A method and apparatus for autonomous auction of additional search tasks by drone swarms

Abstract

This application provides a method and apparatus for autonomous auctioning new search tasks in a drone swarm. The method includes: step 1, numbering each member of the drone swarm within the range of 1 to M; step 2, loading the initial task area location database {D}. j},j=1,…,N;Step 3, Load the takeoff point longitude TOLon i Step 4: Load the bidding time threshold THSubmit and the decision time threshold THDecide; Step 5: If a new task region D is received... N+1 If the information is available, then the bid price (Price) will be calculated. i And send it to other drone members in the drone swarm; Step 6, if the time to enter Step 5 is greater than the bidding time threshold THSubmit, then based on the bids of the local drone and other drone members in the swarm {Price}... j}, j=1,…,M make preliminary decisions, and the preliminary decision results are Result i Send to other drone members in the group; Step 7, for each member i (i = 1, ..., M) in the drone group, if the time to enter step 6 is greater than the decision time threshold THDecide, then based on the preliminary decision results received from the local machine and other drone members in the group {Result j The final decision is made by considering the groups},j=1,…,M.

Description

Technical Field

[0001] This invention pertains to flight control technology, specifically relating to a method and apparatus for autonomous auctioning of new search tasks by a swarm of unmanned aerial vehicles (UAVs). Background Technology

[0002] For drone swarms, comprehensive search of unknown areas is a common mission requirement. Drones gain a complete understanding of the environmental situation and enemy deployments through thorough reconnaissance within the mission area. In this scenario, drones lack prior knowledge, and mission allocation can be viewed as a problem of drone resource optimization and mission scheduling. If new missions are added to the drone swarm after initial mission allocation, online reallocation is necessary.

[0003] Autonomous task auction for drone swarms is a distributed task allocation algorithm, particularly suitable for online allocation of new search tasks. After the auction begins, members of the drone swarm can decide which member will undertake the task through price comparison and negotiation. Compared to centralized task allocation methods, this method has the advantage of distributing computational demands across various onboard computing resources (such as the onboard computers of each drone), improving computational efficiency, and is especially suitable for online task allocation in large-scale drone swarms.

[0004] However, this method also has drawbacks in practical applications. The most significant is its high requirement for inter-drone communication quality. If data loss or delay occurs, it can easily lead to inconsistent decision-making among drone members, potentially causing serious consequences such as swarm chaos. For example, both drones A and B are capable of undertaking a new task, and drone B is better suited for it. However, due to communication issues, drone A did not receive a bid from drone B and mistakenly believes it should undertake the task. This results in a decision-making inconsistency between drones A and B, potentially leading to a collision between them. Therefore, it is necessary to design a method for autonomous auctioning of new search tasks by drone swarms to ensure decision-making consistency. Summary of the Invention

[0005] The purpose of this invention is to provide a method and apparatus for autonomous auctioning of new search tasks by unmanned aerial vehicle (UAV) swarms, which introduces a bidding time threshold and a decision time threshold, as well as a dual decision-making mechanism, to overcome the problem of inconsistent decisions caused by communication data delays and loss in existing task auction methods in practical applications.

[0006] The technical solution of this invention:

[0007] Firstly, this application provides a method for autonomously auctioning new search tasks in a drone swarm, the method including:

[0008] Step 1: Number each member of the drone swarm within the range of 1 to M; where M is the number of drone members.

[0009] Step 2: For each member i (i = 1, ..., M) in the drone swarm, load the initial task area location database {D}. j},j=1,…,N; where N is the number of initial task regions;

[0010] Step 3: For each member i (i = 1, ..., M) in the drone swarm, load the longitude of the takeoff point using TOLon. i Latitude of takeoff point TOLat i Optimal cruising speed V i Load scanning field of view width W i Member load capacity C i Member initial task sequence T i ;

[0011] Step 4: For each member i (i = 1, ..., M) in the drone swarm, load the bidding time threshold THSubmit and the decision time threshold THDecide;

[0012] Step 5: For each member i (i = 1, ..., M) in the drone swarm, if a new task area D is received... N+1 If the information is available, the member will be evaluated based on their capabilities to determine if they are qualified to undertake the task. If qualified, the bid price will be calculated. i And send it to other drone members in the drone swarm;

