CN115616922B - Time coverage control method for heterogeneous mobile robot cluster - Google Patents

Time coverage control method for heterogeneous mobile robot cluster Download PDF

Info

Publication number
CN115616922B
CN115616922B CN202211630146.5A CN202211630146A CN115616922B CN 115616922 B CN115616922 B CN 115616922B CN 202211630146 A CN202211630146 A CN 202211630146A CN 115616922 B CN115616922 B CN 115616922B
Authority
CN
China
Prior art keywords
robot
coverage
time
user
control
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202211630146.5A
Other languages
Chinese (zh)
Other versions
CN115616922A (en
Inventor
张良
邓京辉
周锟
何舒平
宋军
余涛
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Anhui University
Original Assignee
Anhui University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Anhui University filed Critical Anhui University
Priority to CN202211630146.5A priority Critical patent/CN115616922B/en
Publication of CN115616922A publication Critical patent/CN115616922A/en
Application granted granted Critical
Publication of CN115616922B publication Critical patent/CN115616922B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention relates to the technical field of multi-agent and sensor networks, in particular to a time coverage control method of a heterogeneous mobile robot cluster. The optimal coverage position of the robot based on time segmentation is obtained by applying an improved K-means algorithm. Meanwhile, aiming at an under-actuated system of a robot with fixed speed, a Lyapunov function method with fixed time convergence is introduced, a controller with the robot angular speed being converged at fixed time can be obtained by setting a Lyapunov function for the only controllable variable angular speed and combining the fixed time convergence, and the angular speed of the robot can be converged to zero in fixed time by setting parameters in the controller, so that the robot can reach the optimal coverage position more quickly.

