CN117706910B - Robot cluster coverage method and system based on sliding mode control and with different maximum speeds - Google Patents
Robot cluster coverage method and system based on sliding mode control and with different maximum speeds Download PDFInfo
- Publication number
- CN117706910B CN117706910B CN202311811151.0A CN202311811151A CN117706910B CN 117706910 B CN117706910 B CN 117706910B CN 202311811151 A CN202311811151 A CN 202311811151A CN 117706910 B CN117706910 B CN 117706910B
- Authority
- CN
- China
- Prior art keywords
- robot
- sliding mode
- optimal coverage
- control
- robots
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 37
- 238000013461 design Methods 0.000 claims description 4
- 230000036962 time dependent Effects 0.000 abstract description 2
- 238000004088 simulation Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000010420 art technique Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000012850 discrimination method Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- KFOPKOFKGJJEBW-ZSSYTAEJSA-N methyl 2-[(1s,7r,8s,9s,10r,13r,14s,17r)-1,7-dihydroxy-10,13-dimethyl-3-oxo-1,2,6,7,8,9,11,12,14,15,16,17-dodecahydrocyclopenta[a]phenanthren-17-yl]acetate Chemical compound C([C@H]1O)C2=CC(=O)C[C@H](O)[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H](CC(=O)OC)[C@@]1(C)CC2 KFOPKOFKGJJEBW-ZSSYTAEJSA-N 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
Classifications
-
- 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
Landscapes
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a robot cluster coverage method and system based on sliding mode control and with different maximum speeds, and relates to the technical field of robot cluster control. The technical key points of the invention include: calculating and acquiring a plurality of optimal coverage positions of a plurality of robots according to the respective maximum running speed of each robot; and designing an anti-interference sliding mode controller so as to control the robot to travel to the optimal coverage position by using the sliding mode controller. The method comprises the steps of calculating a plurality of optimal coverage positions of a plurality of robots, solving the problem of calculating the optimal coverage positions of time sensitive services required by discrete clients and solving the problem of considering time-dependent task distribution under the background of practical application; the anti-interference sliding mode controller considers the disturbance of robot clusters with different maximum speeds on angle control, and can enable the robots to stably reach the optimal coverage position.
Description
Technical Field
The invention relates to the technical field of robot cluster control, in particular to a robot cluster coverage method and system based on sliding mode control and with different maximum speeds.
Background
Overlay control is one of the most important studies in the field of multiple robots. The robot is mainly used for realizing the optimal distribution of robots in space according to the distribution of clients, and providing better service for the clients. Coverage requires that each robot be able to detect and provide services to the customer. Because of its wide industrial and living background, it is widely used in the fields of rescue, environmental monitoring, space exploration, etc. in military, emergency and disaster. Many scholars are engaged in overlay control research and a distributed control law is designed to optimize the area of the sensing measurement task that implements multiple agent locations. From taking gradient descent and Voronoi partitioning of the cost function, many researchers have further investigated coverage control issues of multi-agent networks and consider physical constraints of mobile robots, such as speed limits, limited sensing or communication range, and information density of task areas, among others.
In many practical scenarios of overlay tasks, clients of the robotic service are typically discretely distributed. Thus, many of the prior art techniques are no longer reliable. In addition, most of the previous work only considers maximization of coverage area or minimization of coverage energy consumption, but in many practical applications, such as search and rescue tasks, it is mainly considered to act as soon as possible and provide services, i.e. to minimize the time for the robot to reach each customer in the task area. Moreover, for clusters of robots of different maximum speeds, there is relatively little research available and the presence of disturbances is rarely considered in the control of the robots. The most widely used at present is the PID control algorithm, which is simply implemented, and in some simple systems, PID control can provide good stability and robustness, but for highly nonlinear systems, PID control cannot provide enough adaptability, and in the case of large system parameter variation or difficult accurate measurement, PID control performance also decreases.
Disclosure of Invention
In view of the above problems, the invention provides a robot cluster coverage method and system based on sliding mode control and with different maximum speeds.
According to an aspect of the invention, a robot cluster coverage method based on sliding mode control at different maximum speeds is provided, and the method comprises the following steps:
calculating and acquiring a plurality of optimal coverage positions of a plurality of robots according to the respective maximum running speed of each robot;
and designing an anti-interference sliding mode controller so as to control the robot to travel to the optimal coverage position by using the sliding mode controller.