[0013] Step 6: For each member i (i = 1, ..., M) in the drone swarm, if the time to enter Step 5 is greater than the bidding time threshold THSubmit, then based on the bids {Price} of the local drone and the received bids of other drone members in the swarm... j}, j = 1, ..., M makes a preliminary decision and sends the preliminary decision result Result i Send to other drone members in the group; if no bid is received from drone member k, then send their bid price (Price). k Set to positive infinity;

[0014] Step 7: For each member i (i = 1, ..., M) in the drone swarm, if the time to enter Step 6 is greater than the decision time threshold TWDecide, then based on the preliminary decision results received from the local drone and other drone members in the swarm, {Result} j}, j=1,…,M to make the final decision; where, if the preliminary decision result of a certain drone member k is not received, then the preliminary decision result Result is used. k Set to 0.

[0015] Furthermore, in step 5, the bid price is calculated. i The process includes:

[0016] Step 51: For each original and newly added task region D j j = 1, ..., N+1, based on the latitude and longitude of the takeoff point (TOLon) i ,TOLat i Let D be the origin of the coordinate system. j The latitude and longitude of each vertex k Coordinates converted to geodetic coordinate system

[0017] Step 52: For each original and newly added task region D j j = 1, ..., N+1, based on coordinates in the geodetic coordinate system Calculate D j area DS j ;

[0018] Step 53: For each original and newly added task region D j ,j=1,…,N+1, calculate D j The coordinates of the center of gravity in the geodetic coordinate system (DGX) j DGY j ):

[0019]

[0020] Step 54: Pre-set the first new task sequence The centroid matrix and area matrix of the task region are formed based on the task sequence number in the task sequence.

[0021] Step 55: Based on the task region area matrix and the task region centroid matrix, calculate the task sequence T according to the following formula. i1 The time required, i.e., the bid price i (1):

[0022]

[0023] Step 56: Repeat steps 54 and 55, inserting the newly added task number N+1 into the initial task sequence T respectively. i The 2nd to TN i Before this task, the 2nd to TN were formed. i A new task sequence for calculating the bid price. i (2) ~ Price i (TN i ), and inserted into the TNth i After the first task, the TN is formed.i +1 new task sequence for calculating bid price i (TN i +1); if TN i If the value is 0, then the new task sequence number N+1 is directly inserted into the initial task sequence T. i The first item is sufficient;

[0024] Step 57: Compare bids {Price} i (j), j = 1, ..., TN i +1}, select the minimum value as the bid price for this member. i Record the corresponding task sequence as the optimal task sequence TB. i .

[0025] Furthermore, step 52 includes:

[0026] Step 521: Based on the coordinates in the geodetic coordinate system Obtain the matrix

[0027]

[0028] Step 522: According to DM j calculate

[0029]

[0030] Step 523: According to Calculate D j area DS j ,

[0031] Furthermore, step 54 includes:

[0032] Step 541: Based on each original and newly added task region D j area DS j and Form the task area matrix

[0033] Step 542: Based on each original and newly added task region D j The coordinates of the center of gravity in the geodetic coordinate system (DGX) j DGY j )and Forming a centroid matrix of the task area

[0034]

[0035] Furthermore, the preliminary decision-making method in step 6 is as follows: Select the first value among j = 1, ..., M that makes {Price} j The smallest j is denoted as the preliminary decision result. i That is, the drone member number that makes the lowest bid by drone member i.

[0036] Furthermore, the final decision-making method in step 7 is: comparing {Result} j}, j=1,…,M, if non-zero results exist and are the same, the decision is considered consistent and is taken as the final decision result; if non-zero results exist and are different, the smallest non-zero result is taken as the final decision result; if no non-zero result exists, the decision is considered to have failed; compare the UAV number with the final decision result, if they are the same, the UAV member is considered to have won the auction for this task, and the optimal task sequence TB recorded in step 5 is executed subsequently. i .

[0037] Furthermore, in step 2:

[0038] The j-th initial task region D j A polygon with a number of sides of not less than 3 and not more than 8 is represented as follows: The above For task area D j The position of the kth vertex, specifically including the vertex longitude. and latitude The DN j For task area D j The number of vertices, with values ​​ranging from [3, 8].

