Mobile storage and charging robot collaborative arrangement method based on specific scene

Abstract

The invention provides a mobile storage and charging robot collaborative arrangement method based on a specific scene, and relates to the technical field of mobile storage and charging robots, and the method comprises the steps: recognizing the feature parameters of a target scene through fusing a scene image, a point cloud image and a planning image; determining a scene demand according to the scene plan of the target scene; cooperatively planning an arrangement scheme of the mobile storage and charging robot according to the characteristic parameters and the scene requirements; according to the arrangement scheme, the mobile storage and charging robot is guided to reach a designated position; the specific scene is automatically identified, the arrangement scheme of the robot is cooperatively planned according to scene features and arrangement requirements, and the arrangement efficiency and practicability of the mobile storage and charging robot in the specific scene are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile storage and charging robots, and in particular to a method for collaboratively arranging mobile storage and charging robots based on specific scenarios. Background Art

[0002] With the widespread application of new energy vehicles, mobile charging robots, as a new type of charging equipment, have gradually attracted attention. Mobile charging robots can carry multiple rechargeable battery packs and provide instant charging services wherever charging is needed. However, in specific scenarios, such as large-scale events and temporary charging needs, how to efficiently and practically deploy mobile charging robots is an urgent problem to be solved.

[0003] In view of this, the present invention provides a collaborative deployment method for mobile storage and charging robots based on specific scenarios, so as to automatically identify specific scenarios, and collaboratively plan the deployment of robots according to scene characteristics and deployment requirements, thereby improving the deployment efficiency and practicality of mobile storage and charging robots in specific scenarios. Summary of the invention

[0004] The object of the present invention is to provide a method for collaboratively arranging mobile storage and charging robots based on specific scenarios, comprising: identifying characteristic parameters of a target scene by fusing a scene image, a point cloud image and a planning image; the characteristic parameters include the area, shape and obstacle position of the scene; wherein, when performing image fusion, the point cloud features of the point cloud image are obtained by extracting the scene features of the scene image; determining the scene requirements according to the scene planning of the target scene; the scene planning includes the duration of the activity, peak hours, vehicle ratio and number of participants; the scene requirements include the number, location and power requirements of charging piles; collaboratively planning the arrangement of mobile storage and charging robots according to the characteristic parameters and the scene requirements; the arrangement plan includes the number of mobile storage and charging robots, designated locations and the number and power of battery packs carried by each mobile storage and charging robot; guiding the mobile storage and charging robot to reach the designated location according to the arrangement plan; the designated location refers to the service waiting position allocated to the mobile storage and charging robot in the arrangement plan.

[0005] Further, identifying the characteristic parameters of the target scene includes: extracting features from the planning image to obtain planning features; aligning the scene image and the point cloud image and fusing them to obtain a first fused image; aligning the planning image and the first fused image and fusing them to obtain a second fused image; reconstructing the image based on the second fused image to obtain a reconstructed scene model; extracting features from the reconstructed scene model to obtain reconstruction characteristic parameters. Based on the difference between the planning features and the reconstruction characteristic parameters, adjusting the reconstructed scene model and the reconstruction characteristic parameters to obtain an initial scene model and the characteristic parameters.

[0006] Further, obtaining the first fused image includes: extracting features from the scene image to obtain scene features; projecting the scene features into 3D space to obtain three-dimensional scene features; calculating the distance between each point cloud point in the point cloud image and the three-dimensional scene feature point; and determining the probability of each point cloud point in the point cloud image being selected based on the distance between the point cloud point and the three-dimensional scene feature point; the calculation formula for the probability of a point cloud point being selected is: Among them, P sample (p) represents the probability of point cloud point p being selected; ρ p represents the point cloud density at point p; e * represents the exponential function; D p represents the minimum distance between the point cloud point p and the feature point of the 3D scene; σ represents the adjustment parameter; σ p Representing the Gaussian scale of the three-dimensional scene feature point with the smallest distance to the point cloud point p; selecting the initial point cloud feature based on the probability of the point cloud point being selected, and constructing the point cloud feature vector of each initial point cloud feature point; determining the three-dimensional feature vector of the three-dimensional scene feature point; calculating the feature distance between the point cloud feature vector and the three-dimensional feature vector, determining the point cloud feature matching the three-dimensional scene feature, and obtaining a feature pair set; determining a transformation matrix based on the feature pair set; based on the transformation matrix, mapping the point cloud image to the scene image to obtain the first fused image.