Further, the dynamics model of the robot is expressed in a polar coordinate system as:
Wherein ρ i represents the distance from the robot to the target point; alpha i represents the included angle between the speed direction of the robot and the target direction; v i denotes the robot linear velocity; ω i represents the angular velocity of the robot.
Further, the anti-interference sliding mode controller is designed to:
Wherein c represents a coefficient in a proportional-integral sliding-mode function s, and c is more than 0; k > 0; d is more than or equal to |d|, D represents disturbance existing on the control of the angle alpha i; sgn (·) is a sign function.
Further, the proportional-integral sliding-mode function s is expressed as:
s(t)＝e(t)+c∫e(t)dt
Wherein e (t) represents an error; c must satisfy the Hurwitz condition, i.e., c > 0.
Further, the disturbance d=asin (t) or d=acos (t), where a is a constant greater than 0.
Further, the calculating and acquiring a plurality of optimal coverage positions of the plurality of robots according to the respective maximum traveling speeds of each robot includes:
given initial position of robot The set of clients that the ith robot can service fastest in the τ iteration is represented as:
Wherein, Representing the iteration number; i=1, 2 …, z, z representing the total number of robots; g j represents/>Corresponding customer locations in the database; p i denotes the position of the i-th robot; p k denotes the position of the kth robot; mu i represents the i-th robot maximum speed; mu k represents the maximum speed of the kth robot; /(I)Representing a position set of the robot cluster iterated for the τ time; /(I)Representing a set of customer locations;
calculating the optimal coverage position of each robot in each iteration according to the following formula according to the client set corresponding to each robot:
In the method, in the process of the invention, Representation/>The number of clients; /(I)Representing the expected coverage position of the ith robot after iteration tau+1;
When (when) Mesh/>When the optimal coverage position of each robot is determined to be/>
According to another aspect of the invention, a robot cluster overlay system based on different maximum speeds of a sliding mode control is proposed, the system comprising:
an optimal coverage position acquisition module configured to acquire a plurality of optimal coverage positions of a plurality of robots according to respective maximum travel speeds of each robot;
And the sliding mode control module is configured to design an anti-interference sliding mode controller so as to control the robot to travel to the optimal coverage position by using the sliding mode controller.
Further, the dynamic model of the robot in the sliding mode control module is expressed as follows in a polar coordinate system:
Wherein ρ i represents the distance from the robot to the target point; alpha i represents the included angle between the speed direction of the robot and the target direction; v i denotes the robot linear velocity; omega i denotes the robot angular velocity;
The anti-interference sliding mode controller is designed as follows:
Wherein c represents a coefficient in a proportional-integral sliding-mode function s, and c is more than 0; k > 0; d is more than or equal to |d|, D represents disturbance existing on the control of the angle alpha i; sgn (·) is a sign function.
Further, the disturbance d=asin (t) or d=acos (t) in the sliding mode control module, wherein a is a constant greater than 0.
Further, the process of calculating and acquiring the plurality of optimal coverage positions of the plurality of robots according to the respective maximum running speeds of each robot in the optimal coverage position acquisition module includes:
given initial position of robot The set of clients that the ith robot can service fastest in the τ iteration is represented as:
Wherein, Representing the iteration number; i=1, 2 …, z, z representing the total number of robots; g j represents/>Corresponding customer locations in the database; p i denotes the position of the i-th robot; p k denotes the position of the kth robot; mu i represents the i-th robot maximum speed; mu k represents the maximum speed of the kth robot; /(I)Representing a position set of the robot cluster iterated for the τ time; /(I)Representing a set of customer locations;
calculating the optimal coverage position of each robot in each iteration according to the following formula according to the client set corresponding to each robot:
In the method, in the process of the invention, Representation/>The number of clients; /(I)Representing the expected coverage position of the ith robot after iteration tau+1;
When (when) And/>When the optimal coverage position of each robot is determined to be/>
The beneficial technical effects of the invention are as follows:
The invention provides a robot cluster coverage method and system based on sliding mode control and with different maximum speeds, which mainly consider that a group of robots with different maximum speeds are used for covering a group of time-sensitive discrete clients. The overall design is divided into two parts, wherein the first part aims at the time-sensitive coverage control problem, and a numerical optimizer is designed to be solved into the optimal coverage position of the robot; the second part is provided with a sliding mode controller for performing disturbance rejection control on a controllable variable omega i; and finally, driving the robot to travel from the initial position to the optimal coverage control position by using the sliding mode controller, and then preparing to provide service for discrete clients at any moment. The optimal coverage position numerical optimizer solves the problem of calculating the optimal coverage position of the time sensitive service required by the discrete client and the problem of considering time-dependent task distribution under the background of practical application; the disturbance-resistant sliding mode controller considers the disturbance of the incomplete mobile robot with constant-speed constraint on angle control, and can enable the robot to stably reach the optimal coverage position.