[0039] Furthermore, in step 3:

[0040] The load capacity C of the i-th member i The value ranges from 1 to 2. 1 indicates that the payload has the ability to search for vehicles and larger targets, while 2 indicates that the payload has the ability to search for personnel and larger targets.

[0041] The initial task sequence T of the i-th member i It consists of several tasks, represented as The above Let j be the area number of the j-th task undertaken by drone member i. The third task undertaken by member 1 of the drone is mission area D2; the TN i The number of mission areas undertaken by drone member i, which can be 0.

[0042] Furthermore, in step 4:

[0043] The bidding time threshold THSubmit is equal to the sum of the calculation period, the inter-machine communication period, and the maximum inter-machine communication delay × 2.

[0044] The decision time threshold TWDecide is equal to the sum of the inter-machine communication period and the maximum inter-machine communication delay × 2.

[0045] Secondly, this application provides a device for autonomous auction of new search tasks for unmanned aerial vehicle (UAV) swarms, the device being used to implement the above-mentioned method for autonomous auction of new search tasks for UAV swarms.

[0046] This invention has the following technical features:

[0047] (1) For new search tasks, the drone swarm can allocate tasks through autonomous auction, and obtain consistent auction results without human intervention.

[0048] (2) The following measures are used to ensure consistency in the results of autonomous auctions among drone swarm members: introduce bidding time thresholds and decision time thresholds, requiring drones to reach the time thresholds before proceeding to the next step, thus eliminating the impact of communication delays and asynchrony; introduce a dual decision-making mechanism to resolve inconsistencies in preliminary decisions caused by communication delays and loss through final decision-making.

[0049] (3) This method is simple to implement, easy to operate, and has practical application value. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a method for autonomously auctioning new search tasks by a drone swarm, as provided by the present invention. Detailed Implementation

[0051] This invention proposes a method for autonomously auctioning new search tasks by a drone swarm. It introduces bidding time thresholds and decision time thresholds, as well as a dual decision-making mechanism, to overcome the inconsistency in decision-making caused by communication data delays and loss in existing task auction methods in practical applications. The technical solution of this invention will be described in detail below with reference to the accompanying drawings.

[0052] like Figure 1 As shown, this application provides a method for autonomously auctioning new search tasks by a drone swarm, including:

[0053] Step 1: Number each member of the drone swarm within the range of 1 to M; where M is the number of drone members.

[0054] Step 2: For each member i (i = 1, ..., M) in the drone swarm, load the initial task area location database {D}. j},j=1,…,N; where N is the number of initial task regions.

[0055] The j-th initial task region D j A polygon with a number of sides of not less than 3 and not more than 8 is represented as follows: The above For task area D j The position of the kth vertex, specifically including the vertex longitude. and latitude The DN j For task area D j The number of vertices, with values ​​ranging from [3, 8].

[0056] Step 3: For each member i (i = 1, ..., M) in the drone swarm, load the longitude of the takeoff point using TOLon. i Latitude of takeoff point TOLat i Optimal cruising speed V i Load scanning field of view width W i Member load capacity C i Member initial task sequence T i .

[0057] The load capacity C of the i-th member i The value ranges from 1 to 2. 1 indicates that the payload has the ability to search for vehicles and larger targets, while 2 indicates that the payload has the ability to search for personnel and larger targets.

[0058] The initial task sequence T of the i-th member i It consists of several tasks, represented as The above Let j be the area number of the j-th task undertaken by drone member i. The third task undertaken by member 1 of the drone is mission area D2; the TN i The number of mission areas undertaken by drone member i, which can be 0.

[0059] Step 4: For each member i (i = 1, ..., M) in the drone swarm, load the bidding time threshold THSubmit and the decision time threshold THDecide.

[0060] The bidding time threshold THSubmit should be equal to the sum of the calculation period, the inter-machine communication period, and the maximum inter-machine communication delay × 2. The decision time threshold THDecide should be equal to the sum of the inter-machine communication period and the maximum inter-machine communication delay × 2. The calculation period is 0.01s, and the inter-machine communication period and the maximum inter-machine communication delay are determined by the communication equipment and are generally 0.1s.

[0061] Step 5: For each member i (i = 1, ..., M) in the drone swarm, if a new task area D is received... N+1 If the information is available, the member will be evaluated based on their capabilities to determine if they are qualified to undertake the task. If qualified, the bid price will be calculated. i And send it to other drone members in the drone swarm.

