Weak communication cluster decision-making method based on adaptive re-consensus

Abstract

The application discloses a weak communication cluster decision-making method based on adaptive re-consensus, and is applied to the technical field of unmanned system cluster decision-making. The method comprises the following steps: an unmanned aerial vehicle (UAV) individual observes an environment and collects intelligence information; the intelligence information is sent to other UAV individuals within a communication range; the UAV individual locally maintains a received information list and a list of communication objects; after cyclically receiving information, a union of successfully received information is taken as a result of an intelligence consensus phase; the UAV individual generates a bid about a specific task according to a self characteristic; the bid information of the UAV individual is sent to other individuals within the communication range; the UAV individual locally maintains a received bid set and a list of communication objects; and the UAV individual selects a task according to the bid set in combination with the intelligence consensus result. The application designs a two-phase marketization negotiation method suitable for unmanned system clusters under weak communication conditions, and strengthens the adaptability to poor communication.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned system cluster decision-making, and more particularly to a weak communication cluster decision-making method based on adaptive re-consensus. BACKGROUND

[0002] An unmanned system cluster is composed of autonomous units with certain information collection and analysis processing and action execution capabilities, and is applied to various types of boring, dangerous and complex tasks as a supplement and replacement for human labor. In practical applications, the internal coordination of the unmanned system cluster is an important guarantee for fully exerting the task capabilities of the unmanned system cluster. The cluster coordination method based on communication network information exchange is the most common and practical unmanned system cluster coordination method.

[0003] The existing communication-based unmanned system cluster coordination technology mainly has the following two types of focus: One method focuses on improving existing communication technology and network structure to improve the ability to provide reliable information exchange channels for unmanned system clusters in more severe and difficult environments. Another method adds communication constraints as additional constraints to the task constraints, and additionally limits the spatial movement and topological relationship of the unmanned system. The common feature of these two methods is to establish a stable communication support for the unmanned system cluster, and then form a coordinated task plan. However, the existing communication-based unmanned system cluster coordination technology will have a resource upper limit and cost when improving communication capabilities, and adding communication constraints as additional constraints for cluster coordination will reduce the flexibility of the cluster and the task efficiency. Therefore, how to propose a weak communication cluster decision-making method that does not require additional improvements in cluster communication capabilities and does not add additional constraints to cluster movement is a problem that needs to be solved by those skilled in the art. SUMMARY

[0004] Therefore, the present application provides a weak communication cluster decision-making method based on adaptive re-consensus. The cluster completes intelligence consensus about the task object and bidding consensus about task allocation in two stages. In the two-stage negotiation, the cluster embeds individual domain consensus rate monitoring and adjustment into the information consensus process to adapt to weak communication conditions and form a coordinated task plan with excellent performance.

[0005] To achieve the above purpose, the present application provides the following technical scheme:

[0006] A weak communication cluster decision-making method based on adaptive re-consensus includes the following steps:

[0007] S1, an unmanned aerial vehicle individual U i Observe the environment to collect intelligence information Wherein, J is the task target object of the cluster;

[0008] S2, the unmanned aerial vehicle individual U i The intelligence information J' i is sent to other unmanned aerial vehicle individuals U r existing within the maximum communication range. Where H i (t) is the set of other unmanned aerial vehicle individuals within the maximum communication range of the individual U i at time t, and I is the set of unmanned aerial vehicle individuals in the cluster.

[0009] S3, the unmanned aerial vehicle individual U i maintains the union of all received intelligence information J' r locally, and the union of all unmanned aerial vehicle individuals U r that form effective communication.

[0010] S4, the unmanned aerial vehicle individual U i After receiving information in a loop, the union of successfully received J' r is recorded as As a result of the intelligence consensus phase, enter S5.

[0011] S5, the unmanned aerial vehicle individual U i generates a bid B about i according to its own characteristics.

[0012] S6, the unmanned aerial vehicle individual U i sends its bid B i information to other individuals in H i (t).

[0013] S7, the unmanned aerial vehicle individual U i maintains the received bid B r of other individuals locally, maps it to local intelligence Y i forms a bid set R i , and forms the union of all individuals U r that form effective communication.

[0014] S8, the unmanned aerial vehicle individual U i After receiving information in a loop, select the task X i in the local intelligence Y i according to the bid set R i .

[0015] Optionally, in S2, the unmanned aerial vehicle individual U i sends the intelligence information J' i to other unmanned aerial vehicle individuals U r existing within the maximum communication range. ir The information transmission success rate is: 0≤p ir (t)≤1.