[0007] Furthermore, determining scenario requirements includes: predicting the maximum number of parked new energy vehicles during the activity based on the scenario planning; the maximum number of parked new energy vehicles refers to the maximum number of new energy vehicles parked in the parking lot during the activity; calculating the minimum entrance distance between the charging pile and the parking lot entrance and the minimum exit distance to the parking lot exit; the minimum entrance distance refers to the shortest distance between the charging pile and the parking lot entrance; the minimum exit distance refers to the shortest distance between the charging pile and the parking lot exit; based on the entrance distance, the exit distance and the maximum number of parked vehicles, determining the number and location of the charging piles; and determining the power requirements of the charging piles based on the activity duration, the peak period and the charging demand distribution.

[0008] Furthermore, determining the location of the charging piles includes: selecting a plurality of charging piles as initial cluster centers based on the sum of the minimum entrance distance and the minimum exit distance; the number of the initial cluster centers is the maximum number of parking spaces; the distance of each initial cluster center is greater than the service distance of the mobile storage and charging robot; the service distance refers to the farthest moving distance of the mobile storage and charging robot; based on the initial cluster centers, clustering the charging piles in the parking lot to obtain a plurality of initial cluster clusters; the distance between the charging piles in each group of the initial cluster clusters is less than the service distance; for each of the initial cluster clusters, respectively calculating the average location of all the charging piles in the cluster, and using the average location as the new cluster center; based on the new cluster center, clustering the charging piles in the parking lot The charging piles are clustered again to obtain new clusters; the clustering cluster update process is repeated until the clusters are stable or the maximum number of iterations is reached to obtain clustering results; based on the entrance distance and exit distance of the charging piles in each cluster in the clustering results, the score of each cluster is determined; the entrance distance refers to the distance between the charging pile and the multiple entrances of the parking lot; the exit distance refers to the distance between the charging pile and the multiple exits of the parking lot; based on the score of each cluster, the clusters in the clustering results are sorted to obtain a sorting result; the sorting result is used to indicate the convenience of the parking space at the charging pile to enter and exit the parking lot; based on the sorting result, the charging piles in one or more clusters are selected as the charging piles required by the scenario, and the positions of the charging piles are obtained.

[0009] Furthermore, the average value of the charging pile scores in each cluster is used as the score of each cluster, and the calculation formula of the charging pile score is: Among them, S core represents the score; im represents the entrance variable; IM represents the total number of parking lot entrances; P im represents the probability of new energy vehicles entering from entrance im; e * represents an exponential function; λ represents the attenuation coefficient related to the parking lot size; d im，ch represents the distance between the entrance im and the charging pile ch; ch represents the charging pile variable; ex represents the exit variable; EX represents the total number of parking lot exits; P ex represents the probability of new energy vehicles leaving from exit ex; d ex，ch Indicates the distance between the exit ex and the charging station ch.

[0010] Furthermore, the arrangement of mobile storage and charging robots is collaboratively planned through a genetic algorithm, including: taking the number of the mobile storage and charging robots as the chromosome length and the designated positions as genes, initializing the population, and obtaining a plurality of first arrangement schemes; calculating the fitness value of each first arrangement scheme based on a fitness function; obtaining a plurality of second arrangement schemes by screening through a roulette wheel method based on the fitness values; performing a crossover and mutation operation on the second arrangement schemes to obtain a third arrangement scheme; taking the third arrangement scheme as a new first arrangement scheme, repeating the screening process and the crossover and mutation process until a stopping condition is reached; and taking the second arrangement scheme that reaches the stopping condition as the arrangement scheme.