Drawings
The invention may be better understood by reference to the following description taken in conjunction with the accompanying drawings, which are included to provide a further illustration of the preferred embodiments of the invention and to explain the principles and advantages of the invention, together with the detailed description below.
Fig. 1 is a flowchart of a robot cluster coverage method based on different maximum speeds of sliding mode control according to an embodiment of the present invention.
Fig. 2 is a block diagram of a robot cluster coverage method based on different maximum speeds of sliding mode control according to an embodiment of the present invention.
FIG. 3 is a diagram showing a simulation example of the coverage effect in the embodiment of the present invention.
Fig. 4 is a graph of distance change between four robots and their respective optimal coverage positions in a simulation in an embodiment of the present invention.
Fig. 5 is a graph showing changes in α during traveling of four robots in a simulation in an embodiment of the present invention.
FIG. 6 is a diagram of global objective functions in simulation in an embodiment of the present inventionA graph of variation with iteration number.
Fig. 7 is a control input diagram of four robot slipform controllers in a simulation in an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, exemplary embodiments or examples of the present invention will be described below with reference to the accompanying drawings. It is apparent that the described embodiments or examples are only implementations or examples of a part of the invention, not all. All other embodiments or examples, which may be made by one of ordinary skill in the art without undue burden, are intended to be within the scope of the present invention based on the embodiments or examples herein.
The embodiment of the invention provides a robot cluster coverage method based on sliding mode control at different maximum speeds, which comprises the following steps as shown in fig. 1:
Step one, calculating and obtaining a plurality of optimal covering positions of a plurality of robots according to the respective maximum driving speed of each robot;
And secondly, designing an anti-interference sliding mode controller so as to control the robot to travel to an optimal coverage position by using the sliding mode controller.
The method starts in step one. In the first step, a plurality of optimal coverage positions of a plurality of robots are obtained according to the respective maximum running speed calculation of each robot.
According to an embodiment of the present invention, as shown in FIG. 2, first, consider a network of z mobile robots in a piece of known two-dimensional task space H ε R 2. Definition of the definitionIs a collection of robots, where p i e H is the position of the ith robot.
Giving a kinetic model of the robot:
Where x i,yi is the coordinate of the ith robot on the two-dimensional plane, θ i is the angle between the speed direction of the ith robot and the positive x axis. v i,ωi is the linear and angular speed, respectively, of the ith robot, where v i belongs to the set μ= { μ 1,μ2,…,μz }, where μ 1,μ2,…,μz is the normal number representing the maximum speed of each robot.
Meanwhile, discrete clients are distributed in a task space H, and the set is G= { G 1,g2,...gn }, wherein G i epsilon H is the position of the ith client, and n is the number of clients. Robots are specialized to provide some time sensitive services to customers. When a customer initiates a service request, the robot is to travel to the customer at its maximum speed. Evaluating the quality of service of each customer is primarily by the minimum arrival time of the robot, i.e
Wherein 2 represents the calculated euclidean distance.
Assuming that the number of clients n is much greater than the number of robots z, the global objective function may be set to:
given initial position of robot Define the responsible client set of the ith robot:
Wherein, Representing the number of iterations of the numerical optimizer; i=1, 2 …, z, z representing the total number of robots; g j represents/>Corresponding customer locations in the database; i+.k, p i denotes the position of the i-th robot; p k denotes the position of the kth robot; mu i represents the i-th robot maximum speed; mu k represents the maximum speed of the kth robot; /(I)Representing a position set of the robot cluster iterated for the τ time; /(I)Representing a set of customer locations; according to definition/>Is the set of clients that the ith robot can provide service fastest for the τ iteration. The collection/>, of responsible clients for each robot can then be obtained
Then calculate the optimal coverage position for each robotThe optimal coverage control position can be found by the following formula.