Description

Time coverage control method for heterogeneous mobile robot cluster
Technical Field
The invention relates to the technical field of multi-agent and sensor networks, in particular to a time coverage control method for a heterogeneous mobile robot cluster.
Background
The multi-agent coverage control problem mainly studies how to deploy and control a group of agents with computing power, sensing power and communication power, so that the multi-agent network realizes the maximum coverage for a specific area. The standard explanation for this coverage is that the robot should be able to detect events/phenomena within the domain, with each mobile sensor node (robot) responsible for all events within its particular dominating area. The basic idea is that the robot should occupy a static position somewhere in its dominant area, which is sufficiently covered by the sensors of the robot. Coverage problem is a very important application area of large-scale intelligent units, and coverage control can effectively monitor a specified target or a specified area. As networking of intelligent systems and miniaturization of intelligent units, deployment of large-scale intelligent units is rapidly becoming possible, many researchers are investing in the research of coverage control. However, conventional coverage control problems generally consider covering continuous data, requiring that the coverage area be convex. Conventional coverage control methods are not available if the data of the covered area is discrete. However, in reality, many examples are to cover discrete data, such as multiple fire trucks covering a region of cells, where the distribution of the cells is discrete. Therefore, how to cover discrete data is a big problem to be solved.
In addition, conventional coverage control is often performed for static agents. These studies only solve the optimal coverage of agents, and do not design the control law to control the system of the actual robot kinematics model. In addition, in real life, since the driving capability of each vehicle differs, it is considered that the driving capability of each robot differs. For example, considering the deployment of a fire truck, it is desirable that the fire truck not only detect a fire but also reach the fire scene as quickly as possible. The problem is divided into two phases, the first phase being that the fire engine is to distribute itself efficiently to the users that need to be monitored, and the second phase being that it arrives at the fastest time after the fire is discovered, in order to extinguish the fire as quickly as possible. If the speed of each fire engine is different and fixed, the user to be monitored by the fire engine must be modified accordingly. A fast fire truck will monitor more users than a slow fire truck. Based on the problem, the user responsible for the robot is distributed based on the time from the robot to the user instead of the distance from the robot to the user, so that the problem of how to reach the user in the fastest time is solved. Meanwhile, how to design a controller for an under-actuated robot with a fixed speed to drive the robot to reach the optimal coverage position after deployment from the initial position as soon as possible is also a big problem to be solved.
Disclosure of Invention
In view of the above, the present invention is directed to a time coverage control method for a heterogeneous mobile robot cluster, so as to solve the problem that it takes a long time for the heterogeneous mobile robot cluster to reach an optimal coverage position.
Based on the above purpose, the present invention provides a time coverage control method for a heterogeneous mobile robot cluster, which includes the following steps:
s1, calculating the time from each robot to each user;
s2, distributing each user to the robot with the shortest time to reach the user, and obtaining a clustering set which takes each robot as a clustering center and covers the user;
s3, calculating to obtain the expected coverage control position of the robot and the division of the users according to the cluster set covering the users;
s4, setting a control law of the robot according to the distance between the robot and the expected coverage control position and the speed of the robot;
s5, controlling the robot to move to the expected coverage control position through the set control law, and calculating the error between the current position of the robot and the expected coverage control position;
