Cold chain warehouse robot path planning method based on AI processing

Abstract

The invention discloses a cold chain warehouse robot path planning method based on AI processing, and belongs to the technical field of path planning, and the method comprises the following steps: S1, rasterizing a cold chain warehouse map, and determining the obstacle density of each grid at a historical timestamp; s2, according to the obstacle density of each grid at the historical timestamp, determining the predicted obstacle density of each grid by using a random forest; and S3, generating a carrying path of the robot for the to-be-carried goods according to the predicted obstacle density of each grid and the obstacle condition at the current moment. The carrying path is automatically generated based on the dynamic driving parameters, the automation of path planning is realized, the energy consumption cost from the current grid to the adjacent grid is considered in the path planning, and the path with lower energy consumption can be selected.

Description

Technical Field

[0001] The present invention belongs to the technical field of path planning, and in particular relates to a cold chain warehouse robot path planning method based on AI processing. Background Art

[0002] In the field of modern cold chain logistics, warehouse operation efficiency and goods freshness are key factors in corporate competitiveness. With the rapid development of artificial intelligence (AI) technology, its application in cold chain warehouse management has become an important means to improve efficiency and reduce losses. In cold chain warehouses, due to the strict requirements for temperature control of stored items, dynamic changes in the warehouse environment (such as door opening and closing, personnel movement, and equipment failures), and the frequent entry and exit of goods, the distribution of obstacles inside the warehouse is constantly changing, which poses a severe challenge to the path planning of warehouse robots.

[0003] Traditional path planning methods are often based on static maps or simple dynamic obstacle detection, which makes it difficult to accurately predict and adapt to the complex and changing obstacle conditions in cold chain warehouses, resulting in inefficient path planning and even possible damage to goods or robot failure due to collisions. Therefore, it is particularly important to develop an intelligent path planning method that can comprehensively consider historical obstacle data, real-time obstacle detection, and predict future obstacle distribution. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes a cold chain warehouse robot path planning method based on AI processing.

[0005] The technical solution of the present invention is: a cold chain warehouse robot path planning method based on AI processing comprises the following steps:

[0006] S1. Rasterize the cold chain warehouse map and determine the obstacle density of each grid at the historical timestamp;

[0007] S2, based on the obstacle density of each grid at the historical timestamp, using random forest to determine the predicted obstacle density of each grid;

[0008] S3. Generate a robot transport path for the goods to be transported based on the predicted obstacle density of each grid and the obstacle situation at the current moment.

[0009] Furthermore, in S1, the obstacle density of the grid from time m to time n on day k is The calculation formula is: ; In the formula, It indicates the duration of time when there is an obstacle in the grid from time m to time n on the kth day. Represents the total duration from time m to time n on the kth day.

[0010] Furthermore, S2 includes the following sub-steps:

[0011] S21, constructing a decision tree for each day of the historical timestamp according to the obstacle density of each grid at the historical timestamp;

[0012] S22, combining the decision trees of all days of the historical timestamp to generate a random forest;

[0013] S23. Use random forest to determine the predicted obstacle density of each grid in the cold chain warehouse.

[0014] The beneficial effect of the above further scheme is: in the present invention, a decision tree is constructed for each day, which can accurately capture the changing pattern of the grid obstacle density each day, making the model closer to the dynamic changes of the actual warehouse environment. The decision tree model can prevent overfitting and improve the generalization ability of the model through a pruning algorithm. In a cold chain warehouse environment, this helps to reduce model deviations caused by abnormal data or special events. Random forest improves the accuracy and stability of predictions by integrating the results of multiple decision trees, more accurately predicting the obstacle density of future grids, and random forests can process high-dimensional data without the need for feature selection.

[0015] Furthermore, S21 includes the following sub-steps:

[0016] S211, taking the obstacle density of all grids on the kth day in the historical timestamp as the root node;

[0017] S212, calculating a number of division features according to the obstacle density of each grid on the kth day;

[0018] S213, dividing the obstacle density of all grids on the kth day into a number of sub-nodes according to a number of division features;

[0019] S214, using the child nodes as leaf nodes of the decision tree, and using the root node to generate a decision tree for the kth day.

