An obstacle avoidance method and system for an automatically guided logistics robot

Abstract

The present invention relates to the technical field of data processing, and in particular to an obstacle avoidance method and system for an automatic guided logistics robot. The method includes: obtaining all feature points of the logistics robot in the current environment at the current moment; obtaining the moving direction of the logistics robot, constructing a plane coordinate system in the moving direction, and calculating the uniformity of all feature points; determining the neighborhood feature points among all feature points, and obtaining the uniformity of the neighborhood feature points; calculating the local uniformity of the environment; calculating the feature distance between any two neighborhood feature points; and clustering the neighborhood feature points by using K-means clustering according to the feature distance to determine the obstacle points, so as to realize the obstacle avoidance of the automatic guided logistics robot. By introducing multi-dimensional features such as uniformity, local uniformity, and color distance, the present invention solves the problem of low accuracy of K-means clustering when the feature points are unevenly distributed, thereby improving the accuracy of obstacle recognition.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to an obstacle avoidance method and system for an automatic guided logistics robot. Background Art

[0002] A logistics robot can perform real-time positioning through a radar installed on itself. Radar obstacle avoidance is an intelligent control technology that uses radar technology to detect and avoid obstacles. Its working principle is that a radar transmitter emits high-frequency electromagnetic waves. When the electromagnetic waves encounter an obstacle, they will be reflected back and received by the radar receiver. The coordinates and color information of the feature points are obtained through the echo energy, and the mean clustering algorithm is used to cluster the feature points obtained by the radar, and then the obstacle avoidance of the logistics robot can be performed through the feature points.

[0003] The patent document with the publication number CN110543975B discloses a method for optimizing the evacuation path of a crowd based on a swarm intelligence algorithm and evacuation entropy. This method is executed by a computer, and the specific steps include: Step S1: Construct an evacuation environment; Step S2: Initialize the parameters of the evacuated crowd and the evacuation path; Step S3: Divide the crowd into multiple small groups based on the k-means clustering algorithm; Step S4: All individuals update their positions based on the hybrid artificial bee colony-bat algorithm; Step S5: All individuals correct their positions based on the evacuation entropy; Step S6: All individuals perform obstacle avoidance; Step S7: If the algorithm does not reach the end condition, return to Step S4, otherwise the algorithm ends and the evacuation is completed.

[0004] However, the above patent document is not directed to the obstacle avoidance problem of logistics robots, and when the mean clustering algorithm is used to cluster the feature points obtained by the radar to identify the feature points of the obstacles that affect the movement of the logistics robot, since the feature points obtained by the radar are often sparse and non-uniformly distributed, this data characteristic will cause interference in the identification of obstacle points, resulting in collisions of the logistics robot during movement. Summary of the Invention

[0005] To solve the problem that when the mean clustering is used to cluster the obtained feature points, due to the non-uniform distribution of the feature points, the accuracy of identifying obstacle points is reduced, resulting in the logistics robot being prone to collisions during movement, the present invention provides an obstacle avoidance method and system for an automatic guided logistics robot.

[0006] In a first aspect, the present invention provides an obstacle avoidance method for an automatic guided logistics robot, adopting the following technical solution:

[0007] An obstacle avoidance method for an automatic guided logistics robot, comprising: obtaining all feature points of the logistics robot in the current environment at the current moment, where the feature information included in the feature points are the horizontal, vertical, and vertical coordinates in the space coordinate system, and , , pixel values; obtaining the moving direction of the logistics robot, constructing a plane coordinate system in the moving direction, obtaining the , coordinates of all feature points in the plane coordinate system, and calculating the variances of the coordinates of all feature points on each same coordinate axis in the space and plane coordinate systems respectively; based on the variances and the sizes of the circumscribed cubes of all feature points on each coordinate axis in the space coordinate system, calculating the uniformity of all feature points; determining the neighborhood feature points among all feature points, and obtaining the uniformity of the neighborhood feature points; based on the uniformity, quantity of all feature points and neighborhood feature points, calculating the local uniformity of the environment; denoting the unit vector of the moving direction as the moving vector, denoting the unit vector of the neighborhood feature points as the feature vector, based on the local uniformity, the cosine similarity of the feature vectors and the moving vector of any two neighborhood feature points, the Euclidean distances from any two neighborhood feature points to the logistics robot, and the , , color distances of any two neighborhood feature points obtained using pixel values, calculating the feature distances of any two neighborhood feature points; according to the feature distances, using k-means clustering to cluster the neighborhood feature points, determining the obstacle points, so as to realize obstacle avoidance of the automatic guided logistics robot.