s6, judging whether the error is smaller than a set value, if so, executing a step S7, and if not, returning to execute the step S5;
and S7, repeating the steps S1-S6, iterating the expected coverage control position of the robot and the division of the user until the expected coverage control position and the division of the user obtained by the iteration of the time are equal to the previous iteration, finishing the algorithm, and outputting the obtained expected coverage control position and the division of the user.
Wherein calculating the time from each robot to each user comprises:
for a network of m mobile robots, in a two-dimensional task space
Figure DEST_PATH_IMAGE001
Definition of
Figure DEST_PATH_IMAGE002
Is a collection of robots, wherein
Figure DEST_PATH_IMAGE003
Is the location of the ith robot;
the dynamic model of the mobile robot is
Figure DEST_PATH_IMAGE004
Wherein
Figure DEST_PATH_IMAGE005
Is the coordinates of the ith robot on a two-dimensional plane,
Figure DEST_PATH_IMAGE006
is the angle between the speed direction of the ith motion sensor and the positive x-axis,
Figure DEST_PATH_IMAGE007
linear and angular velocities of the ith robot, respectively, wherein
Figure DEST_PATH_IMAGE008
Belong to a set
Figure DEST_PATH_IMAGE009
Wherein
Figure DEST_PATH_IMAGE010
The number of the robots is a normal number, and the angle mark m is the number of the robots;
considering fixed speed limits for all robots i.e.
Figure DEST_PATH_IMAGE011
Discrete user points are distributed in the task space and are collected into
Figure DEST_PATH_IMAGE012
Wherein
Figure DEST_PATH_IMAGE013
Is the location of the ith user, and n is the number of users;
the minimum arrival time of the robot is
Figure DEST_PATH_IMAGE014
。
The formula for iteratively calculating the desired coverage control position of the robot is:
Figure DEST_PATH_IMAGE015
wherein
Figure DEST_PATH_IMAGE016
The system consists of an ith robot and a user responsible for the ith robot, and represents the user distributed by the ith robot;
Figure DEST_PATH_IMAGE017
is that
Figure 399227DEST_PATH_IMAGE016
The location of the user of (1);
Figure DEST_PATH_IMAGE018
is that
Figure 722892DEST_PATH_IMAGE016
The number of the users is such that,
Figure DEST_PATH_IMAGE019
representing the expected coverage position of the i-th robot after the iteration of the algorithm.
Preferably, the time-based acquisition of the optimal coverage control position is obtained by a modified K-means algorithm.
Preferably, the formula for calculating the error between the current position of the robot and the desired coverage control position is
Figure DEST_PATH_IMAGE020
。
Preferably, the method further comprises determining whether the result obtained by the algorithm achieves the optimal coverage performance through a dynamic coverage control optimization function, wherein the dynamic coverage control optimization function is
Figure DEST_PATH_IMAGE021
Figure DEST_PATH_IMAGE022
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE023
is a cost function of the coverage plan and,
Figure DEST_PATH_IMAGE024
is a cost function for robot control.
Preferably, the control law of the robot is as follows:
Figure DEST_PATH_IMAGE025
+
Figure DEST_PATH_IMAGE026
wherein
Figure DEST_PATH_IMAGE027
And
Figure DEST_PATH_IMAGE028
respectively representing the distance from the mobile robot to the target point and the included angle between the speed direction of the mobile robot and the target direction,
Figure 472936DEST_PATH_IMAGE008
is a fixed normal number which is a constant number,
Figure DEST_PATH_IMAGE029
a, b, p and q are parameters of a control law, and the control purpose of the control law is that (A), (B), (P) and (q) are parameters of the control law
Figure DEST_PATH_IMAGE030
)=(0,0)；
S4, setting the control law of the robot to obtain the parameters a, b, p and q of the control law, wherein the selection of the parameters meets the requirement of
Figure DEST_PATH_IMAGE031
Wherein
Figure DEST_PATH_IMAGE032
Representing the minimum time for the robot to reach the target point.
Preferably, the design step of the control law comprises:
converting a dynamic model of the robot from a rectangular coordinate system to a polar coordinate system, and obtaining the model by taking a target point as an origin of the polar coordinate system
Figure DEST_PATH_IMAGE033
；
Assuming the existence of a continuous differentiable definite radial unbounded function
Figure DEST_PATH_IMAGE034
To make
Figure DEST_PATH_IMAGE035
，
For all
Figure DEST_PATH_IMAGE036
Wherein
Figure DEST_PATH_IMAGE037
，
Figure DEST_PATH_IMAGE038
>0，0<
