Disclosure of Invention
In order to solve the technical problems, the invention aims to provide an automatic AGV trolley pedestrian following method based on artificial intelligence, which adopts the following technical scheme:
acquiring a plurality of frames of first images; acquiring a target position, an AGV trolley position and a personnel position according to the multi-frame first image;
and acquiring the following demand degree of the personnel, the distance relation between a first task path from the AGV trolley to the target position and a second task path from the personnel position to the target position, and the following degree of the AGV trolley.
Obtaining a final fusion path of the AGV according to the following demand degree, the distance relation and the AGV following degree so that the AGV can run along the final fusion path;
and when the person is identified to carry the goods, selecting the AGV trolley reaching the position of the person.
The invention also provides a technical scheme of the automatic AGV trolley following system based on the artificial intelligence, which comprises a memory and a processor, wherein the processor executes a computer program stored in the memory so as to realize the automatic AGV trolley following method based on the artificial intelligence.
The AGV trolley automatic following method and system have the beneficial effects that: the method comprises the steps of firstly acquiring information in a factory, and then obtaining a first fusion path by acquiring the required degree of personnel; obtaining a second fusion path by obtaining the distance relation between the second task path and the first task path; and obtaining a final fusion path by obtaining the following degree of the AGV trolley, and finally selecting the AGV trolley to be followed. The embodiment of the invention solves the problem that the AGV trolley blindly follows the personnel, and improves the working efficiency of the AGV trolley and the experience degree of the personnel.
Further, the person following demand degree acquisition method includes:
determining the motion trail of the person according to the position of the person in the multi-frame first image, and acquiring the motion direction and the motion speed variation degree of the person;
and acquiring the following demand degree of the personnel according to the change degree.
Further, the method for obtaining the final fusion path comprises the following steps:
acquiring a shortest path from the AGV trolley position to the personnel position; taking the following demand degree as the weight between the shortest path and the first task path to fuse, so as to obtain a first fused path;
taking the distance relation as the weight between the shortest path and the first fusion path to fuse, so as to obtain a second fusion path;
according to the following degree of the AGV trolley; and fusing the weight between the first task path and the second fusion path by taking the following degree as the weight to obtain a final fusion path.
Further, the method for obtaining the following degree of the AGV trolley comprises the following steps:
acquiring the following inhibition amplitude of each AGV trolley according to the following demand degree, the distance relation and the number of the AGV trolleys reaching the personnel position;
and acquiring the following degree according to the following inhibition amplitude and the following demand degree.
Detailed Description
In order to further describe the technical means and effects adopted by the present invention to achieve the preset purpose, the following description is given in detail on the specific implementation, structure, characteristics and effects of an automatic following method for an AGV trolley according to the present invention by referring to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following specifically describes a specific scheme of the automatic following method of the AGV trolley based on artificial intelligence.
In actual warehouse, some goods can't directly distribute the AGV dolly to carry, thereby need personnel to carry out the inspection and the transport of goods and realize the adjustment to the goods position, for example do not put good goods or, the unable discernable goods of AGV dolly or other special goods, all need artificial transport, in order to save staff's physical power, the AGV dolly needs follow staff before the people carries the goods, when the staff found the goods that need carry, place the goods on the dolly of following, let the AGV dolly accomplish the transport.
In order to solve the problem of blind following of the AGV trolley in the warehouse, the embodiment utilizes a path fusion means to achieve the automatic intelligent following purpose of the AGV trolley, and the method is as follows:
referring to fig. 1, a flowchart of steps of an automatic following method of an AGV cart based on artificial intelligence according to an embodiment of the present invention is shown, where the method includes the following steps:
step S001, acquiring a plurality of frames of first images; and acquiring a target position, an AGV trolley position and a personnel position according to the multi-frame first image.
Specifically, a plurality of cameras are installed at the top of the warehouse, the fields of view of the cameras are downward, the fields of view of all the cameras cover the whole warehouse, and the target position, the AGV trolley position, the personnel position and the goods position are obtained according to the acquired multi-frame images.