[0020] The beneficial effect of the above further scheme is: in the present invention, by calculating multiple partitioning features, the distribution characteristics of grid obstacle density can be more comprehensively described, the expression ability and accuracy of the decision tree can be improved, and the obtained multiple child nodes can more accurately describe the difference in obstacle density in different areas. The child nodes are used as leaf nodes to ensure that the decision tree covers the obstacle density of all grids, so that the model can fully reflect the dynamic changes of the warehouse environment. By generating independent decision trees for each day, it can be easily expanded to the entire historical timestamp data set to build a more comprehensive random forest model, further improving the prediction ability and robustness.

[0021] Furthermore, in S212, the corresponding division features from time m to time n on the kth day The calculation formula is:

[0022] ; In the formula, It represents the obstacle density of the rth grid from time m to time n on the kth day, and R represents the number of grids in the cold storage warehouse map.

[0023] The duration of each day can be appropriately divided, for example, it can be divided into 6 time periods, then time m to time n can be expressed as 0:00-3:00 (referring to 3:59), 4:00-7:00, 8:00-11:00, 12:00-15:00, 16:00-19:00 and 20:00-23:00 (referring to 23:59), and the division characteristics of each time period can be obtained.

[0024] Furthermore, in S213, all the partitioning features in the kth day are sorted from large to small, every two adjacent partitioning features are used as the upper and lower limits of the partitioning interval, and all obstacle densities belonging to the same partitioning interval are used as a child node.

[0025] Furthermore, S3 includes the following sub-steps:

[0026] S31, calculating the dynamic driving parameters of each grid using the predicted obstacle density of each grid and whether there is an obstacle at present;

[0027] S32. Generate a transport path for the cold chain warehouse robot according to the dynamic driving parameters of each grid.

[0028] Furthermore, in S31, the calculation formula of the dynamic driving parameter of the grid at the position of the i-th row and the j-th column is:

[0029] ; In the formula, represents the energy cost of traveling from the grid of row i and column j to the grid of row i+1 and column j, represents the energy cost of traveling from the grid at row i and column j to the grid at row i and column j+1, represents the energy cost of traveling from the grid in row i and column j to the grid in row i-1 and column j, represents the energy cost of traveling from the grid in the i-th row and j-th column to the grid in the i-th row and j-1-th column, γ i,j_now Indicates the weight corresponding to whether there is an obstacle in the grid of row i and column j. c represents a constant. P i,j Represents the predicted obstacle density of the i-th row and j-th column raster.

[0030] The beneficial effect of the above further scheme is that: in the present invention, different obstacles have different weights, and the size relationship is: the weight of immovable obstacles> the weight of movable obstacles> the weight of no obstacles, and the weights can be set manually. The path planning method based on dynamic driving parameters can comprehensively consider the driving cost and obstacle conditions of the robot, generate a more efficient handling path, and thus reduce the operating cost.

[0031] Furthermore, in S32, in the row from the starting point of the goods to be transported to the row from the end point of the goods to be transported, the grid with the minimum dynamic driving parameter in each row is determined, and all the determined grids are connected to generate a transport path.

[0032] The beneficial effects of the present invention are as follows: the cold chain warehouse is gridded, and a decision tree is constructed using the obstacle density data of each grid at a historical timestamp to determine the random forest, thereby effectively integrating historical information, improving the accuracy of prediction of future obstacle density, enhancing the effectiveness and reliability of planning, and enabling the robot to avoid potential high-risk areas when planning paths; the transportation path is automatically generated based on dynamic driving parameters, the automation of path planning is realized, and the energy consumption cost from the current grid to the adjacent grid is considered in the path planning, which helps to select a path with lower energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Flowchart of the cold chain warehouse robot path planning method based on AI processing. DETAILED DESCRIPTION

[0034] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0035] like Figure 1 As shown, the present invention provides a cold chain warehouse robot path planning method based on AI processing, comprising the following steps:

[0036] S1. Rasterize the cold chain warehouse map and determine the obstacle density of each grid at the historical timestamp;

[0037] S2, based on the obstacle density of each grid at the historical timestamp, using random forest to determine the predicted obstacle density of each grid;

[0038] S3. Generate a robot transport path for the goods to be transported based on the predicted obstacle density of each grid and the obstacle situation at the current moment.