Wherein the method comprises the steps ofA client set which is responsible for the ith iteration of the ith robot; g j is a component of/>Is the location of the customer; /(I)Is/>Is the number of customers. /(I)Representing the desired coverage position of the ith robot after τ+1 iterations of the numerical optimizer.
The position of the robot is also updated as:
The coverage position obtained by each iteration of the numerical optimizer can improve the coverage performance, namely the global objective function Monotonically decreasing as the number of iterations of the numerical optimizer increases until/>To the minimum, namely the client set/>, which is responsible for the robotOverlay location/>, of clients and robots in (a)Will not change as the number of iterations of the numerical optimizer increases, i.e./> And equation (4) reaches a minimum value.
Then, in the second step, an anti-interference sliding mode controller is designed to control the robot to travel to the optimal coverage position by using the sliding mode controller.
According to the embodiment of the invention, as shown in fig. 2, the numerical optimizer designed according to the above obtains an optimal coverage control position, and then the sliding mode controller is designed to control the robot to travel to the optimal coverage control position obtained by the numerical optimizer.
Firstly, converting a dynamics model of a robot from a rectangular coordinate system to a polar coordinate system to obtain the following model:
where ρ i and α i represent the distance of the mobile robot to the target point and the angle of the mobile robot speed direction to the target direction, respectively, v i,ωi is the linear speed and angular speed of the ith robot, respectively, since in order to reach the optimal coverage control position quickly, the robot travels at its maximum speed, v i＝μi. The purpose of the final control is to make (ρ i,αi) = (0, 0). So the control α i converges to 0 and the robot can move to the desired coverage position. However, considering that there is a disturbance in the actual operation of the robot, which may cause the robot to not accurately travel to the optimal coverage control position, an anti-disturbance sliding mode controller is designed for ω i.
Suppose there is a disturbance d in the angular control:
Where d=asin (t) or d=acos (t), a is a constant greater than 0. Preferably, a is a constant greater than 0 and less than 2. As an example, d=0.5 sin (t).
Then, a proportional-integral sliding mode function is designed as follows:
s(t)＝e(t)+c∫e(t)dt (9)
wherein c must satisfy the Hurwitz condition, i.e., c > 0; the error e (t) and its derivative are:
e(t)＝αi-αd＝αi (10)
Where α d is the ideal angle signal, α d =0 because it is desired to control α i to 0.
The derivatives of the sliding mode functions according to equations (8), (10), (11) are as follows:
the lyapunov function is defined as:
To ensure that The sliding mode controller is designed as follows:
wherein sgn (·) is a sign function, k > 0, D > d|.
Deriving the lyapunov function:
i.e., lyapunov function derivative V' < 0. According to the Lyapunov stability discrimination method, the sliding mode controller designed according to the formula (14) is used for gradually stabilizing the robot cluster control system.
As a preferred embodiment of the invention, the method of the invention is carried out as follows:
1) And calculating which robot each customer belongs to according to the maximum running speed of each robot to serve the same as formula (4).
2) Grouping clients served by robots into one category
3) The expected coverage position of the robot is calculated by the formula (5) and the robot position is updated as in the formula (6).
4) The above three steps are looped, namely the numerical optimizer iterates until S andNo longer changes to obtain optimal coverage position/>
5) And designing a sliding mode controller, and adjusting parameters of c, k and D in the designed sliding mode controller to obtain proper parameters.
6) And using the designed sliding mode controller to control the robot to drive to the optimal coverage position obtained by the numerical optimizer and to provide time-sensitive service for each customer at any time.
The sliding mode controller designed by the invention can well resist disturbance, so that the robot can drive to the obtained optimal coverage position.
Further, the parameters c, k and D in the controller can be obtained through multiple times of debugging in a simulation experiment, and the optimal values of the parameters c, k and D are respectively 10, 5 and 0.5.