[0008] The beneficial effects are as follows: By introducing multi-dimensional features such as uniformity, local uniformity, and color distance, the problem that k-means clustering has low accuracy when the feature points are unevenly distributed is solved, thereby improving the accuracy of obstacle recognition; by calculating the uniformity of the feature points and carefully analyzing the environmental changes, it helps to more reliably perform obstacle clustering and obstacle avoidance; by combining the color distance and the Euclidean distance to calculate the feature distance, the ability to identify obstacles is enhanced, avoiding misjudgment, especially in an environment with similar colors; through the combination of multiple features, obstacles can be recognized timely and accurately, and obstacle avoidance can be performed quickly, improving the safety and efficiency of the logistics robot.

[0009] Furthermore, the uniformity of all feature points satisfies the following relational expression:

[0010] ; In the formula, is the uniformity of all feature points of the logistics robot in the current environment at the current moment, are respectively the horizontal, vertical, and vertical coordinate axes in the space coordinate system, is the variance of the coordinates of all feature points on the th coordinate axis in the space coordinate system, is the dimension of the circumscribed cube of all feature points on the th coordinate axis in the space coordinate system, are respectively the , coordinate axes in the plane coordinate system, is the variance of the coordinates of all feature points on the th coordinate axis in the plane coordinate system.

[0011] The beneficial effects are as follows: By comprehensively considering the distribution of feature points in the space coordinate system and the plane coordinate system, the uniformity degree of obstacles in the environment can be accurately evaluated, helping the logistics robot to better analyze the current environment; According to the uniformity degree of the environment, the logistics robot can judge the dense or sparse areas of obstacles, so as to optimize the obstacle avoidance strategy and improve the accuracy and safety of decision-making.

[0012] Furthermore, determining the neighborhood feature points among all feature points includes: Based on the Euclidean distance from all feature points to the center point of the logistics robot, using K-means clustering to cluster all feature points, obtaining two clustering clusters, and marking the feature points in the clustering cluster with the shortest average Euclidean distance to the logistics robot as neighborhood feature points.

[0013] The beneficial effects are as follows: Through the K-means clustering algorithm, the feature points relatively close to the logistics robot can be accurately identified, improving the perception ability of the logistics robot to the surrounding environment; Focusing on the neighborhood feature points helps the robot reduce unnecessary calculations when planning the path, improving the real-time performance and accuracy of obstacle avoidance decision-making; K-means clustering can be dynamically adjusted according to the changes in the environment, helping the logistics robot to cope with the changes in different environments and maintaining a good obstacle avoidance effect.

[0014] Furthermore, the local uniformity satisfies the following relational expression:

[0015] ; In the formula, is the local uniformity of the logistics robot at the current moment in the environment it is in, is the uniformity degree of all feature points in the environment where the logistics robot is at the current moment, is the uniformity degree of the neighborhood feature points in the environment where the logistics robot is at the current moment, is the number of all feature points, is the number of neighborhood feature points, is the natural exponential function.

[0016] The beneficial effects are as follows: By evaluating the uniformity of neighborhood feature points, the logistics robot can more accurately analyze the layout of the current environment and identify areas with dense or sparse obstacles; by calculating the local uniformity, it helps the logistics robot to dynamically adjust the path planning according to the distribution of environmental feature points, avoid overly crowded or empty areas, optimize the obstacle avoidance effect, and improve the real-time response ability; as the feature points in the environment change, the robot can adjust its behavior strategy in real time, so as to maintain a stable operating state in a dynamic environment.

[0017] Furthermore, the color distance satisfies the following relational expression:

[0018] ; in the formula, is the color distance between neighborhood feature point and neighborhood feature point , and are the pixel values of neighborhood feature point and neighborhood feature point respectively, and are the pixel values of neighborhood feature point and neighborhood feature point respectively, and are the pixel values of neighborhood feature point and neighborhood feature point respectively.

