CN112070328B - Multi-water surface unmanned search and rescue boat task allocation method with partially known environmental information - Google Patents

Multi-water surface unmanned search and rescue boat task allocation method with partially known environmental information Download PDF

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CN112070328B
CN112070328B CN201910499052.0A CN201910499052A CN112070328B CN 112070328 B CN112070328 B CN 112070328B CN 201910499052 A CN201910499052 A CN 201910499052A CN 112070328 B CN112070328 B CN 112070328B
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task
search
rescue
boat
rescue boat
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CN112070328A (en
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黄海滨
庄宇飞
李亚南
梁景凯
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Weihai Tianfan Intelligent Technology Co ltd
Harbin Institute of Technology Weihai
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Harbin Institute of Technology Weihai
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    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention relates to a multi-water surface unmanned search and rescue boat task distribution method with partially known environment information, aiming at the situation that the perceived radius and the communication radius of an unmanned boat are limited and the environment information of an operation area is only partially mastered, an online task distribution technology of a heterogeneous water surface unmanned search and rescue boat is developed, a sequential auction algorithm is improved, for the on-line perceived task to be executed of the search and rescue boat, a candidate task sequence is firstly measured for the matching degree of the operation capacity of each boat and the task, then the operation income of an associated unmanned boat is evaluated for the task to be shot of a current round, the candidate execution boat sequence is updated, finally, the task energy consumption is minimized as a target to execute the auction, and the current round task is executed by a middle-timer.

Description

Multi-water surface unmanned search and rescue boat task allocation method with partially known environmental information
Technical field:
the invention belongs to the field of unmanned system cluster task allocation, and particularly relates to a task allocation method for a water surface unmanned search and rescue boat cluster under the condition that the operation environment part is known.
The background technology is as follows:
the actual maritime search and rescue task is often wide in operation water area range, complex in environment, long in operation time and boring in process. Therefore, with the continuous improvement of autonomy of unmanned systems in recent years, unmanned search and rescue boats on the water surface gradually replace manual operation in maritime search and rescue tasks, and the unmanned search and rescue boats are widely applied. Compared with a single boat, the unmanned boat cluster cooperatively executes tasks by a plurality of boats, has the distribution characteristics of time, space, information, resources and functions, can greatly improve the execution efficiency and fault tolerance of search and rescue tasks, and is a mainstream development trend of future maritime search and rescue application.
In the process of autonomously searching and rescuing a plurality of targets by a plurality of unmanned ships, the plurality of individuals cannot be simply combined together in consideration of parallelism in individual behaviors and certain emergency, and a reasonable task allocation mechanism needs to be introduced, so that the self advantages of each unmanned ship are fully exerted, and the established targets are expected to be efficiently completed.
At present, the task allocation method of the unmanned system cluster mainly abstracts the problem into an optimization problem which can be processed by a computer, and adopts an intelligent optimization algorithm to solve the problem. Although the method is widely applied, the convergence rate of the algorithm is slower for the general optimization problem, and the probability of obtaining a reasonable optimization result is lower. Therefore, the invention fully considers the difference of different search and rescue boat capacities under the condition that the operation environment information is only partially known by referring to the basic thought of the sequential auction algorithm, and realizes the efficient and reasonable allocation of tasks by optimizing the total task loss.
The invention comprises the following steps:
in order to overcome the defects of the prior art, the invention provides a task allocation technology of a multi-water-surface unmanned search and rescue boat based on an auction algorithm. On the basis of the traditional sequential auction algorithm, the matching degree between the type of the task to be executed and the operation capacity of the search and rescue boats and the specific difference of the physical properties of each boat are comprehensively considered, so that the on-line operation of the task distribution process is realized, and the finally obtained task distribution scheme is more reasonable and meets the actual requirements.