It should be noted that, the AGV dolly in this embodiment refers to the AGV dolly within 30 meters of the personnel position radius and the AGV dolly can share the above-mentioned data, and this embodiment uses the circumscribed convex polygon of all goods positions as the goods placement area.
When it is detected that the person enters the placement area, step S002 is performed.
Step S002, obtaining the following demand degree of the personnel, the distance relation between the first task path of the AGV trolley reaching the target position and the second task path of the personnel position reaching the target position, and the following degree of the AGV trolley.
(1) And acquiring the following demand degree of the personnel.
In particular, in warehouses personnel are considered to be not stopped for transporting goods if they advance in a fixed direction and speed or if the speed of movement is high, i.e. no following personnel is required. When the speed of the personnel is reduced to a stop, the uncertainty of the speed and the movement direction of the acquired personnel is large, the intention that the personnel possibly stop is described, and when the intention of carrying goods exists, the AGV trolley is required to have a trend of traveling towards the personnel, so that the requirements of the personnel are quickly responded when the personnel are detected to carry the goods, and the personnel are assisted to carry the goods.
The intention of the person to carry the goods is embodied through the following demand degree of the person, and the following demand degree of the person is obtained according to the movement direction sequence and the movement speed sequence in the preset time period before the current moment.
In particular, a flat motion direction sequence and a flat motion speed sequence are obtainedThe greater this value, the less the change in speed direction, the less likely the person is to handle the load, and the no longer needs the AGV trolley to follow. When the value is smaller, the speed and the direction of the personnel are changeable, and at the moment, whether the personnel need to carry cargoes or not cannot be confirmed, and then the AGV trolley needs to follow. Specific person following demand degree w 1 The specific calculation method of (2) is as follows:
let the result of the normalization of the sequence of the human motion direction be p1= { p1 1 ,Kp1 i ,Kp1 N Normalized velocity magnitude sequence as p2= { p2 } 1 ,Kp2 i ,Kp2 N N is the length of the sequence.
Wherein,representing the mean value of the velocity sequence, P1 represents the plateau of the sequence P1, and P2 represents the plateau of the sequence P2, wherein:
(2) And obtaining the distance relation between the first task path and the second task path.
Let the second task path be G3, the path length of G1 be L1, and the sum of the path lengths of G3 and G2 be L2, calculate a correction weight:
w 2 for the distance relation between the first task path and the second task path, the smaller the difference between L1 and L2 is and the larger L1 is, the w 2 The larger.
(3) Obtaining the following degree of AGV trolley
Regarding each AGV trolley as a node, acquiring an edge weight value for every two AGV trolleys, wherein the edge weight value is the negative exponential power of the difference between the lengths of the shortest paths G2 of the two AGV trolleys, the larger the edge weight value is, the smaller the time difference between the two AGV trolleys and the personnel is, the degree of each AGV trolley is acquired, and the degree of each AGV trolley is the sum of the edge weight values of all the AGV trolleys adjacent to the AGV trolleys, and the specific steps are that:
the specific calculation method for the following inhibition amplitude of the AGV trolley is as follows, wherein the degree of the AGV trolley is d, the node size of the AGV trolley is q:
wherein the node size q= [ w ] 2 (w 1 -1)+1]D is the degree of the AGV trolley.
The more AGV carts arrive together, the smaller the node size, accounting for b i The larger the width, the larger the width of suppressing the following of the AGV carriage.
The specific calculation method of the AGV trolley following degree comprises the following steps:
w 3 =αexp(-b i )w 1
wherein w is 3 B, the following degree of the AGV trolley i To the following inhibition amplitude, w, of the AGV trolley 1 For the following demand of the personnel, α is a super parameter, let α=0.8.
And step S003, obtaining a final fusion path of the AGV trolley according to the following requirement degree, the distance relation and the AGV trolley following degree so as to enable the AGV trolley to travel along the final fusion path.
(1) A first fusion path is obtained.
