruction.

[0080] In step 5, the desired acceleration command is obtained by the following formula (III):

[0081] A Pij =N i ·V Rij ·Ω Lij ×e Vij +A Tj (three)

[0082] Among them, A Pij It represents the expected acceleration command when the i-th UAV takes the j-th target as the current target;

[0083] V Rij represents the relative speed between the i-th UAV and the j-th target;

[0084] Ω Lij represents the relative line-of-sight angular velocity between the i-th UAV and the j-th target;

[0085] A Tj Represents the acceleration of the jth target. These relative speeds, relative line of sight angular rates, and acceleration information are all obtained based on the detection equipment.

[0086] Preferably, the relative speed V between the i-th UAV and the j-th target is Rij Obtained by the following formula (IV):

[0087] V Rij =V Pi -V Tj (Four)

[0088] Among them, V Pi represents the speed of the i-th UAV;

[0089] V Tj represents the speed of the jth target.

[0090] Preferably, the relative line-of-sight angular velocity Ω between the i-th UAV and the j-th target Lij Obtained by the following formula (V):

[0091]

[0092] In a preferred embodiment, the method further comprises:

[0093] Step 6, repeat steps 1 to 5 in real time until all targets are intercepted.

[0094] In the process of repeating steps 1 to 5 in real time, as the actual environment changes, the positions of the target and the drone are constantly changing, and the current target confirmed by the drone can also change accordingly. This setting can ensure the integrity of the target interception and ensure that all targets are effectively intercepted.

[0095] Example

[0096] Set a mission scenario where a cluster of six UAVs intercepts six enemy UAVs. The initial position coordinates and initial moving speed information of the cluster of six UAVs and the enemy UAVs are as shown in the following table. The target moves at a constant speed.

[0097]

[0098] In the table, P1 to P6 represent our six drones, and T1 to T6 represent the enemy's six drones.

[0099] The communication topology between our six drones is as follows: Figure 2 As shown in

[0100] At the beginning of the interception mission, the target reachability matrix of the interceptor is:

[0101]

[0102] This matrix indicates that the first friendly drone P1 can obtain the first to fourth target drones, namely T1, T2, T3, and T4, through detection equipment;

[0103] The second friendly drone P2 can detect the third to sixth target drones, namely T3, T4, T5, and T6, through detection equipment;

[0104] The third UAV P3 can detect the fourth to sixth target UAVs, namely T4, T5, and T6, through detection equipment;

[0105] The fourth friendly drone P4 can obtain the first to fifth target drones, namely T1, T2, T3, T4, and T5, through detection equipment;

[0106] The fifth friendly drone P5 can obtain the second to sixth target drones, namely T2, T3, T4, T5, and T6, through detection equipment;

[0107] The sixth friendly drone P6 can detect the third to sixth target drones, namely T3, T4, T5, and T6, through detection equipment;

[0108] Perform the following interception steps on each of our drones:

[0109] Step 1: Obtain information about the target to be intercepted that can be detected by the detection equipment on the drone, including the target's position, speed information, and acceleration information, where the acceleration information is 0.

[0110] Step 2: Obtain the interception energy consumption of each detectable target, and select the target with the minimum energy consumption, which is called the local optimal target of the UAV;

[0111] In step 2, the interception energy consumption is obtained by the following formula (1):

[0112]

[0113] Among them, J ij represents the energy consumption required for the i-th UAV to intercept the j-th target;

[0114] N i represents the guidance parameter of the i-th UAV, and its value is 4;

[0115] r ij represents the distance between the i-th UAV and the j-th target;

[0116] z ij It represents the miss distance of the i-th UAV intercepting the j-th target, which is obtained by the following formula (II):

[0117] z ij =r ij ·e Vij ×e Lij (two)

[0118] Among them, r ij represents the distance between the i-th UAV and the j-th target;

[0119] e Vij Represents the unit direction vector of the relative speed between the i-th UAV and the j-th target, that is, V Rij direction;

[0120] e Lij The unit direction vector representing the relative line-of-sight angular velocity between the i-th UAV and the j-th target.