[0019] Furthermore, the feature distance satisfies the following relational expression:

[0020] ; in the formula, is the feature distance between neighborhood feature point and neighborhood feature point , is the local uniformity of the logistics robot in the current environment at the current moment, is the color distance between neighborhood feature point and neighborhood feature point , is the cosine similarity between the feature vector and the movement vector of neighborhood feature point , is the cosine similarity between the feature vector and the movement vector of neighborhood feature point , and are the Euclidean distances from neighborhood feature points and neighborhood feature point to the logistics robot respectively.

[0021] The beneficial effects are as follows: By comprehensively considering the similarity between vectors, color distance, and Euclidean distance, the logistics robot can more accurately measure the relationship between neighborhood feature points, improving the understanding of the environment; calculating the feature distance helps the logistics robot identify and analyze the feature differences of different obstacles in the environment, so as to better plan the path and make obstacle avoidance decisions to avoid collisions; as the surrounding feature points change, the robot can update the feature distance in real time to ensure timely and intelligent responses in a dynamic environment; by accurately calculating the distance between neighborhood feature points, the robot can identify potential collision risks earlier and improve the safety during driving.

[0022] Further, the determination of the obstacle point includes: among the two clustering clusters after clustering the neighborhood feature points, the neighborhood feature points within the clustering cluster with the shortest average Euclidean distance to the logistics robot are recorded as preliminary obstacle points; in response to the ratio of the number of preliminary obstacle points to the number of neighborhood feature points being greater than a preset threshold, the preliminary obstacle points are determined as obstacle points.

[0023] Further, the realization of automatically guiding the logistics robot to avoid obstacles includes: in response to the logistics robot identifying an obstacle point, adjusting the moving direction of the logistics robot to complete the obstacle avoidance of the automatically guided logistics robot.

[0024] In a second aspect, the present invention provides an obstacle avoidance system for an automatically guided logistics robot, adopting the following technical solution:

[0025] An obstacle avoidance system for an automatically guided logistics robot includes: a processor and a memory, and the memory stores computer program instructions, which, when executed by the processor, implement the above-mentioned obstacle avoidance method for an automatically guided logistics robot.

[0026] By adopting the above technical solution, the above-mentioned obstacle avoidance method for an automatically guided logistics robot is generated into a computer program and stored in the memory to be loaded and executed by the processor, so as to manufacture a terminal device according to the memory and the processor, which is convenient to use.

[0027] The present invention has the following technical effects:

[0028] Obtain the uniformity of all feature points and neighborhood feature points in the environment where the logistics robot is located according to the coordinate information of the feature points, so that obstacle recognition can be applied to different environments and object placement characteristics; calculate the local uniformity of the logistics robot in the current environment according to the uniformity of all feature points and neighborhood feature points in the environment where the logistics robot is located, narrow the range of obstacle recognition, and weaken the interference of the sparsity and non-uniformity of the feature point distribution on feature point clustering; the logistics robot performs obstacle recognition according to the local uniformity and neighborhood feature points, and combines the global information and local information of the feature point distribution, improving the accuracy of obstacle recognition and making the obstacle avoidance ability of the logistics robot higher during movement. Brief Description of the Drawings

[0029] By referring to the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0030] Figure 1 It is a flowchart of a method for obstacle avoidance of an automatic guided logistics robot according to an embodiment of the present invention.

[0031] Figure 2 It is a first schematic diagram of a plane coordinate system in a method for obstacle avoidance of an automatic guided logistics robot according to an embodiment of the present invention.

[0032] Figure 3 It is a second schematic diagram of a plane coordinate system in a method for obstacle avoidance of an automatic guided logistics robot according to an embodiment of the present invention.

[0033] Figure 4 It is a schematic diagram of neighborhood feature points in a method for obstacle avoidance of an automatic guided logistics robot according to an embodiment of the present invention. Detailed Embodiments

[0034] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0035] It should be understood that when terms such as "first" and "second" are used in the claims, the specification and the drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "comprising" and "including" used in the specification and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0036] An embodiment of the present invention discloses an obstacle avoidance method for an automatic guided logistics robot. Referring to Figure 1 , it includes steps S1 - S6:

[0037] S1: Obtain all feature points of the logistics robot in the current environment at the current moment.

[0038] The feature information included in the feature points includes the horizontal, vertical, and vertical coordinates in the space coordinate system, as well as , , pixel values.