Specifically, when personnel are detected to carry cargoes, the AGV trolley arrives at the personnel to carry cargoes. When personnel are not carrying goods, but there is a possibility of carrying goods, which is indicated by the extent to which personnel follow the demand, then the task of the AGV trolley should be a fusion of the following task and the self-carrying task, in particular:
let AGV carriage position be Q1, target positionThe following demand degree w of personnel is used for the purposes that Q2, the personnel position Q3, the first task path from the AGV trolley to the target position Q2 is G1, and the shortest path from the AGV trolley to the personnel position is G2 1 The weight of the shortest path G2 is fused with the first task path G1, and a first fused path G4 is obtained.
(2) A second fusion path is obtained.
When w is 2 When the weight is greater than the preset weight threshold value, the weight is w 2 The weight of the shortest path G2 is fused with the first fused path G4 to obtain a second fused path G5, when w 2 And when the weight value is smaller than or equal to the preset weight threshold value, the first task path G1 is used as a second fusion path G5.
(3) A final fusion path is obtained.
Obtaining K AGV trolleys with maximum following inhibition amplitude of the AGV trolleys, calculating the following degree of each AGV trolley in the K AGV trolleys, and for the K AGV trolleys, using the following degree w 3 And fusing the weight serving as the first task path weight G1 with the second fusion path G5 to obtain a final fusion path G6.
And S004, when the person is identified to carry the goods, selecting an AGV trolley reaching the position of the person.
The AGV trolley needs to travel along the final fusion path G6 in a period between the current moment and the next moment, the AGV trolley with the largest node size q follows the person along the shortest path G2 when the person is detected to have the goods carrying action through the DNN network, and other AGV trolleys give up the following to complete the original task, so that the purpose of assisting the person in carrying the goods can be guaranteed, and the carrying task of other AGV trolleys is not influenced.
The DNN network of the present embodiment is composed of Opense and TCN networks.
Preferably, the specific method for fusing the shortest path G2 with the first task path G1 is as follows:
and acquiring the shortest path g1 in the two paths, and intercepting a path g2 with the same length as the path g1 on a longer path, wherein the lengths of the path g2 and the path g2 are the same. The travel distance x of the AGV in unit time is obtained by the same starting point, and paths of every other length x are collected on g1Collecting a point, finally obtaining a z1= { z1 1 ,K,z1 k ,Kz1 N Sequence z2 = { z2 from g2 can also be obtained by analogy 1 ,K,z2 k ,Kz2 N The specific method for fusing the shortest path G2 with the first task path G1 is as follows:
let the two-dimensional coordinate of the kth point in the sequence be z1 k And z2 k Obtain a point z k ,
Let z k =w 1 z2 k +(1-w 1 z1 k )
Wherein w is 1 The following demand degree of the personnel is provided.
Each point in the sequence is obtained to obtain a position, and finally a sequence z= { z is obtained 1 ,z 2 ,K,z k K, all points on the sequence z are connected, and the constructed path is represented by w 1 As a result of the weight fusion of the shortest path G2 with the first task path G1.
It should be noted that, the path merging method of the second merged path G5 and the path merging method of the final merged path G6 obtained in this embodiment are consistent with the merging method of the shortest path G2 and the first task path G1.
In summary, in this embodiment, the information inside the factory is collected first, and then the first fusion path is obtained by obtaining the required degree of the personnel; obtaining a second fusion path by obtaining the distance relation between the second task path and the first task path; and obtaining a final fusion path by obtaining the following degree of the AGV trolley, and finally selecting the AGV trolley to be followed. The embodiment of the invention solves the problem that the AGV trolley blindly follows the personnel, and improves the working efficiency of the AGV trolley and the experience degree of the personnel.
Substation site selection system embodiment based on artificial intelligence and big data
The automatic AGV trolley following system based on artificial intelligence of the embodiment comprises a memory and a processor, wherein the processor executes a computer program stored in the memory to realize the automatic AGV trolley following method based on artificial intelligence as described in the embodiment of the automatic AGV trolley following method based on artificial intelligence.
Because the automatic following method of the AGV trolley based on the artificial intelligence has been described in the embodiment of the automatic following method of the AGV trolley based on the artificial intelligence, the description thereof will not be repeated here.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.