Fig. 3 is a simulation diagram of a coverage effect obtained by using four robots with initial positions at the lower left corner to cover 100 random clients and optimizing the coverage positions and controls by the method of the invention. Fig. 4 is a graph of the distance change of four robots from their respective optimal coverage positions in a simulation. Fig. 5 is a graph showing changes in α during traveling of four robots in simulation. FIG. 6 is a global objective function in a simulationA graph of variation with iteration number. Fig. 7 is a control input diagram of four robot slipform controllers in a simulation.
As can be seen from fig. 3 to fig. 7, the robot cluster coverage method based on the sliding mode control and with different maximum speeds can well complete the coverage task of the discrete client based on time, thereby ensuring the coverage performance of the coverage task and solving the problem of disturbance in the running process of the robot.
Another embodiment of the present invention proposes a robot cluster overlay system based on sliding mode control at different maximum speeds, the system comprising:
an optimal coverage position acquisition module configured to acquire a plurality of optimal coverage positions of a plurality of robots according to respective maximum travel speeds of each robot;
And the sliding mode control module is configured to design an anti-interference sliding mode controller so as to control the robot to travel to the optimal coverage position by using the sliding mode controller.
In this embodiment, preferably, the dynamic model of the robot in the sliding mode control module is expressed as:
Wherein ρ i represents the distance from the robot to the target point; alpha i represents the included angle between the speed direction of the robot and the target direction; v i denotes the robot linear velocity; omega i denotes the robot angular velocity;
The anti-interference sliding mode controller is designed as follows:
Wherein c represents a coefficient in a proportional-integral sliding-mode function s, and c is more than 0; k > 0; d is more than or equal to |d|, D represents disturbance existing on the control of the angle alpha i; sgn (·) is a sign function.
In this embodiment, preferably, the disturbance d=asin (t) or d=acos (t) in the sliding mode control module, where a is a constant greater than 0.
In this embodiment, preferably, the process of calculating and acquiring the plurality of optimal coverage positions of the plurality of robots according to the respective maximum travel speeds of each robot in the optimal coverage position acquisition module includes:
given initial position of robot The set of clients that the ith robot can service fastest in the τ iteration is represented as:
Wherein, Representing the iteration number; i=1, 2., z, z represents the total number of robots; g j represents/>Corresponding customer locations in the database; p i denotes the position of the i-th robot; p k denotes the position of the kth robot; mu i represents the i-th robot maximum speed; mu k represents the maximum speed of the kth robot; /(I)Representing a position set of the robot cluster iterated for the τ time; /(I)Representing a set of customer locations;
calculating the optimal coverage position of each robot in each iteration according to the following formula according to the client set corresponding to each robot:
In the method, in the process of the invention, Representation/>The number of clients; /(I)Representing the expected coverage position of the ith robot after iteration tau+1;
When (when) And/>When the optimal coverage position of each robot is determined to be/>
The functions of the robot cluster coverage system with different maximum speeds based on sliding mode control according to the embodiment of the present invention may be described by the aforementioned robot cluster coverage method with different maximum speeds based on sliding mode control, so that the system embodiment is not described in detail, and reference may be made to the above method embodiment, which is not described herein.
While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments are contemplated within the scope of the invention as described herein. The disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is defined by the appended claims.
Claims (6)
1. The robot cluster covering method based on the sliding mode control at different maximum speeds is characterized by comprising the following steps of:
Calculating and acquiring a plurality of optimal coverage positions of a plurality of robots according to the respective maximum running speed of each robot; the method specifically comprises the following steps:
given initial position of robot The set of clients that the ith robot can service fastest in the τ iteration is represented as:
Wherein, Representing the iteration number; i=1, 2., z, z represents the total number of robots; g j represents/>Corresponding customer locations in the database; p i denotes the position of the i-th robot; p k denotes the position of the kth robot; mu i represents the i-th robot maximum speed; mu k represents the maximum speed of the kth robot; /(I)Representing a position set of the robot cluster iterated for the τ time; /(I)Representing a set of customer locations;
calculating the optimal coverage position of each robot in each iteration according to the following formula according to the client set corresponding to each robot:
In the method, in the process of the invention, Representation/>The number of clients; /(I)Representing the expected coverage position of the ith robot after iteration tau+1;
When (when) And/>When the optimal coverage position of each robot is determined to be/>
Designing an anti-interference sliding mode controller to control the robot to run to an optimal coverage position by using the sliding mode controller; the anti-interference sliding mode controller is designed as follows:
Wherein, alpha i represents an included angle between the speed direction of the robot and the target direction; ρ i represents the distance of the robot to the target point; v i denotes the robot linear velocity; s represents a proportional-integral sliding-mode function, s=s (t) =e (t) +c+e (t) dt, e (t) represents an error, c represents a coefficient in the proportional-integral sliding-mode function S, c satisfies the Hurwitz condition, c >0; k >0; d is more than or equal to |d| and d represents disturbance existing on control of the included angle alpha i; sgn (·) is a sign function.