[0121] Step 3, transmitting the obtained local optimal target information to the neighboring machine, and receiving the local optimal target information transmitted by the neighboring machine to the neighboring machine;

[0122] Step 4: Based on the information obtained, each drone confirms a target as the current target by comparing the energy consumption;

[0123] In step 4, when the local optimal target of the UAV does not overlap with the local optimal target of the neighboring UAV, that is, they do not belong to the same target, the UAV confirms the local optimal target as the current target;

[0124] When the local optimal target of the UAV overlaps with the local optimal target of its neighboring UAV, that is, they belong to the same target, the energy consumption of the two UAVs for the local optimal target is compared. The UAV with low energy consumption confirms the local optimal target as the current target. For the UAV with high energy consumption, after eliminating the current target of its neighboring UAV from the targets it detects, the target with the lowest energy consumption is selected as the current target.

[0125] The specific current target allocation scheme is: S = [1, 3, 6, 2, 4, 5] T

[0126] That is, our drone P1 confirms that the target drone T1 is the current target; our drone P2 confirms that the target drone T3 is the current target; our drone P3 confirms that the target drone T6 is the current target; our drone P4 confirms that the target drone T2 is the current target; our drone P5 confirms that the target drone T4 is the current target; our drone P6 confirms that the target drone T5 is the current target;

[0127] Step 5: Based on the current target, obtain the expected acceleration instruction of the UAV, and control the UAV to fly to the current target based on the expected acceleration instruction.

[0128] In step 5, the desired acceleration command is obtained by the following formula (III):

[0129] A Pij =N i ·V Rij ·Ω Lij ×e Vij +A Tj (three)

[0130] Among them, A Pij It represents the expected acceleration command when the i-th UAV takes the j-th target as the current target;

[0131] V Rij represents the relative speed between the i-th UAV and the j-th target;

[0132] A Tjrepresents the acceleration of the jth target;

[0133] Ω Lij The relative line-of-sight angular velocity between the i-th UAV and the j-th target is obtained by the following formula (5):

[0134]

[0135] Step 6, repeat steps 1 to 5 in real time until all targets are intercepted;

[0136] During the execution of step 6, the current target confirmed by each drone does not change, and each drone eventually intercepts the corresponding target drone.

[0137] The simulation results are as follows Figures 3 to 6 as shown in .

[0138] Figure 3 shows the intercept trajectory of the aircraft, Figure 4 A graph showing the relative distance between each aircraft and its corresponding target UAV over time is shown;

[0139] from Figure 3 and Figure 4 It can be seen that under the control and allocation of the distributed multi-target hunting and interception method with the lowest energy of the drone cluster provided by the present invention, the intercepting drone cluster obtains the optimal allocation plan, and each drone successfully turns and intercepts the target drone; the allocation result satisfies the local target information constraint.

[0140] Figure 5 The acceleration curves of six intercepting drones versus time are shown; Figure 6 The curves of interception energy consumption of six interception UAVs varying with time are shown;

[0141] from Figure 5 and 6 It can be seen that the acceleration command and interception energy consumption of each interceptor have the same convergence, which is because the interception energy consumption is composed of distance-weighted energy, that is, quadratic acceleration. Therefore, it is reasonable and feasible to minimize the interception energy consumption as the optimization index of allocation. The analytical interception energy consumption obtained in real time in this application can promote allocation so that the optimal solution can be easily generated when unexpected disturbances occur.

[0142] The present invention has been described above in conjunction with preferred embodiments, but these embodiments are only exemplary and serve only as an illustration. On this basis, the present invention may be subjected to a variety of substitutions and improvements, all of which fall within the scope of protection of the present invention.

