CN117705124B - Route planning method of logistics robot - Google Patents
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Abstract
The invention discloses a path planning method of a logistics robot, which belongs to the technical field of path planning and comprises the following steps: s1, acquiring a map of a logistics warehouse, and determining initial position coordinates and storage position coordinates of logistics packages in the map; s2, determining an ideal logistics robot according to initial position coordinates of the logistics packages in the map, and generating an initial distribution path for the ideal logistics robot; and S3, optimizing the initial distribution path according to the initial position coordinates and the storage position coordinates of the logistics packages in the map, and generating an optimized distribution path for the ideal logistics robot. The route planning method of the logistics robot reduces turning inflection points of the ideal logistics robot as much as possible, reduces resource waste and improves distribution efficiency.
Description
Technical Field
The invention belongs to the technical field of path planning, and particularly relates to a path planning method of a logistics robot.
Background
At present, the logistics warehouse is a place where electronic commerce and logistics companies store a large amount of packages to be sent and stored, and with the rapid growth of express business in China, more and more packages are sent to a logistics transfer station, and more human capital is needed. However, with the development of artificial intelligence, the intelligent logistics robot gradually enters the field of vision of the masses, logistics work is gradually combined with the intelligent robot, the logistics robot for transportation at present adopts a manually preset route for delivery, real-time optimization cannot be performed according to the actual warehouse condition, and therefore excessive resource consumption can be caused in the intelligent carrying process of the robot, and the efficiency is reduced.
Disclosure of Invention
The invention provides a path planning method of a logistics robot in order to solve the problems.
The technical scheme of the invention is as follows: the path planning method of the logistics robot comprises the following steps:
S1, acquiring a map of a logistics warehouse, and determining initial position coordinates and storage position coordinates of logistics packages in the map;
s2, determining an ideal logistics robot according to initial position coordinates of the logistics packages in the map, and generating an initial distribution path for the ideal logistics robot;
And S3, optimizing the initial distribution path according to the initial position coordinates and the storage position coordinates of the logistics packages in the map, and generating an optimized distribution path for the ideal logistics robot.
Initial position coordinates, namely the starting point of the logistics robot path planning; and storing position coordinates, namely, the end point of the logistics robot path planning.
Further, in S2, an initial delivery path is generated for the ideal logistics robot using a free space method.
In S2, the free logistics robot closest to the initial position coordinate travel distance is used as the ideal logistics robot. The free space method adopts a predefined basic shape to construct a free space, the free space is expressed as a connected graph, and then path planning is carried out by searching the graph, but the disadvantage is that the complexity of an algorithm is increased when a plurality of obstacles are arranged, and the planned path is not necessarily optimal. One of the characteristics of the logistics warehouse is that the barriers are more, so that the method disclosed by the invention corrects and optimizes the initial distribution path generated by adopting the existing algorithm, and generates the optimal distribution path for logistics packages.
Further, S3 comprises the following sub-steps:
s31, acquiring all barriers in an initial distribution path, and determining transport penalty coefficients of all the barriers;
S32, determining a first obstacle set to be optimized of the initial distribution path according to the transportation penalty coefficients of the obstacles;
s33, determining a second obstacle set to be optimized in the initial distribution path;
S34, constructing a path optimization model, inputting the first obstacle set to be optimized and the second obstacle set to be optimized into the path optimization model, and generating an optimized distribution path for the ideal logistics robot.
The beneficial effects of the above-mentioned further scheme are: in the invention, a plurality of obstacles exist in an initial distribution path, an ideal logistics robot turns (namely turns) when passing through the obstacles, and the logistics robot turns slower, so that the transportation time is increased, and the transportation cost is increased. The invention builds a path optimization model, and performs smooth operation on turning points of the ideal logistics robot in the initial distribution path to complete path optimization.