[0039] During the movement of the automatic guided logistics robot, the feature points in the environment are obtained by the radar installed on the logistics robot, and the color information of the feature points is obtained through the echo energy. That is, each feature point has six - dimensional feature information including the horizontal coordinate, vertical coordinate, vertical coordinate, R - channel pixel value, G - channel pixel value, and B - channel pixel value.

[0040] S2: Obtain the moving direction of the logistics robot, construct a plane coordinate system in the moving direction, and calculate the uniformity of all feature points.

[0041] Obtain the moving direction of the logistics robot, construct a plane coordinate system in the moving direction, project all feature points onto a plane perpendicular to the moving direction of the logistics robot. As Figure 2 shown, in the figure, the feature points are exactly mapped in the direction of the coordinate axes of the feature points. However, the robot can move in any direction, so it may also map the feature points in any direction. As Figure 3 shown, it is a top view of the projection situation, and the , coordinates of all feature points on the plane coordinate system are obtained, and the variances of the coordinates of all feature points on each same coordinate axis in the space and plane coordinate systems are calculated.

[0042] It should be noted that since the feature points obtained by the radar have non - uniform data characteristics, which affect the recognition of obstacle points. In order to cope with the influence of this data characteristic on the recognition of obstacle points, the present invention obtains the uniformity of all feature points in the environment where the logistics robot is located according to the position information of the feature points.

[0043] Based on the variance and the dimensions of the circumscribed cubes of all feature points on each coordinate axis in the spatial coordinate system, calculate the uniformity of all feature points.

[0044] Specifically, the uniformity of all feature points satisfies the following relational expression:

[0045] ;

[0046] In the formula, is the uniformity of all feature points in the environment where the logistics robot is located at the current moment, are the horizontal, vertical, and vertical coordinate axes in the spatial coordinate system respectively, is the variance of the coordinates of all feature points on the th coordinate axis in the spatial coordinate system, is the dimension of the circumscribed cube of all feature points on the th coordinate axis in the spatial coordinate system, are respectively the , coordinate axes in the plane coordinate system, is the variance of the coordinates of all feature points on the th coordinate axis in the plane coordinate system.

[0047] Among them, is the normalized value of the variance of the coordinates of all feature points on the th coordinate axis in the environment where the logistics robot is located at the current moment. The larger this value is, the wider the distribution range of all feature points on the th coordinate axis is, which means that all feature points are more likely to be evenly distributed in space rather than concentrated in some areas. Then the uniformity of all feature points is greater. And the smaller this value is, the narrower the distribution range of all feature points on the th coordinate axis is, which means that all feature points are more likely to be concentratedly distributed in some areas of space. Then the uniformity of all feature points is smaller. Therefore, the larger it is, the greater the uniformity of all feature points, the smaller it is, the smaller the uniformity of all feature points; is the normalized value of the variance of the coordinates of all feature points on the th coordinate axis in the environment where the logistics robot is located at the current moment. When the logistics robot moves in different directions in the same scene, the objects it faces are in different states. Therefore, the objects faced by the logistics robot during the movement also have a certain significance for the obstacle avoidance of the logistics robot. The larger it is, the wider the distribution range of all feature points facing the object during the movement of the robot, which means that during the movement of the logistics robot, the feature points of the object faced by all feature points are more likely to be evenly distributed in space, and the greater the degree of uniformity of all feature points. The smaller it is, the more concentrated the distribution of all feature points in the moving direction of the logistics robot, which means that during the movement of the logistics robot, the feature points of the object faced by all feature points are more likely to be concentratedly distributed in space, and the smaller the degree of uniformity of all feature points.

[0048] S3: Determine the neighborhood feature points among all feature points and obtain the degree of uniformity of the neighborhood feature points.

[0049] Specifically, the determination of the neighborhood feature points among all feature points includes:

[0050] According to the Euclidean distance from all feature points to the center point of the logistics robot, use K-means clustering with K value of 2 to cluster all feature points to obtain two clusters, and record the feature points in the cluster with the shortest average Euclidean distance to the logistics robot (that is, the average of the Euclidean distances from each feature point in the cluster to the center point of the logistics robot) as neighborhood feature points, as Figure 4 shown.

[0051] The method for obtaining the degree of uniformity of neighborhood feature points is the same as the calculation method for the degree of uniformity of all feature points, and will not be elaborated here.

[0052] S4: Calculate the local uniformity of the environment.