Figure DEST_PATH_IMAGE039
<1，
Figure DEST_PATH_IMAGE040
>1, the system satisfying the condition is stable in a fixed time and is converged in the fixed time, and the convergence time is shorter
Figure DEST_PATH_IMAGE041
Satisfy the requirement of
Figure DEST_PATH_IMAGE042
Designing Lyapunov functions
Figure DEST_PATH_IMAGE043
In a belt
Figure 350369DEST_PATH_IMAGE035
In (1) obtaining
Figure DEST_PATH_IMAGE044
Wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE045
when is coming into contact with
Figure DEST_PATH_IMAGE046
When the formula is established, the control law is obtained
Figure 273063DEST_PATH_IMAGE025
+
Figure 767630DEST_PATH_IMAGE026
Where k is a normal number.
The invention has the beneficial effects that:
(1) Based on a K-means clustering algorithm, the method is extended to the time-based coverage service of discrete users, so that the optimal user division based on time is realized, the optimal coverage position of the robot is obtained, and the problem of time sensitivity in the practical application background is solved.
(2) Considering definitely the non-complete mobile robot with equal speed constraint, a new fixed time control scheme is proposed, so that the robot can reach the optimal coverage position as soon as possible. To ensure that the arrival time of each robot requesting service is minimized.
Drawings
In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a control schematic diagram of a time covering control method according to an embodiment of the present invention, where whycon is a real-time position where an external software can read a robot;
FIG. 2 is a diagram illustrating a transformation from a rectangular coordinate system to a polar coordinate system of a robot dynamics model according to an embodiment of the present invention;
fig. 3 shows that eight robots with initial positions at the lower left corner are used to cover 200 random users according to the embodiment of the present invention, and the linear velocities of the robots are fixed and different. After planning and controlling by the method, a coverage effect simulation graph is obtained;
FIG. 4 is a graph of the distance variations of eight robots from their respective desired coverage positions during an iteration of the method of the present invention;
FIG. 5 is a graph illustrating the variation of the angle between the velocity direction and the target direction of each of eight robots during the iteration of the method of the present invention;
fig. 6 is a schematic diagram illustrating an average time for the robot to reach the user according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to specific embodiments below.
It is to be noted that technical terms or scientific terms used herein should have the ordinary meaning as understood by those having ordinary skill in the art to which the present invention belongs, unless otherwise defined. The use of "first," "second," and similar terms in the present application do not denote any order, quantity, or importance, but rather the terms are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
As shown in fig. 1, embodiments of the present disclosure provide a time coverage control method for a heterogeneous mobile robot cluster, which considers using a group of robots with different fixed constant speeds to cover a group of discrete users. The overall design is divided into two parts, the first part extending the K-means algorithm to time-based coverage services for discrete users. And dividing the discrete user into a plurality of parts according to the time of each robot reaching the discrete user, and calculating the geometric center of each part to obtain the optimal coverage position. And a fixed time control law is designed in the second part, and a controller is set for the unique controllable variable angular speed according to the Lyapunov function of fixed time, so that the included angle between the speed direction of the robot and the target direction is converged to zero in fixed time. And finally, driving the robot to an optimal coverage control position from the initial position, and then providing the coverage service for the discrete user.
The time coverage control method of the heterogeneous mobile robot cluster mainly comprises a dynamic coverage planning algorithm and a dynamic coverage control law.
1. Dynamic coverage planning algorithm design
Considering a network of m mobile robots, in a two-dimensional task space
Figure 503504DEST_PATH_IMAGE001
. Definition of
Figure 932431DEST_PATH_IMAGE002
Is a collection of robots, wherein