Further, in S31, the calculation formula of the transport penalty coefficient p i of the ith obstacle is: ; wherein α i represents the turning angle of the ideal logistics robot passing the ith obstacle, X i represents the abscissa of the ith obstacle in the map, Y i represents the ordinate of the ith obstacle in the map, X i-1 represents the abscissa of the ith-1 obstacle in the map, Y i-1 represents the ordinate of the ith-1 obstacle in the map, X i+1 represents the abscissa of the ith+1 obstacle in the map, Y i+1 represents the ordinate of the ith+1 obstacle in the map, X 0 represents the abscissa of the initial position of the logistics package in the map, Y 0 represents the ordinate of the initial position of the logistics package in the map, X 1 represents the abscissa of the storage position of the logistics package in the map, Y 1 represents the ordinate of the storage position of the logistics package in the map, and R i represents the transportation cost of the ideal logistics robot passing the ith obstacle,/> Representing an upward rounding.
Further, in S32, the specific method for determining the first obstacle set to be optimized is as follows: taking all barriers with transport penalty coefficients smaller than the transport penalty threshold as a first barrier set to be optimized;
The transport penalty threshold may be set manually or may be a mean of transport penalty coefficients for all obstacles.
In S33, the specific method for determining the second set of obstacles to be optimized is as follows: and taking all the obstacles with the turn times larger than 1 except the first obstacle set to be optimized in the initial distribution path as a second obstacle set to be optimized.
Further, the path optimization model comprises a first convolution layer, a second convolution layer, a first activation layer, a second activation layer and a smoothing layer;
The input end of the first convolution layer is used as a first input end of the path optimization model; the input end of the second convolution layer is used as a second input end of the path optimization model; the output end of the first convolution layer, the first activation layer and the first input end of the smoothing layer are sequentially connected; the output end of the second convolution layer, the second activation layer and the second input end of the smoothing layer are sequentially connected; the first output end of the smoothing layer is used as the first output end of the path optimization model; the second output of the smoothing layer serves as the second output of the path optimization model.
The beneficial effects of the above-mentioned further scheme are: in the invention, a first convolution layer performs feature extraction on a first obstacle set to be optimized, a plurality of feature values are output, and a second convolution layer performs feature extraction on a second obstacle set to be optimized, and a plurality of feature values are output. The first activation layer operates the maximum/small characteristic value output by the first convolution layer, and the second activation layer operates the maximum/small characteristic value output by the second convolution layer. The smoothing layer determines a final smoothing angle based on the outputs of the first and second activation layers. When the ideal logistics robot passes through the obstacle, the smooth angle can be directly adopted, or the smooth angle can be modified based on the turning angle of the ideal logistics robot.
Further, the expression of the output U of the first convolution layer is: u= { U 1,…,um,…,uM },; Where u 1 represents the output of the first convolution kernel in the first convolution layer, u m represents the output of the mth convolution kernel in the first convolution layer, u M represents the output of the mth convolution kernel in the first convolution layer, M represents the number of convolution kernels in the first convolution layer, p m represents the transport penalty coefficient of the mth convolution kernel in the first convolution layer for the obstacle to be treated, p m-1 represents the transport penalty coefficient of the mth-1 convolution kernel in the first convolution layer for the obstacle to be treated, p m+1 represents the transport penalty coefficient of the mth+1th convolution kernel in the first convolution layer for the obstacle to be treated, γ m represents the weight of the mth convolution kernel in the first convolution layer, b m represents the size of the mth convolution kernel in the first convolution layer;
The output V of the second convolution layer is expressed as: v= { v 1,…,vn,…,vN }, ; Where v 1 denotes the output of the first convolution kernel in the second convolution layer, v n denotes the output of the nth convolution kernel in the second convolution layer, u N denotes the output of the nth convolution kernel in the second convolution layer, N denotes the number of convolution kernels of the second convolution layer, c n denotes the number of turns of the nth convolution kernel in the second convolution layer that should process the obstacle, γ n denotes the weight of the nth convolution kernel in the second convolution layer, and b n denotes the size of the nth convolution kernel in the second convolution layer.
Further, the expression of the output J 1 of the first active layer is: ; where e represents an exponent, max (·) represents a maximum operation, β 1 represents a bias of the first active layer, U represents an output of the first convolutional layer, σ (·) represents an active function, and c represents a constant;
The expression of the output J 2 of the second active layer is: ; where β 2 denotes the offset of the second active layer and V denotes the output of the second convolutional layer.
Further, the expression of the output W of the smoothing layer is: ; where J 1 represents the output of the first active layer, J 2 represents the output of the second active layer, and min (. Cndot.) represents the minimum operation.