[0053] It should be noted that during the obstacle avoidance process of the logistics robot, if directly clustering the feature points to extract the obstacle points, it may interfere with the recognition of the obstacle points due to the sparsity and non-uniform distribution characteristics of the feature points, affecting the obstacle avoidance action of the logistics robot. During the movement of the logistics robot, the nearby objects are more meaningful for the obstacle avoidance action of the logistics robot. In order to make the obstacle recognition of the logistics robot more accurate for more accurate obstacle avoidance, the present invention calculates the local uniformity of the environment where the logistics robot is located according to the degree of uniformity of all feature points in the environment where the logistics robot is located and the neighborhood feature points in the environment where the logistics robot is located.

[0054] Based on the degree of uniformity and quantity of all feature points and neighborhood feature points, calculate the local uniformity of the environment.

[0055] Specifically, the local uniformity satisfies the following relational expression:

[0056] ;

[0057] In the formula, is the local uniformity of the logistics robot in the current environment at the current moment. is the degree of uniformity of all feature points of the logistics robot in the current environment at the current moment. is the degree of uniformity of the neighborhood feature points of the logistics robot in the current environment at the current moment. is the number of all feature points. is the number of neighborhood feature points. is the natural exponential function.

[0058] Among them, represents the distribution of the feature points near the logistics robot. And the nearby feature points, that is, the neighborhood feature points, can represent to a certain extent the distribution of the feature points in the local range near the logistics robot. Therefore, the larger it is, the larger the local uniformity of the environment where the logistics robot is located. the smaller it is, the smaller the local uniformity of the environment where the logistics robot is located. represents the gap between the number of feature points excluding the neighborhood feature points and the number of neighborhood feature points among all the feature points of the logistics robot. The smaller this value is, the more likely there are certain objects near the logistics robot. Then the objects near the logistics robot are more likely to make the feature points near the logistics robot dense, and the local uniformity is smaller. The larger this value is, the more likely there are no certain objects near the logistics robot. Then the objects near the robot may make the feature points near the robot sparse, and the local uniformity is larger. represents the gap between the degree of uniformity of all feature points in the environment where the logistics robot is located and the degree of uniformity of the neighborhood feature points. The smaller this value is, the less likely there are locally dense feature points in the environment where the logistics robot is located. The distribution of the feature points in the range near the logistics robot and in the environment where the logistics robot is located is more likely to be uniform, and the local uniformity is larger. The larger this value is, the more likely there are dense distributions of feature points in the range near the logistics robot or outside the nearby range, and the local uniformity is relatively smaller.

[0059] S5: Calculate the feature distance between any two neighborhood feature points.

[0060] It should be noted that due to the sparsity and non-uniform distribution of the feature points collected during the movement of the logistics robot, this data characteristic may interfere with the recognition of the feature points of the obstacle. During the obstacle avoidance action of the logistics robot, the feature points closer to the logistics robot are more meaningful for obstacle avoidance. Identifying obstacles through the neighborhood feature points of the robot can reduce the influence of the inherent characteristics of the feature points obtained by the radar on obstacle recognition. Therefore, the present invention performs obstacle recognition based on the local uniformity of the feature points of the logistics robot and the neighborhood feature points of the logistics robot.

[0061] Denote the unit vector of the moving direction as the moving vector, and the unit vector of the neighborhood feature points as the feature vector. Based on the local uniformity, the cosine similarity between the feature vectors of any two neighborhood feature points and the moving vector, the Euclidean distances from any two neighborhood feature points to the center point of the logistics robot, and using 、 、 the color distances of any two neighborhood feature points obtained from the pixel values, calculate the feature distances between any two neighborhood feature points.

[0062] Specifically, the color distance satisfies the following relational expression:

[0063] ;

[0064] In the formula, is the color distance between neighborhood feature point and neighborhood feature point , and are respectively the pixel values of neighborhood feature point and neighborhood feature point , and are respectively the pixel values of neighborhood feature point and neighborhood feature point , and are respectively the pixel values of neighborhood feature point and neighborhood feature point .

[0065] Specifically, the feature distance satisfies the following relational expression:

[0066] ;

[0067] In the formula, is the feature distance between neighborhood feature point and neighborhood feature point , is the local uniformity of the environment where the logistics robot is located at the current moment, is the color distance between neighborhood feature point and neighborhood feature point , is the cosine similarity between the feature vector of neighborhood feature point and the moving vector, is the cosine similarity between the feature vector of neighborhood feature point and the moving vector, and are respectively the neighborhood feature points and neighborhood feature points The Euclidean distance to the logistics robot.