Figure 273413DEST_PATH_IMAGE003
Is the position of the ith robot.
Giving a kinetic model of the constant-velocity incomplete mobile robot:
Figure DEST_PATH_IMAGE047
(1)
wherein
Figure 673302DEST_PATH_IMAGE005
Is the coordinates of the ith robot on a two-dimensional plane,
Figure 162052DEST_PATH_IMAGE006
is the angle between the speed direction of the ith motion sensor and the positive x-axis.
Figure 69703DEST_PATH_IMAGE007
Linear and angular velocities of the ith robot, respectively, wherein
Figure 327509DEST_PATH_IMAGE008
Belong to a set
Figure 429457DEST_PATH_IMAGE009
Wherein
Figure 405503DEST_PATH_IMAGE010
The number of the robots is the angle mark m. We consider the fixed speed limit for all robots i.e.
Figure DEST_PATH_IMAGE048
(2)
At the same time, discrete user points are distributed in the task space and are integrated into
Figure 87152DEST_PATH_IMAGE012
Wherein
Figure 700929DEST_PATH_IMAGE013
Is the location of the ith user and n is the number of users. The robot is specially used for providing some time-sensitive services for customers, such as providing fire-fighting services for a mobile fire truck, or providing emergency medical services for an ambulance and the like. Thus, the quality of service evaluated for each client is primarily the minimum time of arrival through the robot, i.e. the
Figure 708199DEST_PATH_IMAGE014
(3)
First, we calculate the robot time to each user, assigning the user to the robot that has the least time to reach him. We can then get a set of clusters centered around each robot
Figure DEST_PATH_IMAGE049
The set C is the set of users for which each robot is responsible, e.g.
Figure DEST_PATH_IMAGE050
Is the set of customers covered by the first robot that will be responsible for the user assigned to it.
Then we want to calculate the optimal coverage position of each robot
Figure DEST_PATH_IMAGE051
And obtaining the optimal division of all users, completing the coverage of all users and obtaining the optimal coverage performance. The optimal coverage control position can be obtained by the following equation continuously and iteratively.
Figure DEST_PATH_IMAGE052
(4)
Wherein
Figure 483126DEST_PATH_IMAGE016
The system consists of an ith robot and a user responsible for the ith robot;
Figure 30782DEST_PATH_IMAGE017
is that
Figure 200863DEST_PATH_IMAGE016
The location of the user of (1);
Figure 706931DEST_PATH_IMAGE018
is that
Figure 329673DEST_PATH_IMAGE016
The number of users.
Figure 278142DEST_PATH_IMAGE019
Representing the expected coverage position of the i-th robot after the iteration of the algorithm. Each iteration of the coverage location can improve coverage performance until optimal coverage performance is achieved. When the optimal coverage performance is reached, the division of the user points and the coverage position of the robot will not change any more, i.e.
Figure DEST_PATH_IMAGE053
，
Figure DEST_PATH_IMAGE054
。
Based on the dynamic coverage control problem proposed above, we define a dynamic coverage control optimization function as follows:
Figure DEST_PATH_IMAGE055
(5)
the above formula can be split into:
Figure DEST_PATH_IMAGE056
and
Figure 879894DEST_PATH_IMAGE020
。
Figure 494546DEST_PATH_IMAGE023
for the purpose of the cost function of the coverage planning,
Figure 666901DEST_PATH_IMAGE024
cost function for robot control when
Figure 25202DEST_PATH_IMAGE023
When the minimum is reached, the representative user obtains the optimal division, and the optimal coverage position of the robot is calculated. When in use
Figure 966613DEST_PATH_IMAGE024
When the error of the current position of the robot and the optimal coverage control position reaches the minimum value, the error represents that the error of the current position of the robot and the optimal coverage control position reaches the minimum value
Figure 253631DEST_PATH_IMAGE024
Figure 253631DEST_PATH_IMAGE024
0 can be reached. Therefore, when
Figure 116545DEST_PATH_IMAGE024
When the minimum value is reached, the value is,
Figure DEST_PATH_IMAGE057
a minimum is also reached, i.e. the network, which is composed of a set of robots with different fixed constant speeds, achieves an optimal coverage performance.
2. Dynamic overlay control law design
And according to the optimal coverage control position calculated by the previous part of algorithm, designing a coverage control law to control the robot to travel to the optimal coverage control position.