[0039] In the embodiment of the present invention, in S1, the obstacle density of the grid from time m to time n on the kth day is The calculation formula is: ; In the formula, It indicates the duration of time when there is an obstacle in the grid from time m to time n on the kth day. Represents the total duration from time m to time n on the kth day.

[0040] In this embodiment of the present invention, S2 includes the following sub-steps:

[0041] S21, constructing a decision tree for each day of the historical timestamp according to the obstacle density of each grid at the historical timestamp;

[0042] S22, combining the decision trees of all days of the historical timestamp to generate a random forest;

[0043] S23. Use random forest to determine the predicted obstacle density of each grid in the cold chain warehouse.

[0044] In the present invention, a decision tree is constructed for each day, which can accurately capture the changing pattern of the grid obstacle density each day, making the model closer to the dynamic changes of the actual warehouse environment. The decision tree model can prevent overfitting and improve the generalization ability of the model through a pruning algorithm. In a cold chain warehouse environment, this helps to reduce model deviations caused by abnormal data or special events. Random forest improves the accuracy and stability of predictions by integrating the results of multiple decision trees, more accurately predicting the obstacle density of future grids, and random forests can process high-dimensional data without the need for feature selection.

[0045] In the embodiment of the present invention, S21 includes the following sub-steps:

[0046] S211, taking the obstacle density of all grids on the kth day in the historical timestamp as the root node;

[0047] S212, calculating a number of division features according to the obstacle density of each grid on the kth day;

[0048] S213, dividing the obstacle density of all grids on the kth day into a number of sub-nodes according to a number of division features;

[0049] S214, using the child nodes as leaf nodes of the decision tree, and using the root node to generate a decision tree for the kth day.

[0050] In the present invention, by calculating multiple partitioning features, the distribution characteristics of grid obstacle density can be more comprehensively described, the expression ability and accuracy of the decision tree can be improved, and the obtained multiple child nodes can more accurately describe the difference in obstacle density in different areas. The child nodes are used as leaf nodes to ensure that the decision tree covers the obstacle density of all grids, so that the model can fully reflect the dynamic changes of the warehouse environment. By generating independent decision trees for each day, it can be easily expanded to the entire historical timestamp data set to build a more comprehensive random forest model, further improving the prediction ability and robustness.

[0051] In the embodiment of the present invention, in S212, the k-th day from time m to time n corresponds to the division feature The calculation formula is:

[0052] ; In the formula, It represents the obstacle density of the rth grid from time m to time n on the kth day, and R represents the number of grids in the cold storage warehouse map.

[0053] The duration of each day can be appropriately divided, for example, it can be divided into 6 time periods, then time m to time n can be expressed as 0:00-3:00 (referring to 3:59), 4:00-7:00, 8:00-11:00, 12:00-15:00, 16:00-19:00 and 20:00-23:00 (referring to 23:59), and the division characteristics of each time period can be obtained.

[0054] In the embodiment of the present invention, in S213, all the division features in the kth day are sorted from large to small, every two adjacent division features are used as the upper and lower limits of the division interval, and all obstacle densities belonging to the same division interval are used as a child node.

[0055] In this embodiment of the present invention, S3 includes the following sub-steps:

[0056] S31, calculating the dynamic driving parameters of each grid using the predicted obstacle density of each grid and whether there is an obstacle at present;

[0057] S32. Generate a transport path for the cold chain warehouse robot according to the dynamic driving parameters of each grid.

[0058] In the embodiment of the present invention, in S31, the calculation formula of the dynamic driving parameter of the grid at the position of the i-th row and the j-th column is:

[0059] ; In the formula, represents the energy cost of traveling from the grid of row i and column j to the grid of row i+1 and column j, represents the energy cost of traveling from the grid at row i and column j to the grid at row i and column j+1, represents the energy cost of traveling from the grid in row i and column j to the grid in row i-1 and column j, represents the energy cost of traveling from the grid in the i-th row and j-th column to the grid in the i-th row and j-1-th column, γ i,j_now Indicates the weight corresponding to whether there is an obstacle in the grid of row i and column j. c represents a constant. P i,j Represents the predicted obstacle density of the i-th row and j-th column raster.