[0068] Among them, is the color distance between neighborhood feature points and Since the objects in the scene where the logistics robot is located have color continuity, that is, the colors of multiple feature points corresponding to one object are the same or similar, and the colors between feature points corresponding to different objects may have certain differences. In order to cluster multiple feature points corresponding to the same object into the same category, The larger, the greater the feature distance between the two neighborhood feature points, The smaller, the smaller the feature distance between the two neighborhood feature points; is the difference in the Euclidean distance between the two neighborhood feature points and the center point of the logistics robot. Since the Euclidean distances between the feature points belonging to the same object and the logistics robot are similar, in order to better identify obstacles, therefore The larger, the greater the feature distance between the two neighborhood feature points, The smaller, the smaller the feature distance between the two neighborhood feature points; When there is an object occlusion near the logistics robot, the object will cause the feature points near the logistics robot to show a locally dense situation, while when there is no object occlusion near the logistics robot, the feature points near the logistics robot will show a relatively uniform distribution. At this time, the data volumes of the two types of neighborhood feature points clustered may be relatively similar, resulting in misidentification of obstacles. Therefore, when the logistics robot is moving When it is larger, should be reduced on the influence of the feature distance, When it is smaller, should maintain on the influence of the feature distance. Therefore, divide by When it is larger, is smaller, then on the influence of the feature distance is smaller, is relatively larger when it is smaller, then is closer to to maintain on the influence of the feature distance.

[0069] S6: According to the feature distance, use k-means clustering to cluster the neighborhood feature points and determine the obstacle points to achieve obstacle avoidance for the automatically guided logistics robot.

[0070] Specifically, the determination of the obstacle points includes:

[0071] of mean clustering The value is 2. Among the two clustering clusters after clustering the neighborhood feature points, the neighborhood feature points in the clustering cluster with the shortest average Euclidean distance to the logistics robot are denoted as preliminary obstacle points;

[0072] In response to the ratio of the number of preliminary obstacle points to the number of neighborhood feature points being greater than a preset threshold, the preliminary obstacle points are determined as obstacle points.

[0073] Implementers can set the threshold according to the specific implementation situation. For example, 0.5.

[0074] Specifically, the method for realizing obstacle avoidance of an automatically guided logistics robot includes:

[0075] In response to the logistics robot identifying an obstacle point, adjust the moving direction of the logistics robot to complete the obstacle avoidance of the automatically guided logistics robot.

[0076] An embodiment of the present invention also discloses an obstacle avoidance system for an automatically guided logistics robot, including a processor and a memory. The memory stores computer program instructions, and when the computer program instructions are executed by the processor, an obstacle avoidance method for an automatically guided logistics robot according to the present invention is realized.

[0077] The above system further includes other components well known to those skilled in the art such as a communication bus and a communication interface, and their settings and functions are known in the art, so they will not be elaborated here.

[0078] In the present invention, the aforementioned memory can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, device, or component. For example, a computer-readable storage medium can be any suitable magnetic storage medium or magneto-optical storage medium, or any other medium that can be used to store the required information and can be accessed by an application program, module, or both. Any such computer storage medium can be part of the device or accessible or connectable to the device.

[0079] Although this specification has shown and described multiple embodiments of the present invention, it is obvious to those skilled in the art that such embodiments are provided only by way of example. Those skilled in the art will think of many changes, alterations, and alternative ways without departing from the spirit and idea of the present invention. It should be understood that various alternative solutions to the embodiments of the present invention described herein can be adopted in the process of practicing the present invention.