[0062] The newly added task area D N+1 The information comes from ground stations, including the payload capacity requirements of newly added mission areas. N+1 and vertex set The CN N+1 The value can be 1 or 2, where 1 indicates that the area requires the drone's payload to be capable of searching for vehicles and larger targets, and 2 indicates that the area requires the drone's payload to be capable of searching for personnel and larger targets; For task area D N+1 The position of the kth vertex, specifically including the vertex longitude. and latitude The DN N+1 For task area D N+1 The number of vertices, with values ​​ranging from [3, 8].

[0063] The capabilities of this member are derived from the onboard computer of this UAV member, including the member's flight capability and mission capability; wherein, the flight capability takes a value of 0 or 1, where 0 represents no flight capability and 1 represents flight capability; the mission capability takes a value of 0 or 1, where 0 represents no mission capability and 1 represents mission capability.

[0064] The method for evaluating whether a member is capable of undertaking the task is as follows: if the member possesses flight capability, mission capability, and payload capacity C... i Not less than the payload capacity requirement of the newly added mission area (CN) N+1 If the member is deemed capable of undertaking the new task, then the member is deemed not capable of undertaking the new task.

[0065] The bid price i This refers to the time required for member i to complete both the original and new tasks.

[0066] Specifically, step 5 involves calculating the bid price. i The process includes:

[0067] Step 51: For each original and newly added task region D j j = 1, ..., N+1, based on the latitude and longitude of the takeoff point (TOLon) i ,TOLati Let D be the origin of the coordinate system. j The latitude and longitude of each vertex k Coordinates converted to geodetic coordinate system

[0068] Step 52: For each original and newly added task region D j j = 1, ..., N+1, based on coordinates in the geodetic coordinate system Calculate D j area DS j ;

[0069] More specifically, step 52 includes:

[0070] Step 521: Based on the coordinates in the geodetic coordinate system Obtain the matrix

[0071]

[0072] Step 522: According to DM j calculate

[0073]

[0074] Step 523: According to Calculate D j area DS j ,

[0075] Step 53: For each original and newly added task region D j ,j=1,…,N+1, calculate D j The coordinates of the center of gravity in the geodetic coordinate system (DGX) j DGY j ):

[0076]

[0077] Step 54: Pre-set the first new task sequence The centroid matrix and area matrix of the task region are formed based on the task sequence number in the task sequence.

[0078] Among them, the first new task sequence is set in advance. The method is as follows:

[0079] Insert the new task sequence number N+1 into the initial task sequence T. i Before the first task in the process.

[0080] More specifically, step 54 includes:

[0081] Step 541: Based on each original and newly added task region D j area DS j and Form the task area matrix

[0082] Step 542: Based on each original and newly added task region D j The coordinates of the center of gravity in the geodetic coordinate system (DGX) j DGY j )and Forming a centroid matrix of the task area

[0083]

[0084] Step 55: Based on the task region area matrix and the task region centroid matrix, calculate the task sequence T according to the following formula. i1 The time required, i.e., the bid price i (1):

[0085]

[0086] Step 56: Repeat steps 54 and 55, inserting the newly added task number N+1 into the initial task sequence T respectively. i The 2nd to TN i Before this task, the 2nd to TN were formed. i A new task sequence for calculating the bid price. i (2) ~ Price i (TN i ), and inserted into the TNth i After the first task, the TN is formed. i +1 new task sequence for calculating bid price i (TN i +1); if TN i If the value is 0, then the new task sequence number N+1 is directly inserted into the initial task sequence T. i The first item is sufficient;

[0087] Step 57: Compare bids {Price} i (j), j = 1, ..., TN i +1}, select the minimum value as the bid price for this member. i Record the corresponding task sequence as the optimal task sequence TB. i .

[0088] Step 6: For each member i (i = 1, ..., M) in the drone swarm, if the time to enter Step 5 is greater than the bidding time threshold THSubmit, then based on the bids {Price} of the local drone and the received bids of other drone members in the swarm... j}, j = 1, ..., M makes a preliminary decision and sends the preliminary decision result Result i Send to other drone members in the group; if no bid is received from drone member k, then send their bid price (Price). k Set to positive infinity.