The beneficial effects of the invention are as follows: according to the route planning method of the logistics robot, an ideal logistics robot is distributed to complete a distribution task according to the initial position of a map, and an initial distribution route is configured for the ideal logistics robot; in consideration of the characteristic of more barriers of the logistics warehouse, the initial distribution path is corrected and optimized, turning inflection points of an ideal logistics robot are reduced as much as possible, resource waste is reduced, and distribution efficiency is improved.
Drawings
FIG. 1 is a flow chart of a path planning method of a logistics robot;
fig. 2 is a schematic diagram of the structure of the path optimization model.
Detailed Description
Embodiments of the present invention are further described below with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides a path planning method for a logistics robot, which includes the following steps:
S1, acquiring a map of a logistics warehouse, and determining initial position coordinates and storage position coordinates of logistics packages in the map;
s2, determining an ideal logistics robot according to initial position coordinates of the logistics packages in the map, and generating an initial distribution path for the ideal logistics robot;
And S3, optimizing the initial distribution path according to the initial position coordinates and the storage position coordinates of the logistics packages in the map, and generating an optimized distribution path for the ideal logistics robot.
Initial position coordinates, namely the starting point of the logistics robot path planning; and storing position coordinates, namely, the end point of the logistics robot path planning.
In the embodiment of the present invention, in S2, an initial distribution path is generated for an ideal logistics robot by using a free space method.
In S2, the free logistics robot closest to the initial position coordinate travel distance is used as the ideal logistics robot. The free space method adopts a predefined basic shape to construct a free space, the free space is expressed as a connected graph, and then path planning is carried out by searching the graph, but the disadvantage is that the complexity of an algorithm is increased when a plurality of obstacles are arranged, and the planned path is not necessarily optimal. One of the characteristics of the logistics warehouse is that the barriers are more, so that the method disclosed by the invention corrects and optimizes the initial distribution path generated by adopting the existing algorithm, and generates the optimal distribution path for logistics packages.
In an embodiment of the present invention, S3 comprises the following sub-steps:
s31, acquiring all barriers in an initial distribution path, and determining transport penalty coefficients of all the barriers;
S32, determining a first obstacle set to be optimized of the initial distribution path according to the transportation penalty coefficients of the obstacles;
s33, determining a second obstacle set to be optimized in the initial distribution path;
S34, constructing a path optimization model, inputting the first obstacle set to be optimized and the second obstacle set to be optimized into the path optimization model, and generating an optimized distribution path for the ideal logistics robot.
In the invention, a plurality of obstacles exist in an initial distribution path, an ideal logistics robot turns (namely turns) when passing through the obstacles, and the logistics robot turns slower, so that the transportation time is increased, and the transportation cost is increased. The invention builds a path optimization model, and performs smooth operation on turning points of the ideal logistics robot in the initial distribution path to complete path optimization.
In the embodiment of the present invention, in S31, the calculation formula of the transport penalty coefficient p i of the ith obstacle is:
; wherein α i represents the turning angle of the ideal logistics robot passing the ith obstacle, X i represents the abscissa of the ith obstacle in the map, Y i represents the ordinate of the ith obstacle in the map, X i-1 represents the abscissa of the ith-1 obstacle in the map, Y i-1 represents the ordinate of the ith-1 obstacle in the map, X i+1 represents the abscissa of the ith+1 obstacle in the map, Y i+1 represents the ordinate of the ith+1 obstacle in the map, X 0 represents the abscissa of the initial position of the logistics package in the map, Y 0 represents the ordinate of the initial position of the logistics package in the map, X 1 represents the abscissa of the storage position of the logistics package in the map, Y 1 represents the ordinate of the storage position of the logistics package in the map, and R i represents the transportation cost of the ideal logistics robot passing the ith obstacle,/> Representing an upward rounding.
In the embodiment of the present invention, in S32, a specific method for determining the first to-be-optimized obstacle set is as follows: taking all barriers with transport penalty coefficients smaller than the transport penalty threshold as a first barrier set to be optimized;
The transport penalty threshold may be set manually or may be a mean of transport penalty coefficients for all obstacles.