[0011] Furthermore, the calculation formula of the fitness function is: Among them, Fi represents the fitness function, the lower the value, the higher the fitness; C represents the adjustment parameter; P p represents the predicted peak power demand of electric energy; T p represents the predicted duration of the peak power; v represents the moving speed of the mobile storage and charging robot; Indicates the minimum average value of the distance between the charging pile and the designated location of the mobile charging robot; E b Indicates the capacity of the battery; T s It represents the time required for the mobile charging robot to replace batteries; M represents the number of battery packs carried by the robot; and T represents the duration of the activity.

[0012] Furthermore, guiding the mobile storage robot to reach the designated position includes: determining an initial map of the target scene and an initial posture of the mobile storage robot; collecting environmental data through sensors arranged on the mobile storage robot; executing a SLAM algorithm, updating the initial map and the initial posture, and obtaining a current map and a current posture; based on the current map and the current posture, calculating an optimal path for the mobile storage robot to reach the designated position from the current position through a path planning algorithm; the optimal path includes multiple moving positions; based on the optimal path, controlling the mobile storage robot to reach the next moving position, and obtaining a new current position; judging whether the distance between the new current position and the designated position is less than a preset distance threshold; if so, determining that the mobile storage robot has reached the designated position, and stopping iteration; if not, repeating the operation of updating the current position based on the current map and the current posture.

[0013] Furthermore, the initial map is an initial scene model, and the initial scene model is a modeling of the target scene obtained by fusing a scene image, a point cloud image and a planning image.

[0014] The technical solution of the embodiment of the present invention has at least the following advantages and beneficial effects:

[0015] The present invention improves the deployment efficiency and practicality of the mobile storage and charging robot in specific scenarios by automatically identifying specific scenarios and collaboratively planning the deployment of robots according to scenario characteristics and deployment requirements.

[0016] The present invention obtains a reconstructed scene model through the second fused image, and can correct the first fused image through more accurate features in the planning image, so that the obtained second fused image can more accurately represent the layout of the actual scene, thereby improving the accuracy of the reconstructed scene model.

[0017] The present invention extracts point cloud features through extracted scene features, which can improve the efficiency of extracting point cloud features and avoid spending unnecessary time on unmatched features.

[0018] The present invention obtains the locations of multiple charging piles by clustering based on the distances between the charging piles and the entrances and exits, so that the participants can enter and exit the parking lot more conveniently, reducing the time spent by the participants on detours in the parking lot.

[0019] The present invention determines the layout plan through a genetic algorithm, which can make the position of the mobile storage and charging robot more reasonable, ensure the charging efficiency, reduce the number of mobile storage and charging robots, and reduce the cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 An exemplary flow chart of a method for collaborative deployment of mobile storage and charging robots based on a specific scenario provided by the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0022] Figure 1 This is an exemplary flow chart of a method for collaboratively deploying mobile storage and charging robots based on a specific scenario provided by the present invention. Figure 1 As shown, the method for collaboratively deploying mobile storage and charging robots based on specific scenarios provided by the present invention includes the following contents:

[0023] Step 110, identifying characteristic parameters of the target scene by fusing the scene image, the point cloud image and the planning image; the characteristic parameters include the area, shape and obstacle position of the scene; wherein, when performing image fusion, the point cloud features of the point cloud image are obtained by extracting the scene features of the scene image. The target scene refers to a scene where a mobile storage and charging robot needs to be deployed; for example, scenes such as concerts and exhibitions. The characteristic parameters may be related to the characteristics of a building. A scene image may refer to an image acquired by an image acquisition device such as a camera; for example, a visible light image and a depth image. A point cloud image refers to point cloud data acquired by a laser radar (LiDAR); for example, 2D point cloud data and 3D point cloud data. A planning image may refer to an image used for site planning of scene layout; for example, an architectural drawing.