[0089] The preliminary decision-making method is as follows: Select the first value among j = 1, ..., M that makes {Price} j The smallest j is denoted as the preliminary decision result. i That is, the drone member number that makes the lowest bid by drone member i.

[0090] Step 7: For each member i (i = 1, ..., M) in the drone swarm, if the time to enter Step 6 is greater than the decision time threshold TWDecide, then based on the preliminary decision results received from the local drone and other drone members in the swarm, {Result} j}, j=1,…,M to make the final decision; where, if the preliminary decision result of a certain drone member k is not received, then the preliminary decision result Result is used. k Set to 0.

[0091] The final decision-making method is as follows: compare {Result} j}, j=1,…,M, if non-zero results exist and are the same, the decision is considered consistent and is taken as the final decision result; if non-zero results exist and are different, the smallest non-zero result is taken as the final decision result; if no non-zero result exists, the decision is considered to have failed; compare the UAV number with the final decision result, if they are the same, the UAV member is considered to have won the auction for this task, and the optimal task sequence TB recorded in step 5 is executed subsequently. i .

[0092] Example 1

[0093] Step 1: M=3, and the drones are numbered 1 to 3.

[0094] Step 2: N=0, the initial task area location database is empty.

[0095] Step 3: TOLon i =108.60093709°, TOLat i =34.15587989°, V i =20m / s, Wi = 250m, C1 = 1, C2 = 2, C3 = 2, the initial task sequence T i is empty.

[0096] Step 4: THSubmit = 0.31s, THDecide = 0.3s.

[0097] Step 5: The new task area is D1, CN1 = 2, D1 is a hexagon, and the vertices DP1 k (k = 1,..., 6) longitude and latitude are shown in the following table:

[0098]

[0099]

[0100] Member 1 has flight ability and task ability, but C1 < CN1 and cannot undertake task D1, so it cannot send a tender bid.

[0101] Member 2 has flight ability and task ability, and C2 ≥ CN1, so it can undertake task D1. The process of calculating the tender bid is as follows:

[0102]

[0103] DS1 = 523620.45

[0104] DGX1 = 769.15, DGY1 = 7060.13

[0105] Price2(1) = 459.82

[0106] Price2 = 459.82

[0107] Member 2 sends the tender bid Price2 = 459.82 to other members in the UAV group.

[0108] The judgment and calculation processes of Member 3 and Member 2 are similar, and Member 3 sends the tender bid Price3 = 459.82 to other members in the UAV group.

[0109] Step 6: For Members 1 - 3, Price1 = +∞, Price2 = 459.82, Price3 = 459.82, and the preliminary decision result Result1 = 2.

[0110] Step 7: For Members 1 - 3, Result1 = 2, Result2 = 2, Result3 = 2. It is determined that the decision results are consistent. Member 2 undertakes the new task D1, and Members 1 and 3 do not undertake the new task D1.

[0111] Example 2

[0112] Steps 1-5: Same as in Example 1.

[0113] Step 6: For members 1 and 2, Price1 = +∞, Price2 = 459.82, Price3 = 459.82, preliminary decision result Result1 = 2; For member 3, due to communication failure, the bid price of member 2 was not received, Price1 = +∞, Price2 = +∞, Price3 = 459.82, preliminary decision result Result1 = 3.

[0114] Step 7: For members 1 to 3, Result1 = 2, Result2 = 2, Result3 = 3. The decision results are inconsistent. The final decision result is 2. Member 2 will take on the new task D1, while members 1 and 3 will not take on the new task D1.