Claims

1. A distributed multi-target hunting and interception method with the lowest energy of drone cluster, characterized in that: In this method, each drone in the drone cluster performs the following steps: Step 1: Obtain information about the target to be intercepted that can be detected by the detection equipment on the drone; Step 2: Obtain the interception energy consumption of each detectable target, and select the target with the minimum energy consumption, which is called the local optimal target of the UAV; Step 3, transmitting the obtained local optimal target information to a neighboring drone, and receiving the local optimal target information transmitted by the neighboring drone; the neighboring drone refers to a friendly drone that is connected by signal and can communicate and transmit information to each other to jointly perform the interception mission; Step 4, confirming a target as the current target based on the obtained information; Step 5: Based on the current target, obtain the expected acceleration instruction of the UAV, and control the UAV to fly to the current target based on the expected acceleration instruction.

2. The distributed multi-target hunting and interception method with the lowest energy of drone cluster according to claim 1 is characterized in that: In step 2, the interception energy consumption is obtained by the following formula (1): Among them, J ij represents the energy consumption required for the i-th UAV to intercept the j-th target; N i represents the guidance parameters of the i-th UAV; r ij represents the distance between the i-th UAV and the j-th target; z ij It represents the miss distance of the i-th UAV intercepting the j-th target.

3. The distributed multi-target hunting and interception method with the lowest energy of drone cluster according to claim 2 is characterized in that: The miss distance z of the i-th UAV intercepting the j-th target ij Obtained by the following formula (II): z ij =r ij ·e Vij ×e Lij (two) Among them, r ij represents the distance between the i-th UAV and the j-th target; e Vij The unit direction vector representing the relative velocity between the i-th UAV and the j-th target; e Lij The unit direction vector representing the direction of the line between the i-th UAV and the j-th target.

4. The distributed multi-target hunting and interception method with the lowest energy of drone cluster according to claim 1 is characterized in that: In step 4, when the local optimal target of the drone does not overlap with the local optimal target of the neighboring drone, that is, they do not belong to the same target, the drone confirms the local optimal target as the current target; When the local optimal target of the UAV overlaps with the local optimal target of its neighboring UAV, that is, they belong to the same target, the energy consumption of the two UAVs for the local optimal target is compared. The UAV with low energy consumption confirms the local optimal target as the current target. For the UAV with high energy consumption, after eliminating the current target of its neighboring UAV from the targets it detects, the target with the lowest energy consumption is selected as the current target.

5. The distributed multi-target hunting and interception method with the lowest energy of drone cluster according to claim 1 is characterized in that: In step 5, the desired acceleration command is obtained by the following formula (III): A Pij = N i ·V Rij ·Ω Lij × e Vij + A Tj (Three) Among them, A Pij It represents the expected acceleration command when the i-th UAV takes the j-th target as the current target; V Rij represents the relative speed between the i-th UAV and the j-th target; Ω Lij represents the relative line-of-sight angular velocity between the i-th UAV and the j-th target; A Tj represents the acceleration of the jth target.

6. The distributed multi-target hunting and interception method with the lowest energy of drone cluster according to claim 5 is characterized in that: The relative speed V between the i-th UAV and the j-th target Rij Obtained by the following formula (IV): V Rij = V Pi -V Tj (IV) Among them, V Pi represents the speed of the i-th UAV; V Tj represents the speed of the jth target.

7. The distributed multi-target hunting and interception method with the lowest energy of drone cluster according to claim 5 is characterized in that: The relative line-of-sight angular velocity Ω between the i-th UAV and the j-th target Lij Obtained by the following formula (V):

8. The distributed multi-target hunting and interception method with the lowest energy of drone cluster according to claim 1 is characterized in that: The method further includes: Step 6, repeat steps 1 to 5 in real time until all targets are intercepted.

9. The distributed multi-target hunting and interception method with the lowest energy of drone cluster according to claim 1 is characterized in that: Before the UAV executes step 1, the number of targets to be intercepted is obtained through fixed detection equipment, and then the same number of UAVs are commanded to form a cluster to hunt and intercept the targets.

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