[0016] Optionally, S4 specifically refers to:

[0017] When L T <L C When β occurs, repeat S1-S3; otherwise, terminate the loop. Where L... T For J' r The length of the original received information, L, is the union of the sets. C For information J' r Consensus U r The union of the original received information lengths, where β is the expected consensus rate threshold;

[0018] The loop ends when the number of repetitions λ reaches ρ times, where ρ is the consensus threshold.

[0019] The J' successfully received by the drone in the λth loop r The union of information is denoted as As a result of the intelligence consensus phase, we move into S5.

[0020] Optional, individual U drones in S6 i B's own bid i Message sent to H i The success rate of information transmission for other individuals in (t) is: 0≤p ib (t)≤1.

[0021] Optional, S8 specifically includes:

[0022] When L' T <L' C When β occurs, repeat S5-S7; otherwise, terminate the loop. Where L' T For the bid set R i The length of the original received information, L' C For information B r Consensus U r The union of the original received information lengths, where β is the expected consensus rate threshold;

[0023] The loop ends when the number of repetitions λ reaches ρ times, where ρ is the consensus threshold.

[0024] Individual drones are determined based on the bidding set R i In local intelligence Y i Select Task X i .

[0025] Optionally, S1-S4 are the intelligence consensus stage, and S5-S8 are the bidding consensus stage.

[0026] Optionally, a cluster of individual drones I = {U i |U1,U2,U3,…,U N}, cluster task target object J = { P j |P1,P2,P3,…,P M}.

[0027] Via the technical solution, compared with the prior art, the weak communication cluster decision method based on adaptive re-consensus is provided, and has the following beneficial effects:

[0028] 1. The two-stage marketization negotiation method suitable for the unmanned system cluster under the weak communication condition is designed, which is different from the "bidding consensus" structure of the conventional marketization negotiation method, and is extended to the "intelligence consensus + bidding consensus" structure, the cluster negotiation performance under the segmented approximate perfect communication is approximated, and the cluster negotiation level under the perfect communication is approached as a whole.

[0029] 2. The threshold adaptive re-consensus mechanism is proposed, which is characterized in that the consensus rate threshold and the consensus number threshold are designed and applied, the balance between the information consistency of the cluster negotiation and the decision rate of the cluster negotiation is sought, and the overall performance of the cluster is optimized. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of the provided drawings.

[0031] Figure 1 The method flowchart of the present application;

[0032] Figure 2 The method principle diagram of the present application. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0034] The embodiment of the present application discloses a weak communication cluster decision method based on adaptive re-consensus, as shown in Figure 1 and Figure 2 , comprising the following steps:

[0035] S1, the unmanned aerial vehicle individual U i observing the environment to collect intelligence information Where J is the target object of the cluster task;

[0036] S2, Individual U-type drone i intelligence information J' i Send to other individual U drones within the maximum communication range r , Among them, H i (t) represents individual U at time t. i I represents the collection of other individual drones within its maximum communication range;

[0037] S3, Individual U-type drones i Maintain all received intelligence information locally. r The union of all individual UAVs that form effective communication. r The union of;

[0038] S4, Individual U-type drones i After receiving information in a loop, the successfully received J' r The union of sets is denoted as As a result of the intelligence consensus phase, we move to S5;

[0039] S5, Individual U-type drones i Generate information based on its own characteristics Bid B i ;

[0040] S6, Individual U-type drone i B's own bid i Message sent to H i Other individuals in (t);

[0041] S7, Individual U-type Drones i Locally maintain the bids received from other individuals (B). r Mapped to local intelligence Y i Forming the bid set R i And all individuals U that form effective communication r The union of;

[0042] S8, Individual U-type drones i After receiving information in a loop, based on the bid set R i In local intelligence Y i Select Task X i .

[0043] Furthermore, in S2, the individual U drone i intelligence information J' i Send to other individual U drones within the maximum communication range r The information transmission success rate is: 0≤pir (t)≤1.

[0044] Further, S4 is specifically:

[0045] When L T <L C ·β, repeat S1-S3, otherwise end the loop, wherein L T is the length of the original accepted information set J r , and β is the consensus rate expected threshold value. C r Further, the union of the information J r consensus is U r , and β is the consensus rate expected threshold value.

[0046] When the number of repetitions λ reaches ρ times, end the loop, wherein ρ is the consensus number threshold value.