Firstly, converting a kinetic model of the robot from a rectangular coordinate system to a polar coordinate system, and obtaining the following model by taking a target point as an origin of the polar coordinate system, wherein a conversion diagram is shown in FIG. 2;
Figure DEST_PATH_IMAGE058
(6)
wherein
Figure DEST_PATH_IMAGE059
And
Figure DEST_PATH_IMAGE060
respectively representing the distance from the mobile robot to the target point and the included angle between the speed direction of the mobile robot and the target direction,
Figure DEST_PATH_IMAGE061
is a fixed normal number. The purpose of the final control is to make
Figure DEST_PATH_IMAGE062
) = (0,0). The input to the system is only an amount of angular velocity, if not as quickly as possible
Figure 527803DEST_PATH_IMAGE060
Converging to 0, the robot will not be able to move towards the target point, and thus
Figure 526983DEST_PATH_IMAGE059
It cannot converge to 0. So we want to
Figure DEST_PATH_IMAGE063
And designing a control law of fixed time convergence, wherein the fixed time value is set according to the distance between the robot and the target point.
Assuming the existence of a continuous differentiable definite radial unbounded function
Figure DEST_PATH_IMAGE064
To make
Figure 182306DEST_PATH_IMAGE035
(7)
For all
Figure 532516DEST_PATH_IMAGE036
Wherein
Figure 29356DEST_PATH_IMAGE037
，
Figure 148622DEST_PATH_IMAGE038
>0，0<
Figure 571251DEST_PATH_IMAGE039
<1，
Figure 143178DEST_PATH_IMAGE040
>1. A system that satisfies the above conditions will be stable at a fixed time and converge within a fixed time. Time of convergence
Figure 443709DEST_PATH_IMAGE041
Is consistently bounded by the fact that,
Figure DEST_PATH_IMAGE065
。
we design the Lyapunov function
Figure DEST_PATH_IMAGE066
By bringing into (7) can be obtained
Figure DEST_PATH_IMAGE067
(8)
Wherein
Figure DEST_PATH_IMAGE068
，
Figure DEST_PATH_IMAGE069
. When in use
Figure DEST_PATH_IMAGE070
(
Figure DEST_PATH_IMAGE071
Is a normal number), the inequality (8) holds. We therefore get the control law as follows:
Figure 872941DEST_PATH_IMAGE025
+
Figure 404154DEST_PATH_IMAGE026
(9)
(9) Parameter (2) of
Figure DEST_PATH_IMAGE072
，
Figure DEST_PATH_IMAGE073
，
Figure DEST_PATH_IMAGE074
，
Figure DEST_PATH_IMAGE075
Is selected to satisfy the following inequality:
Figure DEST_PATH_IMAGE076
(10)
wherein
Figure 506452DEST_PATH_IMAGE032
Representing the minimum time for the robot to reach the target point, i.e. the initial velocity direction of the robot is and is always heading towards the target point, the time required is the distance divided by the velocity. If it is not
Figure 345095DEST_PATH_IMAGE028
In that
Figure 970112DEST_PATH_IMAGE032
The time is converged to 0, so that the robot can reach the target point more quickly, and the network formed by the robot achieves the optimal coverage performance.
The algorithm provided by the invention has the following input:
1. location of all users
Figure DEST_PATH_IMAGE077
2. Maximum allowable error
Figure DEST_PATH_IMAGE078
3. Initial position of all robots
Figure DEST_PATH_IMAGE079
The algorithm output is:
1. cluster collection of all users
Figure DEST_PATH_IMAGE080
，
2. Optimal coverage control position
Figure DEST_PATH_IMAGE081
。
As shown in fig. 3, eight robots with initial positions at the lower left corner are used to cover a random number of 200 users, and the linear velocities of the robots are fixed and different. After planning and controlling by the method, a coverage effect simulation graph is obtained;
FIG. 4 is a graph of the change in distance of eight robots from their respective desired coverage positions during an iteration of the method of the invention herein;
FIG. 5 is a graph of the change in the respective angles of the velocity directions of eight robots to the target direction during an iteration of the method of the invention herein;
fig. 6 is the average time for the robot to reach the user, decreasing with iteration.
As can be seen from fig. 3 to 6, the time-based coverage control method for the heterogeneous equal-speed incomplete mobile robot provided by the invention can well realize time-based coverage of discrete users in a task space, thereby ensuring the coverage performance of a coverage task and solving the problem that an underactuated robot quickly reaches a target point.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to those examples; within the idea of the invention, also features in the above embodiments or in different embodiments may be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements and the like that may be made without departing from the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (6)