[0060] In the present invention, different obstacles have different weights, and the relationship is: the weight of immovable obstacles > the weight of movable obstacles > the weight of no obstacles, and the weights can be set manually. The path planning method based on dynamic driving parameters can comprehensively consider the driving cost and obstacle conditions of the robot, generate a more efficient transport path, and thus reduce the operating cost.

[0061] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A cold chain warehouse robot path planning method based on AI processing, characterized in that: The following steps are involved: S1. Rasterize the cold chain warehouse map and determine the obstacle density of each grid at the historical timestamp; S2, based on the obstacle density of each grid at the historical timestamp, using random forest to determine the predicted obstacle density of each grid; S3. Generate a robot transport path for the goods to be transported based on the predicted obstacle density of each grid and the obstacle situation at the current moment.

2. The cold chain warehouse robot path planning method based on AI processing according to claim 1 is characterized in that: In S1, the obstacle density of the grid from time m to time n on the kth day The calculation formula is: ; In the formula, It indicates the duration of time when there is an obstacle in the grid from time m to time n on the kth day. Represents the total duration from time m to time n on the kth day.

3. The cold chain warehouse robot path planning method based on AI processing according to claim 1 is characterized in that: The S2 comprises the following sub-steps: S21, constructing a decision tree for each day of the historical timestamp according to the obstacle density of each grid at the historical timestamp; S22, combining the decision trees of all days of the historical timestamp to generate a random forest; S23. Use random forest to determine the predicted obstacle density of each grid in the cold chain warehouse.

4. The cold chain warehouse robot path planning method based on AI processing according to claim 3 is characterized in that: The S21 comprises the following sub-steps: S211, taking the obstacle density of all grids on the kth day in the historical timestamp as the root node; S212, calculating a number of division features according to the obstacle density of each grid on the kth day; S213, dividing the obstacle density of all grids on the kth day into a number of sub-nodes according to a number of division features; S214, using the child nodes as leaf nodes of the decision tree, and using the root node to generate a decision tree for the kth day.

5. The cold chain warehouse robot path planning method based on AI processing according to claim 4 is characterized in that: In S212, the corresponding division features from time m to time n on the kth day The calculation formula is: ; In the formula, It represents the obstacle density of the rth grid from time m to time n on the kth day, and R represents the number of grids in the cold storage warehouse map.

6. The cold chain warehouse robot path planning method based on AI processing according to claim 4 is characterized in that: In S213, all the partitioning features in the kth day are sorted from large to small, every two adjacent partitioning features are used as the upper and lower limits of the partitioning interval, and all obstacle densities belonging to the same partitioning interval are used as a child node.

7. The cold chain warehouse robot path planning method based on AI processing according to claim 1 is characterized in that: The S3 comprises the following sub-steps: S31, calculating the dynamic driving parameters of each grid using the predicted obstacle density of each grid and whether there is an obstacle at present; S32. Generate a transport path for the cold chain warehouse robot according to the dynamic driving parameters of each grid.

8. The cold chain warehouse robot path planning method based on AI processing according to claim 7 is characterized in that: In the above S31, the calculation formula of the dynamic driving parameter of the grid at the position of the i-th row and the j-th column is: ; In the formula, represents the energy cost of traveling from the grid of row i and column j to the grid of row i+1 and column j, represents the energy cost of traveling from the grid at row i and column j to the grid at row i and column j+1, represents the energy cost of traveling from the grid in row i and column j to the grid in row i-1 and column j, represents the energy cost of traveling from the grid in the i-th row and j-th column to the grid in the i-th row and j-1-th column, γ i,j_now Indicates the weight corresponding to whether there is an obstacle in the grid of row i and column j. c represents a constant. P i,j Represents the predicted obstacle density of the i-th row and j-th column raster.

9. The cold chain warehouse robot path planning method based on AI processing according to claim 7 is characterized in that: In S32, in the row from the starting point of the goods to be transported to the row from the end point of the goods to be transported, the grid with the minimum dynamic driving parameter in each row is determined, and all the determined grids are connected to generate a transport path.

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