In S33, the specific method for determining the second set of obstacles to be optimized is as follows: and taking all the obstacles with the turn times larger than 1 except the first obstacle set to be optimized in the initial distribution path as a second obstacle set to be optimized.
In the embodiment of the present invention, as shown in fig. 2, the path optimization model includes a first convolution layer, a second convolution layer, a first activation layer, a second activation layer, and a smoothing layer;
The input end of the first convolution layer is used as a first input end of the path optimization model; the input end of the second convolution layer is used as a second input end of the path optimization model; the output end of the first convolution layer, the first activation layer and the first input end of the smoothing layer are sequentially connected; the output end of the second convolution layer, the second activation layer and the second input end of the smoothing layer are sequentially connected; the first output end of the smoothing layer is used as the first output end of the path optimization model; the second output of the smoothing layer serves as the second output of the path optimization model.
In the invention, a first convolution layer performs feature extraction on a first obstacle set to be optimized, a plurality of feature values are output, and a second convolution layer performs feature extraction on a second obstacle set to be optimized, and a plurality of feature values are output. The first activation layer operates the maximum/small characteristic value output by the first convolution layer, and the second activation layer operates the maximum/small characteristic value output by the second convolution layer. The smoothing layer determines a final smoothing angle based on the outputs of the first and second activation layers. When the ideal logistics robot passes through the obstacle, the smooth angle can be directly adopted, or the smooth angle can be modified based on the turning angle of the ideal logistics robot.
In the embodiment of the present invention, the expression of the output U of the first convolution layer is: u= { U 1,…,um,…,uM },; Where u 1 represents the output of the first convolution kernel in the first convolution layer, u m represents the output of the mth convolution kernel in the first convolution layer, u M represents the output of the mth convolution kernel in the first convolution layer, M represents the number of convolution kernels in the first convolution layer, p m represents the transport penalty coefficient of the mth convolution kernel in the first convolution layer for the obstacle to be treated, p m-1 represents the transport penalty coefficient of the mth-1 convolution kernel in the first convolution layer for the obstacle to be treated, p m+1 represents the transport penalty coefficient of the mth+1th convolution kernel in the first convolution layer for the obstacle to be treated, γ m represents the weight of the mth convolution kernel in the first convolution layer, b m represents the size of the mth convolution kernel in the first convolution layer;
The output V of the second convolution layer is expressed as: v= { v 1,…,vn,…,vN }, ; Where v 1 denotes the output of the first convolution kernel in the second convolution layer, v n denotes the output of the nth convolution kernel in the second convolution layer, u N denotes the output of the nth convolution kernel in the second convolution layer, N denotes the number of convolution kernels of the second convolution layer, c n denotes the number of turns of the nth convolution kernel in the second convolution layer that should process the obstacle, γ n denotes the weight of the nth convolution kernel in the second convolution layer, and b n denotes the size of the nth convolution kernel in the second convolution layer.
In the embodiment of the present invention, the expression of the output J 1 of the first active layer is: ; where e represents an exponent, max (·) represents a maximum operation, β 1 represents a bias of the first active layer, U represents an output of the first convolutional layer, σ (·) represents an active function, and c represents a constant;
The expression of the output J 2 of the second active layer is: ; where β 2 denotes the offset of the second active layer and V denotes the output of the second convolutional layer.