[0047] The union of the information J r successfully received by the UAV individual in λ times of loop is denoted as As a result of the intelligence consensus phase, enter S5.

[0048] Further, in S6, the UAV individual U i sends its bid B i information to other individuals in H i (t). ib The information transmission success rate of other individuals in H i (t) is: 0≤p ib (t)≤1.

[0049] Further, S8 is specifically:

[0050] When L T <L C ·β, repeat S5-S7, otherwise end the loop, wherein L T is the length of the original accepted information set R i , and L C is the length of the original accepted information set B r , and β is the consensus rate expected threshold value. r

[0051] When the number of repetitions λ reaches ρ times, end the loop, wherein ρ is the consensus number threshold value.

[0052] The UAV individual selects the task X i from the bid set R i in the local intelligence Y i .

[0053] Further, S1-S4 is the intelligence consensus phase, and S5-S8 is the bid consensus phase.

[0054] Further, the individual cluster of unmanned aerial vehicles I = {U i |U1, U2, U3, …, U N} and the task target object J = {P j |P1, P2, P3, …, P M}.

[0055] Further, in the embodiment, the unmanned aerial vehicle can also be one of unmanned vehicle, unmanned ship and autonomous robot.

[0056] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the various embodiments can be mutually referred to.

[0057] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A weak communication cluster decision-making method based on adaptive re-consensus, characterized in that, The method comprises the following steps: S1, drone individual U i Observing the environment to gather intelligence information wherein J is a cluster task target object; S2, Individual U-type drone i intelligence information J' i Send to other individual U drones within the maximum communication range r , Among them, H i (t) represents individual U at time t. i I represents the collection of other individual drones within its maximum communication range; S3, the drone individual U i maintains all the intelligence information J received locally r and the union of all the drone individuals U that form an effective communication r ; S4, drone individual U i After the information is received in a cycle, the successfully received J r The union of the two is denoted by As a result of the information consensus phase, S5 is entered; S5, drone individual U i According to the own characteristics, the bid B of the individual U i ; S6, drone individual U i offer B i information to H i other individuals in (t); S7, a drone individual U i maintaining locally the bids B received from other individuals r mapping to local intelligence Y i forming a set of bids R i and the union of all individuals U that form an effective communication r ; S8, drone individual U i After receiving the information in a loop, according to the bid set R i In local intelligence Y i Select task X i .

2. The weak communication cluster decision-making method based on adaptive re-consensus according to claim 1, characterized in that, S2: the unmanned aerial vehicle individual U i The intelligence information j' i is sent to other unmanned aerial vehicle individuals U r existing within the maximum communication range ir The information transmission success rate is: 0 ≤ p ir (t) ≤ 1.

3. The weak communication cluster decision-making method based on adaptive re-consensus according to claim 1, characterized in that, S4 is specifically: When L T <L C • β, repeat S1-S3, otherwise end the loop, where L T is J' r the length of the original accepted information set, L C is information J' r consensus U r is the length of the original accepted information set, and β is the consensus rate expected threshold value. When the number of repetitions λ reaches ρ times, the loop is ended, wherein ρ is a consensus number threshold; The union of information formed by the successful reception of J by the drone individuals in λ cycles is denoted by r The union of information formed by the successful reception of J by the drone individuals in λ cycles is denoted by As a result of the information consensus phase, S5 is entered.

4. The weak communication cluster decision-making method based on adaptive re-consensus according to claim 1, characterized in that, S6 the drone individual U i transmits its bid B i to H i The information transmission success rate of other individuals in (t) is: 0≤p ib (t)≤1.

5. The weak communication cluster decision-making method based on adaptive re-consensus according to claim 1, characterized in that, S8 is specifically: When L' T <L' C • β, repeat S5-S7, otherwise end the loop, where L' T is the set of bids R i the original accepted message length, L' C is the message B r consensus U r the union of the original accepted message length, β is the consensus rate expected threshold; When the number of repetitions λ reaches ρ times, the loop is ended, wherein ρ is a consensus number threshold; The UAV individuals bid according to the set of bids R i In local intelligence Y i Select a task X i .

6. The weak communication cluster decision-making method based on adaptive re-consensus according to claim 1, characterized in that, S1-S4 are intelligence consensus stages, and S5-S8 are bidding consensus stages.

7. The weak communication cluster decision-making method based on adaptive re-consensus according to claim 1, characterized in that, A drone individual cluster I = {U i |U1, U2, U3, …, U N}, a cluster task target object J = {P j |P1, P2, P3, …, P M}.

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