1. A time coverage control method for a heterogeneous mobile robot cluster is characterized by comprising the following steps:
s1, calculating the time from each robot to each user;
s2, distributing each user to the robot with the shortest time to reach the user, and obtaining a clustering set which takes each robot as a clustering center and covers the user;
s3, calculating to obtain the expected coverage control position of the robot and the division of the users according to the cluster set covering the users;
s4, setting a control law of the robot according to the distance between the robot and the expected coverage control position and the speed of the robot;
s5, controlling the robot to move to the expected coverage control position through the set control law, and calculating the error between the current position of the robot and the expected coverage control position;
s6, judging whether the error is smaller than a set value, if so, executing a step S7, and if not, returning to execute the step S5;
s7, repeating the steps S1-S6, iterating the expected coverage control position of the robot and the division of the user until the expected coverage control position and the division of the user obtained by the iteration of the time are equal to the previous iteration, finishing the algorithm, and outputting the obtained expected coverage control position and the division of the user;
the calculating the time from each robot to each user comprises:
for a network consisting of m mobile robots, F ∈ R in a two-dimensional task space 2 Definition of
Figure FDA0004077700090000012
Figure FDA0004077700090000013
Is a set of robots, where p i E is the position of the ith robot;
the dynamic model of the mobile robot is
Figure FDA0004077700090000011
Wherein x i ,y i Is the coordinate of the ith robot on a two-dimensional plane, θ i Is the angle between the speed direction of the ith motion sensor and the positive x-axis, v i ,ω i Linear and angular velocities of the ith robot, respectively, where v i Belong to the set λ = λ 1 ,λ 2 ,…,λ m Wherein λ is 1 ,λ 2 ,…,λ m The number of the robots is a normal number, and the angle mark m is the number of the robots;
considering fixed speed limits for all robots, i.e.
v i ≡λ i
Discrete user points are distributed in the task space, and the set is D = { D = 1 ,d 2 ,...d n In which d is j E is F is the position of the ith user, and n is the number of the users;
the minimum arrival time of the robot is
Figure FDA0004077700090000021
The formula for iteratively calculating the desired coverage control position of the robot is:
Figure FDA0004077700090000022
wherein
Figure FDA0004077700090000023
The system consists of an ith robot and a user responsible for the ith robot, and represents the user distributed by the ith robot; x is the number of j Is that
Figure FDA0004077700090000024
The location of the user of (1);
Figure FDA0004077700090000025
is that
Figure FDA0004077700090000026
The number of the users is such that,
Figure FDA0004077700090000027
representing the expected coverage position of the i-th robot after the algorithm iteration.
2. The method of claim 1, wherein the obtaining of the time-based optimal coverage control position is obtained by a modified K-means algorithm.
3. The method of claim 1, wherein the error between the current position of the robot and the desired coverage control position is calculated as
Figure FDA0004077700090000028
4. The method of claim 3, further comprising determining whether the result of the algorithm achieves optimal coverage performance by a dynamic coverage control optimization function, wherein the dynamic coverage control optimization function is
H＝H p +H d
Figure FDA0004077700090000029
Wherein H p Is a cost function of coverage planning, H d Is a cost function for robot control.
5. The method according to claim 1, wherein the control law of the robot is as follows:
Figure FDA0004077700090000031
where ρ is i And alpha i Respectively representing the distance from the mobile robot to a target point and the included angle between the speed direction of the mobile robot and the target direction, v i Is a fixed normal number of the line that,
Figure FDA0004077700090000032
a, b, p and q are parameters of a control law, and the control purpose of the control law is to enable (rho) i ,α i )＝(0,0)；
S4, setting the control law of the robot to obtain the parameters a, b, p and q of the control law, wherein the selection of the parameters meets the requirement of
Figure FDA0004077700090000033
Wherein T is min Representing the minimum time for the robot to reach the target point.
6. The method for controlling time coverage of a cluster of heterogeneous mobile robots according to claim 5, wherein the step of designing the control law comprises:
converting a dynamic model of the robot from a rectangular coordinate system to a polar coordinate system, and obtaining the model by taking a target point as an origin of the polar coordinate system
Figure FDA0004077700090000034
Assuming that there is a continuously differentiable positive radial unbounded function V, the
Figure FDA0004077700090000035
For all x ≠ 0, where a>0，b>0，0<p<1，q>1, the system satisfying the condition is stable in a fixed time and is converged in the fixed time, and the convergence time T satisfies
Figure FDA0004077700090000036
Designing Lyapunov functions
Figure FDA0004077700090000037
Bringing in
Figure FDA0004077700090000038
In (1) obtaining
Figure FDA0004077700090000041
Figure FDA0004077700090000042
Φ i α i ≤0
Wherein the content of the first and second substances,
Figure FDA0004077700090000043
when phi is i ＝-kα i When the formula is established, the control law is obtained
Figure FDA0004077700090000044
Where k is a normal number.
CN202211630146.5A 2022-12-19 2022-12-19 Time coverage control method for heterogeneous mobile robot cluster Active CN115616922B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202211630146.5A CN115616922B (en) 2022-12-19 2022-12-19 Time coverage control method for heterogeneous mobile robot cluster