[0080] The above are all preferred embodiments of the present invention. The protection scope of the present invention is not limited by this. Therefore, all equivalent changes made according to the structure, shape, and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. An obstacle avoidance method for an automatic guided logistics robot, characterized in that: include: Obtain all feature points of the logistics robot in the environment at the current moment, the feature information of the feature points includes the horizontal, vertical and vertical coordinates in the spatial coordinate system, and , , Pixel value; Get the moving direction of the logistics robot, build a plane coordinate system in the moving direction, and get all the feature points in the plane coordinate system. , Coordinates, calculate the variance of the coordinates of all feature points on each of the same coordinate axes in the space and plane coordinate systems; based on the variance and the size of the smallest circumscribed cube of all feature points on each coordinate axis in the space coordinate system, calculate the uniformity of all feature points; determine the neighborhood feature points among all feature points, and obtain the uniformity of the neighborhood feature points; calculate the local uniformity of the environment based on the uniformity and quantity of all feature points and neighborhood feature points; record the unit vector of the moving direction as the moving vector, and record the unit vector of the neighborhood feature point as the feature vector, based on the local uniformity, the cosine similarity of the feature vectors of any two neighborhood feature points and the moving vector, the Euclidean distance from any two neighborhood feature points to the logistics robot, and use , , The color distance between any two neighborhood feature points obtained by the pixel value is calculated to calculate the feature distance between any two neighborhood feature points; based on the feature distance, use Mean clustering is used to cluster neighborhood feature points and determine obstacle points to achieve obstacle avoidance for the automatic guided logistics robot.

2. The obstacle avoidance method of an automatic guided logistics robot according to claim 1, characterized in that: The uniformity of all the feature points satisfies the following relationship: ； In the formula, is the uniformity of all feature points in the environment where the logistics robot is currently located. are the horizontal, vertical and vertical axes in the spatial coordinate system, is the number of feature points in the spatial coordinate system The variance of the coordinates on the axes, The smallest circumscribed cube of all feature points in the spatial coordinate system The dimensions on the coordinate axes, They are respectively , Coordinate axes, is the number of all feature points in the plane coordinate system The variance of the coordinates on the axes.

3. The obstacle avoidance method of an automatic guided logistics robot according to claim 1, characterized in that: The step of determining the neighborhood feature points among all the feature points includes: According to the Euclidean distance from all feature points to the center point of the logistics robot, K-means clustering is used to cluster all feature points to obtain two clusters. The feature points in the cluster with the shortest average Euclidean distance to the logistics robot are recorded as neighborhood feature points.

4. The obstacle avoidance method of an automatic guided logistics robot according to claim 1, characterized in that: The local uniformity satisfies the following relationship: ； In the formula, is the local uniformity of the logistics robot in the environment at the current moment, is the uniformity of all feature points in the environment where the logistics robot is currently located. is the uniformity of the neighborhood feature points of the logistics robot in the environment at the current moment, is the number of all feature points, is the number of neighborhood feature points, is a natural exponential function.

5. The obstacle avoidance method of an automatic guided logistics robot according to claim 1, characterized in that: The color distance satisfies the following relationship: ； In the formula, Neighborhood feature points and neighborhood feature points The color distance, and Neighborhood feature points and neighborhood feature points of Pixel value, and Neighborhood feature points and neighborhood feature points of Pixel value, and Neighborhood feature points and neighborhood feature points of Pixel value.

6. The obstacle avoidance method of an automatic guided logistics robot according to claim 1, characterized in that: The characteristic distance satisfies the following relationship: ； In the formula, Neighborhood feature points and neighborhood feature points The characteristic distance of is the local uniformity of the logistics robot in the environment at the current moment, Neighborhood feature points and neighborhood feature points The color distance, Neighborhood feature points The cosine similarity between the feature vector and the motion vector, Neighborhood feature points The cosine similarity between the feature vector and the motion vector, and Neighborhood feature points and neighborhood feature points Euclidean distance to the logistics robot.

7. The obstacle avoidance method of an automatic guided logistics robot according to claim 1, characterized in that: The step of determining the obstacle point comprises: Among the two clusters after the clustering of the neighborhood feature points is completed, the neighborhood feature points in the cluster with the shortest average Euclidean distance to the logistics robot are recorded as preliminary obstacle points; In response to the ratio of the number of the preliminary obstacle point to the number of the neighborhood feature points being greater than a preset threshold, the preliminary obstacle point is identified as an obstacle point.

8. The obstacle avoidance method of an automatic guided logistics robot according to claim 1, characterized in that: The method for realizing obstacle avoidance of the automatic guided logistics robot includes: In response to the logistics robot identifying an obstacle point, the moving direction of the logistics robot is adjusted to complete the obstacle avoidance of the automatic guided logistics robot.

9. An obstacle avoidance system for an automatic guided logistics robot, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an obstacle avoidance method for an automatic guided logistics robot according to any one of claims 1-8 is implemented.

Images