2. The method for covering clusters of robots of different maximum speeds based on sliding mode control according to claim 1, characterized in that the dynamics model of the robot is expressed in polar coordinate system as:
wherein ρ i represents the distance from the robot to the target point; alpha i represents the included angle between the speed direction of the robot and the target direction; v i denotes the robot linear velocity; ω i represents the robot angular velocity.
3. The method of robot cluster coverage based on different maximum speeds of sliding mode control according to claim 1, wherein the disturbance d = Asin (t) or d = Acos (t), where a is a constant greater than 0.
4. Robot cluster covering system based on sliding mode control's different maximum speeds, characterized by comprising:
An optimal coverage position acquisition module configured to acquire a plurality of optimal coverage positions of a plurality of robots according to respective maximum travel speeds of each robot; the process comprises the following steps:
given initial position of robot The set of clients that the ith robot can service fastest in the τ iteration is represented as:
Wherein, Representing the iteration number; i=1, 2., z, z represents the total number of robots; g j represents/>Corresponding customer locations in the database; p i denotes the position of the i-th robot; p k denotes the position of the kth robot; mu i represents the i-th robot maximum speed; mu k represents the maximum speed of the kth robot; /(I)Representing a position set of the robot cluster iterated for the τ time; /(I)Representing a set of customer locations;
calculating the optimal coverage position of each robot in each iteration according to the following formula according to the client set corresponding to each robot:
In the method, in the process of the invention, Representation/>The number of clients; /(I)Representing the expected coverage position of the ith robot after iteration tau+1;
When (when) And/>When the optimal coverage position of each robot is determined to be/>
A slip-form control module configured to design an anti-interference slip-form controller to control the robot to travel to its optimal coverage position using the slip-form controller; the anti-interference sliding mode controller is designed as follows:
Wherein, alpha i represents an included angle between the speed direction of the robot and the target direction; ρ i represents the distance of the robot to the target point; v i denotes the robot linear velocity; s represents a proportional-integral sliding-mode function, s=s (t) =e (t) +c+e (t) dt, e (t) represents an error, c represents a coefficient in the proportional-integral sliding-mode function s, c satisfies the Hurwitz condition, c >0; k >0; d is more than or equal to |d|, D represents disturbance existing on control of the included angle alpha i; sgn (·) is a sign function.
5. The robot cluster overlay system of different maximum speeds based on sliding mode control of claim 4, wherein the dynamics model of the robot in the sliding mode control module is expressed in polar coordinate system as:
wherein ρ i represents the distance from the robot to the target point; alpha i represents the included angle between the speed direction of the robot and the target direction; v i denotes the robot linear velocity; ω i represents the robot angular velocity.
6. The robot cluster overlay system of different maximum speeds based on slip-mode control of claim 4, wherein the disturbance in the slip-mode control module d = Asin (t) or d = Acos (t), where a is a constant greater than 0.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311811151.0A CN117706910B (en) | 2023-12-27 | 2023-12-27 | Robot cluster coverage method and system based on sliding mode control and with different maximum speeds |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311811151.0A CN117706910B (en) | 2023-12-27 | 2023-12-27 | Robot cluster coverage method and system based on sliding mode control and with different maximum speeds |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117706910A CN117706910A (en) | 2024-03-15 |
| CN117706910B true CN117706910B (en) | 2024-06-07 |
Family
ID=90149712
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311811151.0A Active CN117706910B (en) | 2023-12-27 | 2023-12-27 | Robot cluster coverage method and system based on sliding mode control and with different maximum speeds |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117706910B (en) |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108388270A (en) * | 2018-03-21 | 2018-08-10 | 天津大学 | Cluster unmanned plane track posture cooperative control method towards security domain |
| CN108614426A (en) * | 2018-06-06 | 2018-10-02 | 黄山学院 | A kind of Multi Mobile Robots Formation's robust control method based on interference observer |
| CN111673742A (en) * | 2020-05-22 | 2020-09-18 | 江苏信息职业技术学院 | Industrial robot trajectory tracking control algorithm |
| CN111708369A (en) * | 2020-07-17 | 2020-09-25 | 武汉科技大学 | Route planning method for transformer substation inspection robot |
| CN112077847A (en) * | 2020-09-08 | 2020-12-15 | 西华大学 | Position tracking control method of robot interfered by non-matching |
| US10962976B1 (en) * | 2019-11-29 | 2021-03-30 | Institute Of Automation, Chinese Academy Of Sciences | Motion control method and system for biomimetic robotic fish based on adversarial structured control |
| CN113051807A (en) * | 2021-04-17 | 2021-06-29 | 西北工业大学 | Multi-agent coverage method for complex area based on k-Means and sliding mode variable structure control |
| CN113721607A (en) * | 2021-08-16 | 2021-11-30 | 燕山大学 | Optimal control method for tracking track of wheeled robot |
| CN114967703A (en) * | 2022-06-15 | 2022-08-30 | 华中科技大学 | Robot coverage control method, control system and controller based on time cost |
| CN115236973A (en) * | 2022-07-27 | 2022-10-25 | 浙江工业大学 | AGV trajectory tracking control method based on PSO Lyapunov function |
| CN115616922A (en) * | 2022-12-19 | 2023-01-17 | 安徽大学 | Time coverage control method for heterogeneous mobile robot cluster |
| CN115665757A (en) * | 2022-10-28 | 2023-01-31 | 东南大学 | Distributed unknown time-varying region multi-autonomous-body optimal coverage method and system |
| CN116142497A (en) * | 2023-02-08 | 2023-05-23 | 北京航空航天大学 | Mars unmanned aerial vehicle and control method based on task manifold controller |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10353399B2 (en) * | 2017-07-21 | 2019-07-16 | AI Incorporated | Polymorphic path planning for robotic devices |
| US11340610B2 (en) * | 2018-07-24 | 2022-05-24 | Huili Yu | Autonomous target following method and device |
| CN112621759B (en) * | 2020-12-28 | 2021-12-24 | 燕山大学 | Teleoperation system fractional order sliding mode synchronous control method based on event trigger mechanism |
| US20230219230A1 (en) * | 2020-12-18 | 2023-07-13 | Strong Force Vcn Portfolio 2019, Llc | Fleet Management Platform for Multi-Purpose Robots |
-
2023
- 2023-12-27 CN CN202311811151.0A patent/CN117706910B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108388270A (en) * | 2018-03-21 | 2018-08-10 | 天津大学 | Cluster unmanned plane track posture cooperative control method towards security domain |
| CN108614426A (en) * | 2018-06-06 | 2018-10-02 | 黄山学院 | A kind of Multi Mobile Robots Formation's robust control method based on interference observer |
| US10962976B1 (en) * | 2019-11-29 | 2021-03-30 | Institute Of Automation, Chinese Academy Of Sciences | Motion control method and system for biomimetic robotic fish based on adversarial structured control |
| CN111673742A (en) * | 2020-05-22 | 2020-09-18 | 江苏信息职业技术学院 | Industrial robot trajectory tracking control algorithm |
| CN111708369A (en) * | 2020-07-17 | 2020-09-25 | 武汉科技大学 | Route planning method for transformer substation inspection robot |
| CN112077847A (en) * | 2020-09-08 | 2020-12-15 | 西华大学 | Position tracking control method of robot interfered by non-matching |
| CN113051807A (en) * | 2021-04-17 | 2021-06-29 | 西北工业大学 | Multi-agent coverage method for complex area based on k-Means and sliding mode variable structure control |
| CN113721607A (en) * | 2021-08-16 | 2021-11-30 | 燕山大学 | Optimal control method for tracking track of wheeled robot |
| CN114967703A (en) * | 2022-06-15 | 2022-08-30 | 华中科技大学 | Robot coverage control method, control system and controller based on time cost |
| CN115236973A (en) * | 2022-07-27 | 2022-10-25 | 浙江工业大学 | AGV trajectory tracking control method based on PSO Lyapunov function |
| CN115665757A (en) * | 2022-10-28 | 2023-01-31 | 东南大学 | Distributed unknown time-varying region multi-autonomous-body optimal coverage method and system |
| CN115616922A (en) * | 2022-12-19 | 2023-01-17 | 安徽大学 | Time coverage control method for heterogeneous mobile robot cluster |
| CN116142497A (en) * | 2023-02-08 | 2023-05-23 | 北京航空航天大学 | Mars unmanned aerial vehicle and control method based on task manifold controller |
Non-Patent Citations (4)
| Title |
|---|
| 《Dynamic Coverage Control Based on K-Means》;Dengxiu Yu 等;《IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS》;20210519;第1-9页 * |
| 《ＴＡＭＰ：面向区域覆盖的层次化多机器人任务分配方法》;安浩嘉 等;《计算机科学》;20230904;第1-9页 * |
| 《Time-sensitive coverage control for non-holonomic and heterogeneous robots:An extremum seeking framework and application》;Liang Zhang 等;《IET Control Theory & Applications》;20230206;第1-11页 * |
| 《移动机器人的线性自抗扰控制设计与实验验证》;王会明 等;《控制理论与应用》;20220731;第1-8页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117706910A (en) | 2024-03-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109597864B (en) | Method and system for real-time positioning and map construction of ellipsoid boundary Kalman filtering | |
| CN109947136B (en) | Collaborative active sensing method for unmanned aerial vehicle group rapid target search | |
| CN110398969A (en) | Automatic driving vehicle adaptive prediction time domain rotating direction control method and device | |
| CN110376882A (en) | Pre-determined characteristics control method based on finite time extended state observer | |
| CN112148036B (en) | Bilateral tracking control method of fixed time estimator of networked robot system | |
| Chang et al. | A new fuzzy strong tracking cubature Kalman filter for INS/GNSS | |
| CN106681345A (en) | Crowd-searching-algorithm-based active-disturbance-rejection control method for unmanned plane | |
| CN109976189A (en) | A kind of intelligence naval vessels automatic cruising analog simulation method | |
| CN117369495A (en) | Unmanned aerial vehicle formation track planning method based on model predictive control | |
| Zhang et al. | Distributed control of nonholonomic robots without global position measurements subject to unknown slippage constraints | |
| CN117706910B (en) | Robot cluster coverage method and system based on sliding mode control and with different maximum speeds | |
| Zhang et al. | Adaptive fault-tolerant sliding mode control for high-speed trains with actuator faults under strong winds | |
| CN116755323A (en) | Multi-rotor unmanned aerial vehicle PID self-tuning method based on deep reinforcement learning | |
| CN110162084B (en) | Formation control method of flying missile cluster system based on consistency theory | |
| Pushpangathan et al. | Robust simultaneously stabilizing decoupling output feedback controllers for unstable adversely coupled nano air vehicles | |
| Yang et al. | Sliding mode predictive active fault-tolerant control method for discrete multi-faults system | |
| Medina et al. | Uncertainty-dependent optimal control for robot control considering high-order cost statistics | |
| Guan et al. | A new integrated navigation system for the indoor unmanned aerial vehicles (UAVs) based on the neural network predictive compensation | |
| Liang et al. | Fractional order nonsingular terminal sliding mode cooperative fault-tolerant control for high-speed trains with actuator faults based on grey wolf optimization | |
| CN115422698A (en) | Servo drive variable-period online inertia identification method | |
| CN114861435A (en) | Multi-agent system consistency sliding mode control algorithm under finite time observer | |
| Liang et al. | Adaptive control of mechanical servo system with glowworm swarm friction identification | |
| Shao et al. | A novel distributed source seeking method based on multi-robots flocking | |
| Liang et al. | An improved UAV path optimization algorithm for target accurately and quickly localization | |
| Salman et al. | Controlling a vehicle braking and longitudinal acceleration using a seeking control approach |
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 |