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202211630146.5A CN115616922B (en) 2022-12-19 2022-12-19 Time coverage control method for heterogeneous mobile robot cluster

Publications (2)

Publication Number Publication Date
CN115616922A CN115616922A (en) 2023-01-17
CN115616922B true CN115616922B (en) 2023-03-21

Family

ID=84881009

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202211630146.5A Active CN115616922B (en) 2022-12-19 2022-12-19 Time coverage control method for heterogeneous mobile robot cluster

Country Status (1)

Country Link
CN (1) CN115616922B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117706910A (en) * 2023-12-27 2024-03-15 哈尔滨工业大学 Robot cluster coverage method and system based on sliding mode control and with different maximum speeds

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110076783A (en) * 2019-06-14 2019-08-02 北京邮电大学 A kind of plane activation lacking mechanical arm position control method based on fuzzy control and liapunov function
CN114859899A (en) * 2022-04-18 2022-08-05 哈尔滨工业大学人工智能研究院有限公司 Actor-critic stability reinforcement learning method for navigation obstacle avoidance of mobile robot

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7663333B2 (en) * 2001-06-12 2010-02-16 Irobot Corporation Method and system for multi-mode coverage for an autonomous robot
US10836038B2 (en) * 2014-05-21 2020-11-17 Fanuc America Corporation Learning path control
CN113051807B (en) * 2021-04-17 2023-04-14 西北工业大学 Multi-agent coverage method for complex area based on k-Means and sliding mode variable structure control
CN113595619B (en) * 2021-07-30 2022-09-02 中国电子科技集团公司第五十四研究所 Unmanned aerial vehicle group communication and coverage combination optimization method
CN115437396A (en) * 2022-08-23 2022-12-06 电子科技大学 Complex environment-oriented multi-heterogeneous robot visual servo formation transformation method

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110076783A (en) * 2019-06-14 2019-08-02 北京邮电大学 A kind of plane activation lacking mechanical arm position control method based on fuzzy control and liapunov function
CN114859899A (en) * 2022-04-18 2022-08-05 哈尔滨工业大学人工智能研究院有限公司 Actor-critic stability reinforcement learning method for navigation obstacle avoidance of mobile robot

Also Published As

Publication number Publication date
CN115616922A (en) 2023-01-17

Similar Documents

Publication Publication Date Title
Ma et al. Cooperative target tracking in balanced circular formation: Multiple UAVs tracking a ground vehicle
CN115616922B (en) Time coverage control method for heterogeneous mobile robot cluster
Lee et al. Controlled coverage using time-varying density functions
Zhang et al. A visual distance approach for multicamera deployment with coverage optimization
Xu et al. Cnn partitioning and offloading for vehicular edge networks in web3
Saim et al. Distributed average tracking in multi-agent coordination: Extensions and experiments
CN111142567B (en) Unmanned aerial vehicle target position exchange method and device in unmanned aerial vehicle system
CN110442134B (en) Multi-agent cluster control method based on double-layer network
Hong et al. UAV search-and-rescue planning using an adaptive memetic algorithm
Raja et al. Dynamic polygon generation for flexible pattern formation in large-scale UAV swarm networks
CN114186859A (en) Multi-machine cooperative multi-target task allocation method in complex unknown environment
CN111553404B (en) Self-adaptive dynamic planning method for communication network node
Gautam et al. A graph partitioning approach for fast exploration with multi-robot coordination
Pal et al. Multi robot exploration using a modified a* algorithm
He et al. Moving target tracking by UAVs in an urban area
Papatheodorou et al. Experimental studies on distributed control for area coverage using mobile robots
CN115665757A (en) Distributed unknown time-varying region multi-autonomous-body optimal coverage method and system
Ferreira Filho et al. Segregation of heterogeneous swarms of robots in curves
Sabattini et al. Hierarchical coordination strategy for multi-AGV systems based on dynamic geodesic environment partitioning
Zuo et al. Adaptive and collision-free line coverage algorithm for multi-agent networks with unknown density function
Fu et al. Obstacle avoidance and collision avoidance of UAV swarm based on improved VFH algorithm and information sharing strategy
DeVries et al. Kernel design for coordination of autonomous, time-varying multi-agent configurations
Pustowka et al. Market-based task allocation in a multi-robot surveillance system
Machida et al. Consensus-based artificial potential field approach for swarm
Han et al. UAV robust strategy control based on MAS

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant