CN118143949A - Cooperative control method for multiple intelligent transfer robots - Google Patents

Abstract

The invention relates to the technical field of cooperative control of robots, and discloses a cooperative control method of a multi-intelligent transfer robot, which comprises the following steps: the carrying robot acquires a regional point set through a sensor of the carrying robot, and constructs a local environment map according to the regional point set; constructing a global transfer robot system control model and carrying out real-time solving to obtain real-time control parameters of different transfer robots; the carrying robot carries out cooperative control carrying according to the real-time control parameters. The invention adopts a cooperative control exploration mode of the carrying robots to further explore the regional point set of each subregion, quickly obtains global environment map representation of a factory region, builds a global carrying robot system control model by taking the global yaw degree as a target in the carrying process to reduce the collision probability among the carrying robots, and enables the carrying robots to reach the cooperative carrying real-time control parameters of the target carrying grid coordinates as soon as possible, thereby realizing the cooperative carrying in a multi-carrying robot scene.

Description

Cooperative control method for multiple intelligent transfer robots

Technical Field

The invention relates to the technical field of cooperative control of robots, in particular to a cooperative control method of multiple intelligent transfer robots.

Background

The transfer robot plays an important role in modern industrial production, and can realize tasks such as automatic transfer, assembly and material handling. With the continuous expansion of the production scale of factories and the complexity of production lines, a single transfer robot has been difficult to meet the production requirements, and the cooperative work among a plurality of transfer robots has become an important problem to be solved. In view of the above, the invention provides a multi-intelligent transfer robot cooperative control method, which realizes the cooperative work of the multi-transfer robots through an intelligent control technology, improves the overall efficiency of a production line, and meets the actual demands of rapidly coping with market demand changes and production task scheduling.

Disclosure of Invention

In view of the above, the present invention provides a cooperative control method for multiple intelligent transfer robots, which aims to: 1) Dividing a factory area into subareas, setting an automatic exploration method for the transfer robots, when the transfer robots move according to the generated moving direction and moving step length, if an obstacle is perceived, continuing to randomly generate and move the section of route, avoiding the generation of the section of route by a traditional exploration algorithm to obtain an area point set, combining the current positions of different transfer robots, adopting a transfer robot cooperative control exploration mode to further explore the area point set of each subarea, improving exploration efficiency, obtaining the area point set after cooperative control exploration, transmitting local environment maps of areas where different transfer robots are located to a background server for integration, and obtaining a global environment map representation of the factory area; 2) And constructing a global transfer robot system control model by taking the running speed and the direction of the transfer robots as control parameters and taking the transfer robot braking parameters as constraint conditions and taking the global yaw degree in the minimum transfer process as a target, carrying out unconstrained processing on the objective function by combining the constraint conditions, solving the objective function, obtaining cooperative transfer real-time control parameters for reducing the collision probability between the transfer robots and enabling the transfer robots to reach the target transfer grid coordinates as soon as possible, and realizing cooperative transfer under a multi-transfer robot scene.

The invention provides a multi-intelligent transfer robot cooperative control method, which comprises the following steps:

S1: the transfer robot acquires a regional point set through a sensor of the transfer robot, and constructs a local environment map according to the regional point set, wherein an improved rapid-growth random tree is a main implementation method for acquiring the regional point set;

s2: the method comprises the steps that local environment maps of areas where different carrying robots are located are sent to a background server to be integrated, a global carrying robot system control model is built, the global carrying robot system control model takes the running speed and the running direction of the carrying robots as control parameters, takes the braking parameters of the carrying robots as constraint conditions, and takes the global yaw degree in the carrying process as a target;

s3: carrying out real-time solving on the constructed global transfer robot system control model to obtain real-time control parameters of different transfer robots, wherein the self-adaptive penalty parameters are the main implementation method of the optimized solving;

S4: and sending the solved real-time control parameters of different transfer robots to the corresponding transfer robots, and carrying out cooperative control transfer by the transfer robots according to the real-time control parameters.

As a further improvement of the present invention:

optionally, the step S1 of the carrying robot obtaining the regional point set through its own sensor includes:

dividing a factory area into K sub-areas, arranging a carrying robot in each sub-area, and acquiring an area point set of each sub-area by the carrying robot by using a sensor of the carrying robot, wherein the area point set acquisition flow of the kth sub-area is as follows:

S11: the carrying robot adds the initial position to the region point set V _k of the kth sub-region;

S12: extracting the most recently added position from the region point set V _k Randomly generating a moving direction of the transfer robot, and moving the transfer robot according to the generated moving direction and a moving step alpha;

S13: if the sensor of the transfer robot does not sense the front obstacle during the movement, the position is moved Adding the set of region points V _k, and returning to the step S12;

if the sensor of the transfer robot senses a front obstacle during the movement, the position where the front obstacle is sensed is taken as the movement position Will move positionAdding the set of region points V _k, and returning to the step S12;

S14: repeating the steps S12-S13 to obtain a regional point set V _k containing a plurality of positions:

V_k＝{V_k(i)|i∈[1,num_k]}

Wherein:

V _k (i) represents the i-th position in the region point set V _k, num _k represents the total number of positions in the region point set V _k;

s15: further exploring the regional point set of each subarea in a cooperative control exploration mode of the carrying robot to obtain a regional point set after cooperative control exploration, wherein the regional point set after cooperative control exploration of the kth subarea is V' _k;

constructing a local environment map according to the regional point set, wherein the local environment map construction flow of the kth sub-region is as follows:

initializing to generate a local environment map, connecting adjacent positions in the local environment map according to the occurrence sequence of the positions in the regional point set V' _k, wherein the connection result is a passable position sequence in the local environment map, and marking the positions in the passable position sequence as passable;

Marking the searched position and the position marking result in a local environment map;

The position in the local environment map which is not marked currently is marked as non-passable.

Optionally, the further exploring the regional point set of each sub-region by adopting a mode of exploring cooperative control of the transfer robot to obtain a regional point set after exploring cooperative control, including:

further exploring the regional point set of each subregion by adopting a mode of cooperative control exploration of the transfer robot to obtain a regional point set after cooperative control exploration, wherein the further exploration flow of the regional point set V _k is as follows:

calculating to obtain a drift value of any position in the regional point set V _k, wherein the drift value of the position V _k (i) is:

Wherein:

V _k (i) represents the drift value of position V _k (i);

Omega _k (i) represents a position set composed of positions existing in a region point set V _k in a region with a radius R with a position V _k (i) as a center, V epsilon omega _k (i), and V represents any position in the position set omega _k (i);

Sum _k (i) represents the total number of positions in the set of positions Ω _k (i);

And carrying out drift processing on any position in the regional point set V _k based on the drift value, wherein a drift processing formula of the position V _k (i) is as follows:

V_k(i)←V_k(i)+v_k(i)

Forming a set of positions after the drifting processing, performing iterative operation of the drifting processing until the positions in the set are unchanged, taking the positions in the set at the moment as key positions of the kth sub-area, and forming J _k key positions in the set into a key position set of the kth sub-area:

Wherein:

A j-th key position representing a k-th sub-region;

obtaining the current positions of all the transfer robots, and calculating to obtain a set of key positions reached by the current position of each robot The method comprises the steps of commanding a robot with the current position of the robot with the highest profit function to search the critical position to obtain the position of the critical position and a position marking result, wherein the position marking result is whether the critical position can pass or not, and the current position loc of the robot corresponds to the critical positionThe profit function value of (2) is:

Wherein:

representing the current position loc of the robot versus the key position/> Is a function value of the income;

Expressed as position/> The number of positions present in the set of region points V _k in the region of radius R;

representing the current position loc and the key position/>, of the robot A Euclidean distance between them;

λ represents the exploration cost weight.

Optionally, in the step S2, the sending the local environment map of the area where the different handling robots are located to the background server for integration includes:

the method comprises the steps of sending local environment maps of areas where different transfer robots are located to a background server for integration to obtain a global environment map of a factory area, dividing the global environment map into grids, wherein the area of each grid after division is equal to the occupied area of the transfer robot, marking the grids after division, marking the grids as passable if passable positions exist in the grids, otherwise marking the grids as not passable, and the representation form of the global environment map is as follows:

Map＝(Map(a,b))_A×B

inf＝{inf_ab＝((x_a,y_b),β_ab)|a∈[1,A],b∈[1,B]}

Wherein:

map is a matrix representation form of the global environment Map, map (a, B) represents an a-th row and B-th column grid in the global environment Map, A represents the row number of the grid in the global environment Map, and B represents the column number of the grid in the global environment Map;

inf _ab represents Map information of an a-th row b-th column grid Map (a, b) in the global environment Map, (x _a,y_b) represents grid coordinates of the a-th row b-th column grid Map (a, b) in the global environment Map, [ beta ] _ab represents a grid marking result of the a-th row b-th column grid Map (a, b) in the global environment Map, [ beta ] _ab =1 represents that the a-th row b-th column grid Map (a, b) in the global environment Map is passable, and [ beta ] _ab =0 represents that the a-th row b-th column grid Map (a, b) in the global environment Map is not passable;

And constructing a global transfer robot system control model based on the global environment map of the factory area.

Optionally, the building the global handling robot system control model based on the global environment map of the factory area includes:

A global transfer robot system control model is built based on a global environment map of a factory area, the global transfer robot system control model takes the running speed and the running direction of a transfer robot as control parameters, takes the braking parameters of the transfer robot as constraint conditions, and takes the global yaw degree in the process of minimizing transfer as a target;

The construction flow of the global transfer robot system control model is as follows:

s21: acquiring grid coordinates of a transfer robot at a current time t and target transfer grid coordinates, wherein the grid coordinates of a d-th transfer robot at the current time t are as follows Target handling grid coordinates areD represents the total number of the transfer robots;

S22: initializing and generating the running speed and the running direction of the transfer robot at the current time t, wherein the running speed and the running direction of the d-th transfer robot at the current time t are as follows WhereinFor the linear speed of the d-th transfer robot at the current time t,For the angular velocity of the d-th transfer robot at the current time t,The movement direction angle of the d-th transfer robot at the current time t is shown;

s23: constructing constraint conditions of the braking parameters of the transfer robot, wherein the constraint conditions of the braking parameters of the d transfer robot are as follows:

Wherein:

v _min (1) is a preset minimum linear velocity, and v _max (1) is a preset maximum linear velocity;

v _min (2) is a preset minimum angular velocity, and v _max (2) is a preset maximum angular velocity;

s24: the method comprises the steps of taking the global yaw degree of the transfer robot in the transfer process as a target, and constructing an objective function of a control model of the global transfer robot system:

θ(t)＝(θ₁(t),θ₂(t),...,θ_d(t),...,θ_D(t))

Wherein:

F (θ (t)) represents an objective function of a control model of the global transfer robot system, θ (t) represents a control parameter vector of the D transfer robots at time t, and θ _d (t) represents a control parameter of the D-th transfer robot at time t; in the embodiment of the invention, the movement azimuth angle of the transfer robot is the calculation result of the angular velocity;

exp (·) represents an exponential function that bases on the natural constant;

Representing grid coordinates A Euclidean distance between them;

representing grid coordinates reached at time t+1 after the d-th transfer robot operates according to the control parameter θ _d (t);

delta represents the time interval between adjacent moments of coordinated control of the transfer robot; in an embodiment of the present invention, in the present invention,

The time interval between the current time t and t+1 is delta;

sum _d(θ_d (t)) is expressed at time t+1 The number of transfer robots in a circular area with a preset distance dis as a radius is taken as the center;

Epsilon represents a preset adjustment coefficient; in the embodiment of the invention, epsilon is set to 0.1;

and solving an objective function F (theta (t)) to obtain collaborative conveying real-time control parameters for reducing collision probability among the conveying robots and enabling the conveying robots to reach the target conveying grid coordinates as soon as possible, wherein a solving result of the objective function is a solving result of a global conveying robot system control model.

Optionally, in the step S3, the real-time solution is performed on the constructed global handling robot system control model to obtain real-time control parameters of different handling robots, including:

carrying out real-time solving on the constructed global transfer robot system control model to obtain real-time control parameters of different transfer robots at the current time t, wherein the real-time solving flow of the global transfer robot system control model is as follows:

S31: initializing to generate U groups of particle vectors, wherein each group of particle vectors is a control parameter vector of D transfer robots, and the initialized and generated U group of particle vectors is

S32: setting the current iteration number of the particle vector as Z, and setting the maximum iteration number as Z, wherein the Z-th iteration result of the particle vector of the ith group is thatThe initial value of z is 0, whereA vector value representing a particle vector in a d-th section, corresponding to a control parameter of the d-th transfer robot;

S33: unconstrained processing is carried out on an objective function of a control model of the global transfer robot system, and an evaluation function for evaluating the particle vector is formed:

Wherein:

value (L) represents an evaluation function for the particle vector L, L (d) represents a vector value of the particle vector in the d-th section,

Control parameters corresponding to the d-th transfer robot;

σ _p denotes the penalty factor for the p-th constraint, and δ _p (L (d)) denotes the excess of the control parameter beyond the p-th constraint in the vector value L (d); wherein the 1 st constraint condition is a linear velocity constraint, and the 2 nd constraint condition is an angular velocity constraint;

S34: substituting the iterative result of the particle vector into the evaluation function to obtain an evaluation function value of the particle vector, wherein the particle vector The evaluation function value of (2) is

S35: and carrying out iterative mutation treatment on the particle vectors by adopting a genetic algorithm until the maximum iterative times are reached, selecting the particle vector with the lowest evaluation function at the moment as a real-time control parameter vector of the D transfer robots at the current time t, and decomposing the selected particle vector to obtain the real-time control parameter of each transfer robot at the current time t.

Optionally, in the step S4, the sending the solved real-time control parameters of different transfer robots to the corresponding transfer robots includes:

The different transfer robots obtained through solving are sent to the corresponding transfer robots, and the transfer robots adjust the self-running linear speed and angular speed according to the real-time control parameters at the current time t and run; and when the carrying robot reaches the target carrying grid coordinates, carrying out cargo carrying, and updating the target carrying grid coordinates until carrying all cargoes.

In order to solve the above-described problems, the present invention provides an electronic apparatus including:

A memory storing at least one instruction;

The communication interface is used for realizing the communication of the electronic equipment; and

And the processor executes the instructions stored in the memory to realize the multi-intelligent transfer robot cooperative control method.

In order to solve the above-mentioned problems, the present invention also provides a computer readable storage medium having at least one instruction stored therein, the at least one instruction being executed by a processor in an electronic device to implement the above-mentioned multi-intelligent transfer robot cooperative control method.

Compared with the prior art, the invention provides a multi-intelligent transfer robot cooperative control method, which has the following advantages:

Firstly, the scheme provides a factory area collaborative exploration method, which divides a factory area into K subareas, and arranges a transfer robot in each subarea, wherein the transfer robot obtains an area point set of each subarea by using a sensor, and the area point set obtaining flow of the kth subarea is as follows: the carrying robot adds the initial position to the region point set V _k of the kth sub-region; extracting the most recently added position from the region point set V _k Randomly generating a moving direction of the transfer robot, and moving the transfer robot according to the generated moving direction and a moving step alpha; if the sensor of the transfer robot does not sense a front obstacle during the movement, the movement positionAdding to the regional point set V _k; if a sensor of the transfer robot senses a front obstacle during movement, the position at which the front obstacle is sensed is taken as a movement positionWill move positionTo the set of region points V _k. Further exploring the regional point set of each subregion by adopting a mode of cooperative control exploration of the transfer robot to obtain a regional point set after cooperative control exploration, wherein the further exploration flow of the regional point set V _k is as follows: calculating to obtain a drift value of any position in the regional point set V _k, wherein the drift value of the position V _k (i) is:

Wherein: v _k (i) represents the drift value of position V _k (i); omega _k (i) represents a position set composed of positions existing in a region point set V _k in a region with a radius R with a position V _k (i) as a center, V epsilon omega _k (i), and V represents any position in the position set omega _k (i); sum _k (i) represents the total number of positions in the set of positions Ω _k (i); and carrying out drift processing on any position in the regional point set V _k based on the drift value, wherein a drift processing formula of the position V _k (i) is as follows:

V_k(i)←V_k(i)+v_k(i)

Forming a set of positions after the drifting processing, performing iterative operation of the drifting processing until the positions in the set are unchanged, taking the positions in the set at the moment as key positions of the kth sub-area, and forming J _k key positions in the set into a key position set of the kth sub-area:

Wherein: A j-th key position representing a k-th sub-region; obtaining the current positions of all the transfer robots, and calculating to obtain the arrival key position set/>, of the current position of each robot The method comprises the steps of commanding a robot with the current position of the robot with the highest profit function to search the critical position to obtain the position of the critical position and a position marking result, wherein the position marking result is whether the critical position can pass or not, and the current position loc of the robot corresponds to the critical positionThe profit function value of (2) is:

Wherein: representing the current position loc of the robot versus the key position/> Is a function value of the income; Expressed as position/> The number of positions present in the set of region points V _k in the region of radius R; representing the current position loc and the key position/>, of the robot A Euclidean distance between them; λ represents the exploration cost weight. According to the method, the factory area is divided into subareas, an automatic exploration method is set for the transfer robots, when the transfer robots move according to the generated moving direction and moving step length, if obstacles are perceived, random generation movement of the section of route is continued, the generation of the section of route is avoided from being abandoned by a traditional exploration algorithm, an area point set is obtained, the current positions of different transfer robots are combined, the area point set of each subarea is further explored in a mode of cooperative control exploration of the transfer robots, exploration efficiency is improved, the area point set after cooperative control exploration is obtained, and a local environment map of the area where the different transfer robots are located is sent to a background server to be integrated, so that global environment map representation of the factory area is obtained.

Meanwhile, the scheme provides a global transfer robot system control method, a global transfer robot system control model is built based on a global environment map of a factory area, the global transfer robot system control model takes the running speed and the running direction of a transfer robot as control parameters, takes the braking parameters of the transfer robot as constraint conditions, and takes the global yaw degree in the minimum transfer process as a target; the construction flow of the global transfer robot system control model is as follows: acquiring grid coordinates of a transfer robot at a current time t and target transfer grid coordinates, wherein the grid coordinates of a d-th transfer robot at the current time t are as followsTarget handling grid coordinates areD represents the total number of the transfer robots; initializing and generating the running speed and the running direction of the transfer robot at the current time t, wherein the running speed and the running direction of the d-th transfer robot at the current time t areWhereinFor the linear speed of the d-th transfer robot at the current time t,For the angular velocity of the d-th transfer robot at the current time t,The movement direction angle of the d-th transfer robot at the current time t is shown; constructing constraint conditions of the braking parameters of the transfer robot, wherein the constraint conditions of the braking parameters of the d transfer robot are as follows:

Wherein: v _min (1) is a preset minimum linear velocity, and v _max (1) is a preset maximum linear velocity; v _min (2) is a preset minimum angular velocity, and v _max (2) is a preset maximum angular velocity; the method comprises the steps of taking the global yaw degree of the transfer robot in the transfer process as a target, and constructing an objective function of a control model of the global transfer robot system:

θ(t)＝(θ₁(t),θ₂(t),...,θ_d(t),...,θ_D(t))

Wherein: f (θ (t)) represents an objective function of a control model of the global transfer robot system, θ (t) represents a control parameter vector of the D transfer robots at time t, and θ _d (t) represents a control parameter of the D-th transfer robot at time t; exp (·) represents an exponential function that bases on the natural constant; representing grid coordinates/> A Euclidean distance between them; /(I)Representing grid coordinates reached at time t+1 after the d-th transfer robot operates according to the control parameter θ _d (t); delta represents the time interval between adjacent moments of coordinated control of the transfer robot; sum _d(θ_d (t)) represents at time t+1 as The number of transfer robots in a circular area with a preset distance dis as a radius is taken as the center; epsilon represents a preset adjustment coefficient; and solving an objective function F (theta (t)) to obtain collaborative conveying real-time control parameters for reducing collision probability among the conveying robots and enabling the conveying robots to reach the target conveying grid coordinates as soon as possible, wherein a solving result of the objective function is a solving result of a global conveying robot system control model. According to the scheme, the running speed and the running direction of the transfer robots are used as control parameters, the braking parameters of the transfer robots are used as constraint conditions, the global transfer robot system control model is built by taking the global yaw degree in the minimum transfer process as a target, the constraint conditions are combined to perform unconstrained processing on the objective function, the objective function is solved, the collision probability among the transfer robots is reduced, the cooperative transfer real-time control parameters enabling the transfer robots to reach the target transfer grid coordinates as soon as possible are obtained, and the cooperative transfer under a multi-transfer robot scene is realized.

Drawings

Fig. 1 is a schematic flow chart of a cooperative control method of multiple intelligent transfer robots according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of an electronic device for implementing a cooperative control method of multiple intelligent transfer robots according to an embodiment of the present invention.

In the figure: 1 an electronic device, 10 a processor, 11 a memory, 12 a program, 13 a communication interface.

The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

The embodiment of the application provides a cooperative control method for a plurality of intelligent transfer robots. The execution main body of the multi-intelligent transfer robot cooperative control method includes, but is not limited to, at least one of a server, a terminal and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the multi-intelligent transfer robot cooperative control method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.

Example 1:

S1: the carrying robot acquires the regional point set through the sensor and constructs a local environment map according to the regional point set.

In the step S1, the transfer robot obtains a regional point set through its own sensor, including:

dividing a factory area into K sub-areas, arranging a carrying robot in each sub-area, and acquiring an area point set of each sub-area by the carrying robot by using a sensor of the carrying robot, wherein the area point set acquisition flow of the kth sub-area is as follows:

S11: the carrying robot adds the initial position to the region point set V _k of the kth sub-region;

S12: extracting the most recently added position from the region point set V _k Randomly generating a moving direction of the transfer robot, and moving the transfer robot according to the generated moving direction and a moving step alpha;

S13: if the sensor of the transfer robot does not sense the front obstacle during the movement, the position is moved Adding the set of region points V _k, and returning to the step S12;

if the sensor of the transfer robot senses a front obstacle during the movement, the position where the front obstacle is sensed is taken as the movement position Will move positionAdding the set of region points V _k, and returning to the step S12;

S14: repeating the steps S12-S13 to obtain a regional point set V _k containing a plurality of positions:

V_k＝{V_k(i)|i∈[1,num_k]}

Wherein:

V _k (i) represents the i-th position in the region point set V _k, num _k represents the total number of positions in the region point set V _k;

s15: further exploring the regional point set of each subarea in a cooperative control exploration mode of the carrying robot to obtain a regional point set after cooperative control exploration, wherein the regional point set after cooperative control exploration of the kth subarea is V' _k;

constructing a local environment map according to the regional point set, wherein the local environment map construction flow of the kth sub-region is as follows:

initializing to generate a local environment map, connecting adjacent positions in the local environment map according to the occurrence sequence of the positions in the regional point set V' _k, wherein the connection result is a passable position sequence in the local environment map, and marking the positions in the passable position sequence as passable;

Marking the searched position and the position marking result in a local environment map;

The position in the local environment map which is not marked currently is marked as non-passable.

The method for searching the regional point set of each sub-region by adopting the cooperative control searching mode of the transfer robot to obtain the regional point set after the cooperative control searching comprises the following steps:

further exploring the regional point set of each subregion by adopting a mode of cooperative control exploration of the transfer robot to obtain a regional point set after cooperative control exploration, wherein the further exploration flow of the regional point set V _k is as follows:

calculating to obtain a drift value of any position in the regional point set V _k, wherein the drift value of the position V _k (i) is:

Wherein:

V _k (i) represents the drift value of position V _k (i);

Omega _k (i) represents a position set composed of positions existing in a region point set V _k in a region with a radius R with a position V _k (i) as a center, V epsilon omega _k (i), and V represents any position in the position set omega _k (i);

Sum _k (i) represents the total number of positions in the set of positions Ω _k (i);

And carrying out drift processing on any position in the regional point set V _k based on the drift value, wherein a drift processing formula of the position V _k (i) is as follows:

V_k(i)←V_k(i)+v_k(i)

Forming a set of positions after the drifting processing, performing iterative operation of the drifting processing until the positions in the set are unchanged, taking the positions in the set at the moment as key positions of the kth sub-area, and forming J _k key positions in the set into a key position set of the kth sub-area:

Wherein:

A j-th key position representing a k-th sub-region;

obtaining the current positions of all the transfer robots, and calculating to obtain a set of key positions reached by the current position of each robot The method comprises the steps of commanding a robot with the current position of the robot with the highest profit function to search the critical position to obtain the position of the critical position and a position marking result, wherein the position marking result is whether the critical position can pass or not, and the current position loc of the robot corresponds to the critical positionThe profit function value of (2) is:

Wherein:

representing the current position loc of the robot versus the key position/> Is a function value of the income;

Expressed as position/> The number of positions present in the set of region points V _k in the region of radius R;

representing the current position loc and the key position/>, of the robot A Euclidean distance between them;

λ represents the exploration cost weight.

S2: and sending the local environment maps of the areas where the different transfer robots are located to a background server for integration, and constructing a global transfer robot system control model, wherein the global transfer robot system control model takes the running speed and the running direction of the transfer robots as control parameters, takes the braking parameters of the transfer robots as constraint conditions, and takes the global yaw degree in the minimum transfer process as a target.

In the step S2, local environment maps of the areas where the different transfer robots are located are sent to a background server for integration, including:

the method comprises the steps of sending local environment maps of areas where different transfer robots are located to a background server for integration to obtain a global environment map of a factory area, dividing the global environment map into grids, wherein the area of each grid after division is equal to the occupied area of the transfer robot, marking the grids after division, marking the grids as passable if passable positions exist in the grids, otherwise marking the grids as not passable, and the representation form of the global environment map is as follows:

Map＝(Map(a,b))_A×B

inf＝{inf_ab＝((x_a,y_b),β_ab)|a∈[1,A],b∈[1,B]}

Wherein:

map is a matrix representation form of the global environment Map, map (a, B) represents an a-th row and B-th column grid in the global environment Map, A represents the row number of the grid in the global environment Map, and B represents the column number of the grid in the global environment Map;

inf _ab represents Map information of an a-th row b-th column grid Map (a, b) in the global environment Map, (x _a,y_b) represents grid coordinates of the a-th row b-th column grid Map (a, b) in the global environment Map, [ beta ] _ab represents a grid marking result of the a-th row b-th column grid Map (a, b) in the global environment Map, [ beta ] _ab =1 represents that the a-th row b-th column grid Map (a, b) in the global environment Map is passable, and [ beta ] _ab =0 represents that the a-th row b-th column grid Map (a, b) in the global environment Map is not passable;

And constructing a global transfer robot system control model based on the global environment map of the factory area.

The global transfer robot system control model is constructed based on the global environment map of the factory area, and comprises the following steps:

A global transfer robot system control model is built based on a global environment map of a factory area, the global transfer robot system control model takes the running speed and the running direction of a transfer robot as control parameters, takes the braking parameters of the transfer robot as constraint conditions, and takes the global yaw degree in the process of minimizing transfer as a target;

The construction flow of the global transfer robot system control model is as follows:

s21: acquiring grid coordinates of a transfer robot at a current time t and target transfer grid coordinates, wherein the grid coordinates of a d-th transfer robot at the current time t are as follows Target handling grid coordinates areD represents the total number of the transfer robots; /(I)

S22: initializing and generating the running speed and the running direction of the transfer robot at the current time t, wherein the running speed and the running direction of the d-th transfer robot at the current time t are as followsWhereinFor the linear speed of the d-th transfer robot at the current time t,For the angular velocity of the d-th transfer robot at the current time t,The movement direction angle of the d-th transfer robot at the current time t is shown;

s23: constructing constraint conditions of the braking parameters of the transfer robot, wherein the constraint conditions of the braking parameters of the d transfer robot are as follows:

Wherein:

v _min (1) is a preset minimum linear velocity, and v _max (1) is a preset maximum linear velocity;

v _min (2) is a preset minimum angular velocity, and v _max (2) is a preset maximum angular velocity;

s24: the method comprises the steps of taking the global yaw degree of the transfer robot in the transfer process as a target, and constructing an objective function of a control model of the global transfer robot system:

θ(t)＝(θ₁(t),θ₂(t),...,θ_d(t),...,θ_D(t))

Wherein:

F (θ (t)) represents an objective function of a control model of the global transfer robot system, θ (t) represents a control parameter vector of the D transfer robots at time t, and θ _d (t) represents a control parameter of the D-th transfer robot at time t; in the embodiment of the invention, the movement azimuth angle of the transfer robot is the calculation result of the angular velocity;

exp (·) represents an exponential function that bases on the natural constant;

Representing grid coordinates A Euclidean distance between them;

representing grid coordinates reached at time t+1 after the d-th transfer robot operates according to the control parameter θ _d (t);

Delta represents the time interval between adjacent moments of coordinated control of the transfer robot; in the embodiment of the invention, the time interval between the current time t and t+1 is delta;

sum _d(θ_d (t)) is expressed at time t+1 The number of transfer robots in a circular area with a preset distance dis as a radius is taken as the center;

Epsilon represents a preset adjustment coefficient; in the embodiment of the invention, epsilon is set to 0.1;

and solving an objective function F (theta (t)) to obtain collaborative conveying real-time control parameters for reducing collision probability among the conveying robots and enabling the conveying robots to reach the target conveying grid coordinates as soon as possible, wherein a solving result of the objective function is a solving result of a global conveying robot system control model.

S3: and solving the constructed global transfer robot system control model in real time to obtain real-time control parameters of different transfer robots.

And step S3, carrying out real-time solving on the constructed overall transfer robot system control model to obtain real-time control parameters of different transfer robots, wherein the method comprises the following steps:

carrying out real-time solving on the constructed global transfer robot system control model to obtain real-time control parameters of different transfer robots at the current time t, wherein the real-time solving flow of the global transfer robot system control model is as follows:

S31: initializing to generate U groups of particle vectors, wherein each group of particle vectors is a control parameter vector of D transfer robots, and the initialized and generated U group of particle vectors is

S32: setting the current iteration number of the particle vector as Z, and setting the maximum iteration number as Z, wherein the Z-th iteration result of the particle vector of the ith group is thatThe initial value of z is 0, whereA vector value representing a particle vector in a d-th section, corresponding to a control parameter of the d-th transfer robot;

S33: unconstrained processing is carried out on an objective function of a control model of the global transfer robot system, and an evaluation function for evaluating the particle vector is formed:

Wherein:

value (L) represents an evaluation function for the particle vector L, L (d) represents a vector value of the particle vector in the d-th section,

Control parameters corresponding to the d-th transfer robot;

σ _p denotes the penalty factor for the p-th constraint, and δ _p (L (d)) denotes the excess of the control parameter beyond the p-th constraint in the vector value L (d); wherein the 1 st constraint condition is a linear velocity constraint, and the 2 nd constraint condition is an angular velocity constraint;

S34: substituting the iterative result of the particle vector into the evaluation function to obtain an evaluation function value of the particle vector, wherein the particle vector The evaluation function value of (2) is

S35: and carrying out iterative mutation treatment on the particle vectors by adopting a genetic algorithm until the maximum iterative times are reached, selecting the particle vector with the lowest evaluation function at the moment as a real-time control parameter vector of the D transfer robots at the current time t, and decomposing the selected particle vector to obtain the real-time control parameter of each transfer robot at the current time t.

S4: and sending the solved real-time control parameters of different transfer robots to the corresponding transfer robots, and carrying out cooperative control transfer by the transfer robots according to the real-time control parameters.

And in the step S4, the solved different real-time control parameters of the transfer robot are sent to the corresponding transfer robot, and the method comprises the following steps:

The different transfer robots obtained through solving are sent to the corresponding transfer robots, and the transfer robots adjust the self-running linear speed and angular speed according to the real-time control parameters at the current time t and run; and when the carrying robot reaches the target carrying grid coordinates, carrying out cargo carrying, and updating the target carrying grid coordinates until carrying all cargoes.

Example 2:

fig. 2 is a schematic structural diagram of an electronic device for implementing a cooperative control method of multiple intelligent transfer robots according to an embodiment of the present invention.

The electronic device 1 may comprise a processor 10, a memory 11, a communication interface 13 and a bus, and may further comprise a computer program, such as program 12, stored in the memory 11 and executable on the processor 10.

The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the program 12, but also for temporarily storing data that has been output or is to be output.

The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective components of the entire electronic device using various interfaces and lines, executes or executes programs or modules (a program 12 for realizing cooperative Control of the multiple intelligent carrier robots, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

The communication interface 13 may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device 1 and other electronic devices and to enable connection communication between internal components of the electronic device.

The bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.

Fig. 2 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 2 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.

For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.

The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.

It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.

The program 12 stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, may implement:

The carrying robot acquires a regional point set through a sensor of the carrying robot, and constructs a local environment map according to the regional point set;

The local environment maps of the areas where the different carrying robots are located are sent to a background server for integration, and a global carrying robot system control model is built;

Solving the constructed overall transfer robot system control model in real time to obtain real-time control parameters of different transfer robots;

And sending the solved real-time control parameters of different transfer robots to the corresponding transfer robots, and carrying out cooperative control transfer by the transfer robots according to the real-time control parameters.

Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 2, which are not repeated herein.

It should be noted that, the foregoing reference numerals of the embodiments of the present invention are merely for describing the embodiments, and do not represent the advantages and disadvantages of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that comprises the element.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.

The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.

Claims (7)

1. A method for cooperatively controlling a plurality of intelligent transfer robots, the method comprising:

s1: the carrying robot acquires a regional point set through a sensor of the carrying robot, and constructs a local environment map according to the regional point set;

s2: the method comprises the steps that local environment maps of areas where different carrying robots are located are sent to a background server to be integrated, a global carrying robot system control model is built, the global carrying robot system control model takes the running speed and the running direction of the carrying robots as control parameters, takes the braking parameters of the carrying robots as constraint conditions, and takes the global yaw degree in the carrying process as a target;

S3: solving the constructed overall transfer robot system control model in real time to obtain real-time control parameters of different transfer robots;

S4: and sending the solved real-time control parameters of different transfer robots to the corresponding transfer robots, and carrying out cooperative control transfer by the transfer robots according to the real-time control parameters.

2. The method for cooperatively controlling a plurality of intelligent transfer robots as set forth in claim 1, wherein the transfer robot in step S1 obtains a set of area points through its own sensor, comprising:

dividing a factory area into K sub-areas, arranging a carrying robot in each sub-area, and acquiring an area point set of each sub-area by the carrying robot by using a sensor of the carrying robot, wherein the area point set acquisition flow of the kth sub-area is as follows:

S11: the carrying robot adds the initial position to the region point set V _k of the kth sub-region;

S12: extracting the most recently added position from the region point set V _k Randomly generating a moving direction of the transfer robot, and moving the transfer robot according to the generated moving direction and a moving step alpha;

S13: if the sensor of the transfer robot does not sense the front obstacle during the movement, the position is moved Adding the set of region points V _k, and returning to the step S12;

if the sensor of the transfer robot senses a front obstacle during the movement, the position where the front obstacle is sensed is taken as the movement position Will move positionAdding the set of region points V _k, and returning to the step S12;

S14: repeating the steps S12-S13 to obtain a regional point set V _k containing a plurality of positions:

V_k＝{V_k(i)|i∈[1,num_k]}

Wherein:

V _k (i) represents the i-th position in the region point set V _k, num _k represents the total number of positions in the region point set V _k;

s15: further exploring the regional point set of each subarea in a cooperative control exploration mode of the carrying robot to obtain a regional point set after cooperative control exploration, wherein the regional point set after cooperative control exploration of the kth subarea is V' _k;

constructing a local environment map according to the regional point set, wherein the local environment map construction flow of the kth sub-region is as follows:

initializing to generate a local environment map, connecting adjacent positions in the local environment map according to the occurrence sequence of the positions in the regional point set V' _k, wherein the connection result is a passable position sequence in the local environment map, and marking the positions in the passable position sequence as passable;

Marking the searched position and the position marking result in a local environment map;

The position in the local environment map which is not marked currently is marked as non-passable.

3. The multi-intelligent transfer robot cooperative control method according to claim 2, wherein the further searching the regional point set of each sub-region by using the transfer robot cooperative control searching method to obtain the regional point set after cooperative control searching comprises:

further exploring the regional point set of each subregion by adopting a mode of cooperative control exploration of the transfer robot to obtain a regional point set after cooperative control exploration, wherein the further exploration flow of the regional point set V _k is as follows:

calculating to obtain a drift value of any position in the regional point set V _k, wherein the drift value of the position V _k (i) is:

Wherein:

V _k (i) represents the drift value of position V _k (i);

Omega _k (i) represents a position set composed of positions existing in a region point set V _k in a region with a radius R with a position V _k (i) as a center, V epsilon omega _k (i), and V represents any position in the position set omega _k (i);

Sum _k (i) represents the total number of positions in the set of positions Ω _k (i);

And carrying out drift processing on any position in the regional point set V _k based on the drift value, wherein a drift processing formula of the position V _k (i) is as follows:

V_k(i)←V_k(i)+v_k(i)

Forming a set of positions after the drifting processing, performing iterative operation of the drifting processing until the positions in the set are unchanged, taking the positions in the set at the moment as key positions of the kth sub-area, and forming J _k key positions in the set into a key position set of the kth sub-area:

Wherein:

A j-th key position representing a k-th sub-region;

obtaining the current positions of all the transfer robots, and calculating to obtain a set of key positions reached by the current position of each robot The method comprises the steps of commanding a robot with the current position of the robot with the highest profit function to search the critical position to obtain the position of the critical position and a position marking result, wherein the position marking result is whether the critical position can pass or not, and the current position loc of the robot corresponds to the critical positionThe profit function value of (2) is:

Wherein:

representing the current position loc of the robot versus the key position/> Is a function value of the income;

Expressed as position/> The number of positions present in the set of region points V _k in the region of radius R;

representing the current position loc and the key position/>, of the robot A Euclidean distance between them;

λ represents the exploration cost weight.

4. The method for cooperatively controlling multiple intelligent transfer robots as set forth in claim 1, wherein in step S2, the local environment map of the area where the different transfer robots are located is sent to a background server for integration, and the method comprises:

the method comprises the steps of sending local environment maps of areas where different transfer robots are located to a background server for integration to obtain a global environment map of a factory area, dividing the global environment map into grids, wherein the area of each grid after division is equal to the occupied area of the transfer robot, marking the grids after division, marking the grids as passable if passable positions exist in the grids, otherwise marking the grids as not passable, and the representation form of the global environment map is as follows:

Map＝(Map(a,b))_A×B

inf＝{inf_ab＝((x_a,y_b),β_ab)|a∈[1,A],b∈[1,B]}

Wherein:

map is a matrix representation form of the global environment Map, map (a, B) represents an a-th row and B-th column grid in the global environment Map, A represents the row number of the grid in the global environment Map, and B represents the column number of the grid in the global environment Map;

inf _ab represents Map information of an a-th row b-th column grid Map (a, b) in the global environment Map, (x _a,y_b) represents grid coordinates of the a-th row b-th column grid Map (a, b) in the global environment Map, [ beta ] _ab represents a grid marking result of the a-th row b-th column grid Map (a, b) in the global environment Map, [ beta ] _ab =1 represents that the a-th row b-th column grid Map (a, b) in the global environment Map is passable, and [ beta ] _ab =0 represents that the a-th row b-th column grid Map (a, b) in the global environment Map is not passable;

And constructing a global transfer robot system control model based on the global environment map of the factory area.

5. The multi-intelligent transfer robot cooperative control method according to claim 4, wherein the building the global transfer robot system control model based on the global environment map of the factory area comprises:

A global transfer robot system control model is built based on a global environment map of a factory area, the global transfer robot system control model takes the running speed and the running direction of a transfer robot as control parameters, takes the braking parameters of the transfer robot as constraint conditions, and takes the global yaw degree in the process of minimizing transfer as a target;

The construction flow of the global transfer robot system control model is as follows:

s21: acquiring grid coordinates of a transfer robot at a current time t and target transfer grid coordinates, wherein the grid coordinates of a d-th transfer robot at the current time t are as follows Target handling grid coordinates areD represents the total number of the transfer robots;

S22: initializing and generating the running speed and the running direction of the transfer robot at the current time t, wherein the running speed and the running direction of the d-th transfer robot at the current time t are as follows WhereinFor the linear speed of the d-th transfer robot at the current time t,For the angular velocity of the d-th transfer robot at the current time t,The movement direction angle of the d-th transfer robot at the current time t is shown;

s23: constructing constraint conditions of the braking parameters of the transfer robot, wherein the constraint conditions of the braking parameters of the d transfer robot are as follows:

Wherein:

v _min (1) is a preset minimum linear velocity, and v _max (1) is a preset maximum linear velocity;

v _min (2) is a preset minimum angular velocity, and v _max (2) is a preset maximum angular velocity;

s24: the method comprises the steps of taking the global yaw degree of the transfer robot in the transfer process as a target, and constructing an objective function of a control model of the global transfer robot system:

θ(t)＝(θ₁(t),θ₂(t),...,θ_d(t),...,θ_D(t))

Wherein:

F (θ (t)) represents an objective function of a control model of the global transfer robot system, θ (t) represents a control parameter vector of the D transfer robots at time t, and θ _d (t) represents a control parameter of the D-th transfer robot at time t;

exp (·) represents an exponential function that bases on the natural constant;

representing grid coordinates/> A Euclidean distance between them;

representing grid coordinates reached at time t+1 after the d-th transfer robot operates according to the control parameter θ _d (t);

Delta represents the time interval between adjacent moments of coordinated control of the transfer robot;

sum _d(θ_d (t)) is expressed at time t+1 The number of transfer robots in a circular area with a preset distance dis as a radius is taken as the center;

Epsilon represents a preset adjustment coefficient;

and solving an objective function F (theta (t)) to obtain collaborative conveying real-time control parameters for reducing collision probability among the conveying robots and enabling the conveying robots to reach the target conveying grid coordinates as soon as possible, wherein a solving result of the objective function is a solving result of a global conveying robot system control model.

6. The multi-intelligent transfer robot cooperative control method according to claim 5, wherein the step S3 of solving the constructed global transfer robot system control model in real time to obtain real-time control parameters of different transfer robots comprises:

carrying out real-time solving on the constructed global transfer robot system control model to obtain real-time control parameters of different transfer robots at the current time t, wherein the real-time solving flow of the global transfer robot system control model is as follows:

S31: initializing to generate U groups of particle vectors, wherein each group of particle vectors is a control parameter vector of D transfer robots, and the initialized and generated U group of particle vectors is

S32: setting the current iteration number of the particle vector as Z, and setting the maximum iteration number as Z, wherein the Z-th iteration result of the particle vector of the ith group is thatThe initial value of z is 0, whereA vector value representing a particle vector in a d-th section, corresponding to a control parameter of the d-th transfer robot;

S33: unconstrained processing is carried out on an objective function of a control model of the global transfer robot system, and an evaluation function for evaluating the particle vector is formed:

Wherein:

value (L) represents an evaluation function for the particle vector L, L (d) represents a vector value of the particle vector in the d-th section, and corresponds to a control parameter of the d-th transfer robot;

σ _p denotes the penalty factor for the p-th constraint, and δ _p (L (d)) denotes the excess of the control parameter beyond the p-th constraint in the vector value L (d); wherein the 1 st constraint condition is a linear velocity constraint, and the 2 nd constraint condition is an angular velocity constraint;

S34: substituting the iterative result of the particle vector into the evaluation function to obtain an evaluation function value of the particle vector, wherein the particle vector The evaluation function value of (2) is

S35: and carrying out iterative mutation treatment on the particle vectors by adopting a genetic algorithm until the maximum iterative times are reached, selecting the particle vector with the lowest evaluation function at the moment as a real-time control parameter vector of the D transfer robots at the current time t, and decomposing the selected particle vector to obtain the real-time control parameter of each transfer robot at the current time t.

7. The multi-intelligent transfer robot cooperative control method of claim 6, wherein in the step S4, the real-time control parameters of the different transfer robots obtained by solving are sent to the corresponding transfer robots, and the method comprises the following steps:

The different transfer robots obtained through solving are sent to the corresponding transfer robots, and the transfer robots adjust the self-running linear speed and angular speed according to the real-time control parameters at the current time t and run; and when the carrying robot reaches the target carrying grid coordinates, carrying out cargo carrying, and updating the target carrying grid coordinates until carrying all cargoes.