In the embodiment of the present invention, the expression of the output W of the smoothing layer is: ; where J 1 represents the output of the first active layer, J 2 represents the output of the second active layer, and min (. Cndot.) represents the minimum operation.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (2)
1. The path planning method of the logistics robot is characterized by comprising the following steps of:
S1, acquiring a map of a logistics warehouse, and determining initial position coordinates and storage position coordinates of logistics packages in the map;
s2, determining an ideal logistics robot according to initial position coordinates of the logistics packages in the map, and generating an initial distribution path for the ideal logistics robot;
s3, optimizing an initial distribution path according to the initial position coordinates and the storage position coordinates of the logistics packages in the map, and generating an optimized distribution path for an ideal logistics robot;
the step S3 comprises the following substeps:
s31, acquiring all barriers in an initial distribution path, and determining transport penalty coefficients of all the barriers;
S32, determining a first obstacle set to be optimized of the initial distribution path according to the transportation penalty coefficients of the obstacles;
s33, determining a second obstacle set to be optimized in the initial distribution path;
s34, constructing a path optimization model, inputting a first obstacle set to be optimized and a second obstacle set to be optimized into the path optimization model, and generating an optimized distribution path for the ideal logistics robot;
In S31, the calculation formula of the transport penalty coefficient p i of the ith obstacle is:
; wherein α i represents the turning angle of the ideal logistics robot passing the ith obstacle, X i represents the abscissa of the ith obstacle in the map, Y i represents the ordinate of the ith obstacle in the map, X i-1 represents the abscissa of the ith-1 obstacle in the map, Y i-1 represents the ordinate of the ith-1 obstacle in the map, X i+1 represents the abscissa of the ith+1 obstacle in the map, Y i+1 represents the ordinate of the ith+1 obstacle in the map, X 0 represents the abscissa of the initial position of the logistics package in the map, Y 0 represents the ordinate of the initial position of the logistics package in the map, X 1 represents the abscissa of the storage position of the logistics package in the map, Y 1 represents the ordinate of the storage position of the logistics package in the map, and R i represents the transportation cost of the ideal logistics robot passing the ith obstacle,/> Representing an upward rounding;
in S32, the specific method for determining the first obstacle set to be optimized is as follows: taking all barriers with transport penalty coefficients smaller than the transport penalty threshold as a first barrier set to be optimized;
In S33, the specific method for determining the second set of obstacles to be optimized includes: taking all the obstacles with the number of turns being more than 1 except the first obstacle set to be optimized in the initial distribution path as a second obstacle set to be optimized;
the path optimization model comprises a first convolution layer, a second convolution layer, a first activation layer, a second activation layer and a smoothing layer;
The input end of the first convolution layer is used as a first input end of a path optimization model; the input end of the second convolution layer is used as a second input end of the path optimization model; the output end of the first convolution layer, the first activation layer and the first input end of the smoothing layer are sequentially connected; the output end of the second convolution layer, the second activation layer and the second input end of the smoothing layer are sequentially connected; the first output end of the smoothing layer is used as the first output end of the path optimization model; the second output end of the smoothing layer is used as the second output end of the path optimization model;
the expression of the output U of the first convolution layer is: u= { U 1,…,um,…,uM }, ; Where u 1 represents the output of the first convolution kernel in the first convolution layer, u m represents the output of the mth convolution kernel in the first convolution layer, u M represents the output of the mth convolution kernel in the first convolution layer, M represents the number of convolution kernels in the first convolution layer, p m represents the transport penalty coefficient of the mth convolution kernel in the first convolution layer for the obstacle to be treated, p m-1 represents the transport penalty coefficient of the mth-1 convolution kernel in the first convolution layer for the obstacle to be treated, p m+1 represents the transport penalty coefficient of the mth+1th convolution kernel in the first convolution layer for the obstacle to be treated, γ m represents the weight of the mth convolution kernel in the first convolution layer, b m represents the size of the mth convolution kernel in the first convolution layer;
the expression of the output V of the second convolution layer is: v= { v 1,…,vn,…,vN }, ; Wherein v 1 represents the output of the first convolution kernel in the second convolution layer, v n represents the output of the nth convolution kernel in the second convolution layer, v N represents the output of the nth convolution kernel in the second convolution layer, N represents the number of convolution kernels of the second convolution layer, c n represents the turn number of the nth convolution kernel in the second convolution layer to deal with the obstacle, w n represents the weight of the nth convolution kernel in the second convolution layer, and b n represents the size of the nth convolution kernel in the second convolution layer;
the expression of the output J 1 of the first active layer is: ; where e represents an exponent, max (·) represents a maximum operation, β 1 represents a bias of the first active layer, U represents an output of the first convolutional layer, σ (·) represents an active function, and c represents a constant;
the expression of the output J 2 of the second active layer is: ; where β 2 denotes the bias of the second active layer and V denotes the output of the second convolutional layer;
the expression of the output W of the smoothing layer is: ; where J 1 represents the output of the first active layer, J 2 represents the output of the second active layer, and min (. Cndot.) represents the minimum operation.
2. The route planning method of a logistics robot of claim 1, wherein in S2, an initial distribution route is generated for the ideal logistics robot using a free space method.
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