The invention adopts the following specific technical scheme:
step 1; initializing, and loading known operation sea area part environment information:
wherein the number of tasks to be performed: set to m, labeled set t= { T 1 ,t 2 ,...,t m }；
Type of task to be performed: by variables
Figure BDA0002089578810000011
Characterization of execution of the jth task t j The cost of e T, the variable is related only to the type of task being performed, and is independent of the performance of the search and rescue boat performing the task;
number of search and rescue boats: set to n, labeled set u= { U 1 ,u 2 ,...,u n }；
The operation capability of the search and rescue boat: the ith search and rescue boat u i I=1, 2,..n implements the j-th task t j J=1, 2, the capacity of m is set as
Figure BDA0002089578810000021
And let->
Figure BDA0002089578810000022
When->
Figure BDA0002089578810000023
At the time, indicate search and rescue boat u i Does not have execution task t j Is not limited in terms of the ability to perform;
setting the initial task subsequences of the search and rescue boats as empty sets;
step 2: setting sampling time intervals delta T, collecting carried sensor information by each search and rescue boat at intervals delta T, and inserting the detected task to be executed into a task subsequence of the corresponding boat;
step 3: executing internal auction, marking all the tasks to be auctioned in the round as t' 1 ,t′ 2 ...t′ p P is less than or equal to m, and aiming at each task t 'to be shot' k K=1, 2,..p performs the following steps:
step 3-1: will be associated with task t' k K=1, 2., where, p is marked as a set of all candidate search and rescue boats
Figure BDA0002089578810000024
I.e. the main wheel has q k The boat detects task t' k And the boats can communicate with each other, and the complement is defined as +.>
Figure BDA0002089578810000025
Corresponding to the current wheel not detecting the task t' k Is searched for and rescue boatA collection;
step 3-2: for any u ks ∈U k ,s＝1,2,...,q k Calculating all search and rescue boats u i E U, 1=1, 2,.. ks Internal auction bid values for (a)
Figure BDA0002089578810000026
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0002089578810000027
corresponding to completion task t' k Cost of pays->
Figure BDA0002089578810000028
Corresponding boat u ks Realize task t' k Is a function of the ability of the (c) to,
Figure BDA0002089578810000029
b is a constant value parameter set by a user and represents the current unknown search and rescue boat u i And task t' k Geometric distance between>
Figure BDA00020895788100000210
Corresponding search and rescue boat u ks And task t 'to be executed' k The geometric distance epsilon > 0 between the two is a smaller constant parameter defined by a user;
step 3-3: if it is
Figure BDA00020895788100000211
Then reserve u ks In candidate search and rescue boat set U k Is unchanged; otherwise, if c=epsilon, updating the candidate search and rescue boat set U k By u i Instead of u ks ；
Step 4: candidate search and rescue boat set U corresponding to p tasks to be executed in the round after updating 1 ,U 2 ,...,U p The task corresponding to the set with the largest elements is used as the auction task of the round and marked as
Figure BDA00020895788100000212
(subscript f corresponds to the current auction being round f);
step 5: external auction and the task to be beaten of this round
Figure BDA0002089578810000031
Corresponding candidate search and rescue boat set marks are as follows
Figure BDA0002089578810000032
Step 5-1: for all search and rescue boats
Figure BDA0002089578810000033
Calculate its and task->
Figure BDA0002089578810000034
Geometric distance between->
Figure BDA0002089578810000035
Then determine the search and rescue boat of this round of hosting auction +.>
Figure BDA0002089578810000036
Figure BDA0002089578810000037
While acting as a communication node for the current round of auctions,
Figure BDA0002089578810000038
responsible for statistics collection->
Figure BDA0002089578810000039
Bidding conditions of each search and rescue boat in the middle and in the following
Figure BDA00020895788100000310
Broadcasting the final auction result in range;
step 5-2: the performance index adopted in the auction process is
Figure BDA00020895788100000311
Figure BDA00020895788100000312
Auction task on behalf of the h round is assigned to search and rescue boat +.>
Figure BDA00020895788100000313
Otherwise->
Figure BDA00020895788100000314
Variable(s)
Figure BDA00020895788100000315
Wherein the variables are
Figure BDA00020895788100000316
Corresponding search and rescue boat->
Figure BDA00020895788100000317
Run to task in the h round->
Figure BDA00020895788100000318
Energy consumed at the location;
step 5-3:
Figure BDA00020895788100000319
the corresponding search and rescue boat is the final winner of the round of auction;
step 6: updating the task sequence to be executed, and eliminating the completed task from the task sequence. And (3) returning to the step (2) if the task sequence to be executed is not empty, otherwise ending the algorithm flow.
The beneficial technical effects obtained by the invention are as follows:
the traditional auction algorithm is mostly only suitable for task allocation problems of isomorphic cluster systems, but is imperfect in priori knowledge, and unmanned systems are often low in operation efficiency and unreasonable in allocation scheme due to communication and detection of heterogeneous systems with limited radius. The method improves the bidding strategy and specific auction mode of each round of auction based on the traditional sequential auction algorithm. Meanwhile, the difference of the respective capacities of the heterogeneous unmanned ship clusters is considered. By repeatedly distributing tasks on line in real time, the task distribution efficiency and the rationality are improved.
Description of the drawings:
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a schematic diagram of a task allocation scheme for an isomorphic scenario according to the present invention.
Fig. 3 is a schematic diagram of a task allocation scheme according to the present invention for a heterogeneous scenario.
FIG. 4 is a schematic diagram of a task allocation scheme of the present invention in the case of a limited search and rescue boat communication radius and perceived radius.
The specific embodiment is as follows:
in order to make the objects, technical solutions and advantages of the present invention more clear and clear, the following detailed description of the present invention is further described with reference to the accompanying drawings and examples. The specific embodiments described herein are offered by way of illustration only, and not by way of limitation. In the simulation process, the verification of the performance of the task allocation algorithm is focused, so that a mathematical model of the unmanned search and rescue boat on the water surface adopts a linear second-order system. The simulation environment is as follows: intel 3.00GHz main frequency, a PC with 4.00GB memory, a Windows 7 operating system and a Matlab R2012a simulation platform.
The algorithm flow chart of the invention is shown in fig. 1, and the detailed steps are as follows:
step 1: initializing: loading the known environmental information of the working sea area part,
step 1-1: setting the number of tasks to be executed: set to m, labeled set t= { T 1 ,t 2 ,...,t m }；
Step 1-2: type of task to be performed: by variables
Figure BDA0002089578810000041
Characterization execution Noj tasks t j The cost of e T, the variable is related only to the type of task being performed, and is independent of the performance of the search and rescue boat performing the task;
step 1-3: number of search and rescue boats: set to n, labeled set u= { U 1 ,u 2 ,...,u n }；
Step 1-4: the operation capability of the search and rescue boat: the ith search and rescue boat u i I=1, 2,..n implements the j-th task t j J=1, 2, the capacity of m is set as
Figure BDA0002089578810000042
And let->
Figure BDA0002089578810000043
When->
Figure BDA0002089578810000044
At the time, indicate search and rescue boat u i Does not have execution task t j Is not limited in terms of the ability to perform; setting the initial task subsequences of the search and rescue boats as empty sets;
in general, n.ltoreq.m is required, and the operation capacity of the search and rescue boat in steps 1 to 4 is set for the convenience of subsequent application
Figure BDA0002089578810000045
The normalization processing is carried out, and when the normalization processing is carried out, at least the operation capability value of one boat is ensured to be different from zero for each round of auction task; meanwhile, the operation capacity of a certain rescue boat in the cluster can be set to be 1 for the appointed task, namely, the appointed task can only be implemented by the rescue boat with the operation capacity of 1.
Step 2: setting sampling time intervals delta T, collecting carried sensor information by each search and rescue boat at intervals delta T, and inserting the detected task to be executed into a task subsequence of the corresponding boat; the selection of the sampling time value has a great influence on the actual system performance; if the value of Δt is too small, the performance of the navigation system carried by each boat and the communication bandwidth between boats are correspondingly improved, otherwise, if the value of Δt is too large, a missing detection phenomenon may occur, and the final allocation scheme is poor.
Step 3: internal auction, marking all the tasks to be auctioned in this round as t' 1 ,t′ 2 ...t′ p P is less than or equal to m, and aiming at each task t 'to be shot' k K=1, 2,..p performs the following steps:
step 3-1: will be associated with task t' k K=1, 2., where, p is marked as a set of all candidate search and rescue boats
Figure BDA0002089578810000046
I.e. the main wheel has q k The boat detects task t' k And the boats can communicate with each other, and the complement is defined as +.>
Figure BDA0002089578810000051
Corresponding to the current wheel not detecting the task t' k Is gathered by the search and rescue boats;
for any u ks ∈U k ,s＝1,2,...,q k Calculating all search and rescue boats u i E U, 1=1, 2,.. ks Internal auction bid values for (a)
Figure BDA0002089578810000052
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0002089578810000053
corresponding to completion task t' k Cost of pays->
Figure BDA0002089578810000054
Corresponding boat u ks Realize task t' k Is a function of the ability of the (c) to,
Figure BDA0002089578810000055
b is a constant value parameter set by a user and represents the current unknown search and rescue boat u i And task t' k Geometric distance between>
Figure BDA0002089578810000056
Corresponding search and rescue boat u ks And task t 'to be executed' k The geometric distance epsilon > 0 between the two is a smaller constant parameter defined by a user;
if it is
Figure BDA0002089578810000057
I.e. u i Located at u ks Outside the communication radius range. Thus u ks Search and rescue boat u cannot be directly obtained i The current specific position can only determine the value of the parameter B in a predictive mode. In practical application, the value of B should be larger than the upper limit of the communication radius of all the operation boats.
The function of the step 3-2 is to judge the search and rescue boat u ks In other words, whether the task t 'should take part in the present round' k Or to forego other tasks to search for a subsequent task that is more appropriate for its job capability. At the same time, in order to avoid task t' k All candidate search and rescue boats corresponding to the same
Figure BDA0002089578810000058
All failures in the internal auction, the constant parameter C must be greater than zero. Further, to ensure->
Figure BDA0002089578810000059
And epsilon differ significantly in value, so epsilon is set to a normal value parameter that is small enough.
Step 3-3: if it is
Figure BDA00020895788100000510
Then reserve u ks In candidate search and rescue boat set U k Is unchanged; otherwise, if c=epsilon, updating the candidate search and rescue boat set U k By u i Instead of u ks ；
Step 4: candidate search and rescue boat set U corresponding to p tasks to be executed in the round after updating 1 ,U 2 ,...,U p The task corresponding to the set with the largest elements is used as the auction task of the round and marked as
Figure BDA00020895788100000511
(subscript f corresponds to the current auction being round f);
step 5: external auction and the task to be beaten of this round
Figure BDA00020895788100000512
Corresponding candidate search and rescue boat set marks are as follows
Figure BDA00020895788100000513
Step 5-1: for all search and rescue boats
Figure BDA00020895788100000514
Calculate its and task->
Figure BDA00020895788100000515
Geometric distance between->
Figure BDA00020895788100000516
Then determine the search and rescue boat of this round of hosting auction +.>
Figure BDA00020895788100000517
Figure BDA00020895788100000518
While acting as a communication node for the current round of auctions,
Figure BDA00020895788100000519
responsible for statistics collection->
Figure BDA00020895788100000520
Bidding conditions of each search and rescue boat in the middle and in the following
Figure BDA00020895788100000521
Broadcasting the final auction result in range;
step 5-2: the performance index adopted in the auction process is
Figure BDA0002089578810000061
Figure BDA0002089578810000062
Auction task on behalf of the h round is assigned to search and rescue boat +.>
Figure BDA0002089578810000063
Otherwise->
Figure BDA0002089578810000064
Variable(s)
Figure BDA0002089578810000065
Wherein the variables are
Figure BDA0002089578810000066
Corresponding search and rescue boat->
Figure BDA0002089578810000067
Run to task in the h round->
Figure BDA0002089578810000068
Energy consumed at the location;
in practical application, the search and rescue boat always moves at a constant speed, so that the performance index is
Figure BDA0002089578810000069
Can be used for representing search and rescue boats
Figure BDA00020895788100000610
All the energy consumed by the auction up to round f (including round f).
Step 5-3:
Figure BDA00020895788100000611
corresponding searchThe rescue boat is the final winner of the round of auction;
step 6: updating the task sequence to be executed, and eliminating the completed task from the task sequence. And (3) returning to the step (2) if the task sequence to be executed is not empty, otherwise ending the algorithm flow.
In the implementation process of the algorithm, the search and rescue boat is required to store the task information which is completed and detected by the current wheel, and simultaneously stores the positions of all other search and rescue boats within the communication range and the completed task information. And periodically updating the information through the acquisition of the information of the on-board sensor at each sampling moment. In order to reduce the communication burden, the algorithm does not adopt a real-time communication mode, and the corresponding search and rescue boat provides a communication application only when the following conditions occur: (1) detecting a new task; (2) detecting other member boats; (3) the task is successfully allocated.
The parameters of this embodiment are set as follows: the area of the working area is 100m multiplied by 100m; all the search and rescue boats run at a constant speed (1 m/s); sampling time Δt=10s; constant parameter b=60, epsilon=0.0001; working capacity of each boat
Figure BDA00020895788100000612
The value is in the value range of [0,1 ]]Random generation.
Fig. 2 and fig. 3 correspond to allocation schemes of the algorithm under isomorphic (all the search and rescue boats in the cluster have the same operation capability without considering the limitation of communication and perception radius) and heterogeneous (all the search and rescue boats in the cluster have different operation capability without considering the limitation of communication and perception radius) scenes respectively. Wherein 40 tasks to be executed (shown as solid points in the figure) are randomly distributed in the working area, and 3 search and rescue boats are arranged to execute the tasks. The hollow circles correspond to the initial positions of the three search and rescue boats which are distributed randomly. The diamond shaped markings in fig. 3 correspond to a particular type of task that can only be accomplished by a designated search and rescue boat. The operation targets are that all tasks are executed by the three search and rescue boats, and the operation returns to the corresponding initial position after the operation is finished. It can be seen that the matching degree between the search and rescue boat and the task to be executed is fully considered, and the method is applicable to isomorphic and heterogeneous cluster systems. Moreover, each search and rescue boat executes tasks distributed in the nearby area as much as possible, so that excessive resource consumption and conflicts among boats are avoided. The distribution scheme is reasonable and efficient.
FIG. 4 is a schematic diagram of a task allocation scheme of the present invention in the case of a limited search and rescue boat communication radius and perceived radius; 50 tasks (shown by solid dots) are randomly distributed in the working water area, and two boats are adopted to execute search and rescue tasks. It can be seen that in the case where the job environment information is only partially known, the present invention can still obtain a more satisfactory allocation scheme by online real-time task redistribution. Thereby verifying the utility of the present invention.
The beneficial technical effects obtained by the invention are as follows:
the traditional auction algorithm is mostly only suitable for task allocation problems of isomorphic cluster systems, but is imperfect in priori knowledge, and unmanned systems are often low in operation efficiency and unreasonable in allocation scheme due to communication and detection of heterogeneous systems with limited radius. The method improves the bidding strategy and specific auction mode of each round of auction based on the traditional sequential auction algorithm. Meanwhile, the difference of the respective capacities of the heterogeneous unmanned ship clusters is considered. By repeatedly distributing tasks on line in real time, the task distribution efficiency and the rationality are improved.

Claims (3)

1. The task allocation method for the unmanned multi-water-surface search and rescue boat with the known environment information part is characterized by comprising the following steps of:
step 1: initializing, loading known operation sea area part environment information, including
Step 1-1: number of tasks to be performed: set to m, labeled set t= { T 1 ,t 2 ,...,t m }；
Step 1-2: type of task to be performed: by variable ec tj Characterization of execution of the jth task t j The cost of e T, the variable is related only to the type of task being performed, and is independent of the performance of the search and rescue boat performing the task;
step 1-3: number of search and rescue boats: set to n, labeled set u= { U 1 ,u 2 ,...,u n }；
Step 1-4: the operation capability of the search and rescue boat: search and rescue boat u i I=1, 2,..n implements the j-th task t j J=1, 2, the capacity of m is set as
Figure FDA0002089578800000011
And let->
Figure FDA0002089578800000012
When->
Figure FDA0002089578800000013
At the time, indicate search and rescue boat u i Does not have execution task t j Is not limited in terms of the ability to perform;
setting the initial task subsequences of the search and rescue boats as empty sets;
step 2: setting sampling time intervals delta T, collecting carried sensor information by each search and rescue boat at intervals delta T, and inserting the detected task to be executed into a task subsequence of the corresponding boat;
step 3: executing internal auction, marking all the tasks to be auctioned in the round as t' 1 ,t′ 2 ...t′ p P is less than or equal to m, and aiming at each task t 'to be shot' k K=1, 2,..p performs the following steps:
step 3-1: will be associated with task t' k K=1, 2., where, p is marked as a set of all candidate search and rescue boats
Figure FDA0002089578800000014
I.e. the main wheel has q k The boat detects task t' k And the boats can communicate with each other, and the complement is defined as +.>
Figure FDA0002089578800000015
Corresponding to the current wheel not detecting the task t' k Is gathered by the search and rescue boats;
step 3-2: for any u ks ∈U k ,s＝1,2,...,q k Calculating all search and rescue boats u i E U, i=1, 2,.. ks Internal bidding of (C)Clapping value
Figure FDA0002089578800000016
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure FDA0002089578800000017
corresponding to completion task t' k Cost of pays->
Figure FDA0002089578800000018
Corresponding boat u ks Realize task t' k Is a function of the ability of the (c) to,
Figure FDA0002089578800000019
b is a constant value parameter customized by a user and represents the current unknown search and rescue boat u i And task t' k Geometric distance between>
Figure FDA00020895788000000110
Corresponding search and rescue boat u ks And task t 'to be executed' k The geometric distance epsilon > 0 between the two is a smaller constant parameter defined by a user;
step 3-3: if it is
Figure FDA0002089578800000021
Then reserve u ks In candidate search and rescue boat set U k Is unchanged; otherwise, if c=epsilon, updating the candidate search and rescue boat set U k By u i Instead of u ks ；
Step 4: candidate search and rescue boat set U corresponding to p tasks to be executed in the round after updating 1 ,U 2 ,...,U p The task corresponding to the set with the largest elements is used as the auction task of the round and marked as
Figure FDA0002089578800000022
(subscript f corresponds to the current auction being round f);
step 5: executing external auction and the task to be beaten in the round
Figure FDA0002089578800000023
Corresponding candidate search and rescue boat set marks are as follows
Figure FDA0002089578800000024
Step 6: updating the task sequence to be executed, eliminating the completed task from the task sequence, returning to the step (2) if the task sequence to be executed is not empty, and ending the algorithm flow if not.
2. A method of task allocation for a multi-surface unmanned search and rescue boat with partially known environmental information according to claim 1, wherein step 5 comprises the following steps:
step 5-1: for all search and rescue boats
Figure FDA0002089578800000025
Calculate its and task->
Figure FDA0002089578800000026
Geometric distance between->
Figure FDA0002089578800000027
Then determine the search and rescue boat of this round of hosting auction +.>
Figure FDA0002089578800000028
Figure FDA0002089578800000029
While acting as a communication node for the current round of auctions,
Figure FDA00020895788000000210
responsible for statistics collection->
Figure FDA00020895788000000211
Bid conditions of each search and rescue boat in +.>
Figure FDA00020895788000000212
Broadcasting the final auction result in range;
step 5-2: the performance index adopted in the auction process is
Figure FDA00020895788000000213
Figure FDA00020895788000000214
Auction task on behalf of the h round is assigned to search and rescue boat +.>
Figure FDA00020895788000000215
Otherwise
Figure FDA00020895788000000216
Variable->
Figure FDA00020895788000000217
Wherein the variables are
Figure FDA00020895788000000218
Corresponding search and rescue boat->
Figure FDA00020895788000000219
Run to task in the h round->
Figure FDA00020895788000000220
Energy consumed at the location;
step 5-3:
Figure FDA00020895788000000221
the corresponding search and rescue boat isThis round of auction eventually winners.
3. The task allocation method for the unmanned search and rescue boat on the multiple water surfaces with the known environmental information part according to claim 1, wherein n is less than or equal to m in the step 1, and the operation capacity of the search and rescue boat in the steps 1-4 is set for facilitating the subsequent application
Figure FDA00020895788000000222
Normalization processing was performed.
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CN113190041B (en) * 2021-04-30 2022-05-10 哈尔滨工业大学 Unmanned aerial vehicle cluster online target distribution method based on constraint relaxation technology
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108873894A (en) * 2018-06-11 2018-11-23 上海大学 A kind of target following cooperative control system and method based on more unmanned boats
CN108985580A (en) * 2018-06-16 2018-12-11 齐齐哈尔大学 Multirobot disaster based on improved BP searches and rescues method for allocating tasks

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108873894A (en) * 2018-06-11 2018-11-23 上海大学 A kind of target following cooperative control system and method based on more unmanned boats
CN108985580A (en) * 2018-06-16 2018-12-11 齐齐哈尔大学 Multirobot disaster based on improved BP searches and rescues method for allocating tasks

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
基于DAMPSO算法的USVs集群攻击任务规划研究;李杰;孙尧;;计算机工程与应用;49(20);1-4+51 *
面向多无人机的协同任务预分配及重分配研究;吴歇尔;南昌航空大学;第2018年卷(第11期);全文 *

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