Claims

1. A method for autonomously auctioning new search tasks in a drone swarm, characterized in that the method... include: Step 1, position each member of the drone swarm at 1~ M Numbering is performed within the specified range; wherein, the... M Number of drone crew members; Step 2, for each member of the drone swarm Load the initial task area location database ; wherein, the N This represents the initial number of task regions; Step 3, for each member of the drone swarm Loading takeoff point longitude Latitude of takeoff point Optimal cruising speed Load scanning field of view width Member load capacity Member initial task sequence ; Step 4, for each member of the drone swarm Load the bidding time threshold THSubmit and the decision time threshold THDecide; The bidding time threshold THSubmit is equal to the sum of the calculation period, the inter-machine communication period, and the maximum inter-machine communication delay × 2. The decision time threshold TWDecide is equal to the sum of the inter-machine communication period and the maximum inter-machine communication delay × 2. Step 5, for each member of the drone swarm If a new task area is received If the information is available, the member will be evaluated based on their capabilities to determine if they are qualified to undertake the task. If they are qualified, the bid price will be calculated. And send it to other drone members in the drone swarm; Step 6, for each member of the drone swarm If the time to enter step 5 exceeds the bidding time threshold THSubmit, then the bidding will be based on the bids received from the local drone and other drone members in the group. Make preliminary decisions and present the preliminary decision results. Send to other drone members in the group; if a drone member does not receive the message, the message will be sent to them. k If the bid price is [amount], then [the bid price] will be [amount]. Set to positive infinity; Step 7, for each member of the drone swarm If the time to enter step 6 exceeds the decision time threshold TWDecide, then the decision will be based on the preliminary decision results received from the local drone and other drone members in the group. To make a final decision; among other things, if no information is received from a certain drone member k The preliminary decision results, then the preliminary decision results Set to 0; Step 5: Calculating the bid price The process includes: Step 51: For each original and newly added task area Based on the latitude and longitude of the takeoff point Let the origin be the coordinate system. each vertex k latitude and longitude Coordinates converted to geodetic coordinate system ; Step 52: For each original and newly added task area According to coordinates in the geodetic coordinate system ,calculate area ; Step 53: For each original and newly added task area ,calculate The coordinates of the center of gravity in the geodetic coordinate system : ； Step 54: Pre-set the first new task sequence The centroid matrix and area matrix of the task region are formed based on the task number in the task sequence. Step 55: Based on the task region area matrix and the task region centroid matrix, calculate the task sequence according to the following formula. The time required, i.e., the bid price : ； Step 56: Repeat steps 54 and 55, respectively, adding the new task number. Insert into the initial task sequence The 2nd in Before the first task, form the second~ A new task sequence for calculating bid prices. ~ , and insert into the first After the first task, the second task is formed. A new task sequence for calculating bid prices. ;like Then directly add the new task number. Insert into the initial task sequence The first item is sufficient; Step 57: Compare bids The minimum value among them is selected as the bid price for this member. Record the corresponding task sequence as the optimal task sequence. ; Step 52 includes: Step 521: Based on the coordinates in the geodetic coordinate system , to obtain the matrix ； Step 522: According to calculate ； Step 523: According to ,calculate area , ; Step 54 includes: Step 541: Based on each original and newly added task area area and Forming a task area matrix ; Step 542: Based on each original and newly added task area The coordinates of the center of gravity in the geodetic coordinate system and Forming a centroid matrix of the task area 。 2. The method according to claim 1, characterized in that, The preliminary decision-making method in step 6 is as follows: Choose the first one smallest j Record as preliminary decision results drone crew i The drone member number that received the lowest bid was selected.

3. The method according to claim 1, characterized in that, The final decision-making method in step 7 is: comparison If non-zero results exist and are identical, then the decisions are considered consistent and are taken as the final decision results. If non-zero results exist and are not the same, take the smallest non-zero result as the final decision result; If no non-zero result exists, the decision is considered a failure. Compare the drone's ID with the final decision result; if they match, the drone member is considered to have won the task through auction, and the optimal task sequence recorded in step 5 is executed subsequently. .

4. The method according to claim 1, characterized in that, In step 2: The first j Initial task area A polygon with a number of sides of not less than 3 and not more than 8 is represented as follows: ; wherein For the mission area The k The vertex positions, specifically including vertex longitude. and latitude The For the mission area The number of vertices, with values ​​ranging from 0 to 1. .

5. The method according to claim 1, characterized in that, In step 3: The first i Individual member load capacity The value ranges from 1 to 2, where 1 indicates that the payload has the capability to search for vehicles and larger targets, and 2 indicates that the payload has the capability to search for personnel and larger targets. The first i Initial task sequence for each member It consists of several tasks, represented as ; wherein For drone members i The first j Item task area number, if The third task undertaken by member 1 of the drone team is the mission area. The For drone members i The number of task areas undertaken can be set to 0.

6. A device for autonomous auctioning additional search tasks for unmanned aerial vehicle (UAV) swarms, characterized in that, The device is used to implement the method for autonomous auctioning new search tasks for unmanned aerial vehicle swarms as described in claim 1.

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