[0024] Step 120, determining the scenario requirements according to the scenario planning of the target scenario; the scenario planning includes the duration of the activity, peak hours, vehicle ratio and number of participants; the scenario requirements include the number, location and power requirements of charging piles. The scenario planning may be related to the usage of the scenario.

[0025] Step 130, collaboratively planning the deployment of mobile storage and charging robots based on the characteristic parameters and the scenario requirements; the deployment plan includes the number of mobile storage and charging robots, designated locations, and the number and power of battery packs carried by each mobile storage and charging robot. Mobile storage and charging robots refer to automated equipment used to provide mobile charging services for new energy vehicles.

[0026] Step 140, according to the arrangement plan, guide the mobile storage and charging robot to reach a designated position; the designated position refers to the service waiting position allocated to the mobile storage and charging robot in the arrangement plan. The robot can be arranged to the designated position through a SLAM (real-time localization and mapping) algorithm.

[0027] Identifying characteristic parameters of the target scene includes: extracting features from the planning image to obtain planning features. Planning features may refer to features related to building information. Image processing and feature extraction may be performed on the planning image through a computer vision library, a deep learning model, etc. to obtain planning features. For example, feature extraction is performed on the planning image through a SIFT (Scale Invariant Feature Transform) or SURF (speeded-Up Robust Features) algorithm. The scene image and the point cloud image are aligned and fused to obtain a first fused image. The first fused image refers to a new scene image with point cloud information obtained after mapping the point cloud image onto the scene image. The planning image and the first fused image are aligned and fused to obtain a second fused image. The second fused image refers to an image with scene and point cloud information obtained after mapping the first fused image onto the planning image. Since the planning features are not easy to change, such as the position of the building wall, the second fused image obtained by fusing these relatively accurate features can more accurately represent the layout of the actual scene. The planning image and the first fused image may be fused by fusing the scene image and the point cloud image to obtain a second fused image. Image reconstruction is performed based on the second fused image to obtain a reconstructed scene model. The reconstructed scene model may refer to a virtual scene model obtained by modeling the scene according to the data acquired by the sensor. Specifically, 3D reconstruction may be performed by the point cloud information in the second fused image to obtain a reconstructed scene model; of course, the second fused image may also be processed by other methods to obtain a reconstructed scene model, for example, various image-based modeling methods. Feature extraction is performed on the reconstructed scene model to obtain a reconstruction feature parameter. The reconstruction feature parameter may refer to a feature parameter extracted from the reconstructed scene model. Feature extraction may be performed on the reconstructed scene model by various feasible methods, including but not limited to 3D descriptors, global feature extraction, and deep learning. Based on the difference between the planning feature and the reconstruction feature parameter, the reconstructed scene model and the reconstruction feature parameter are adjusted to obtain an initial scene model and the feature parameter. For example, the position of other pixel points may be corrected based on the distance between the position of the reconstruction feature parameter and the corresponding planning feature point position.

[0028] Obtaining a first fused image includes: extracting features from the scene image to obtain scene features. The scene features can be used to represent features in the scene image. Projecting the scene features into a 3D space to obtain three-dimensional scene features. The three-dimensional scene features can be used to represent features of the scene features in the three-dimensional space. The calculation formula for the three-dimensional scene features is: Among them, X i′ represents the horizontal coordinate of the three-dimensional scene feature; Y i ′ represents the vertical coordinate of the three-dimensional scene feature; Z i ′ represents the vertical coordinate of the three-dimensional scene feature; fx represents the focal length of the camera in the horizontal axis direction; fy represents the focal length of the camera in the y-axis direction; cx represents the horizontal coordinate offset of the camera's main optical center in the image coordinate system; cy represents the vertical coordinate offset of the camera's main optical center in the image coordinate system; x i The horizontal axis represents the scene characteristics; y i The vertical coordinate representing the scene features; depth(x i ,y i ) represents the depth value of the feature point in the scene feature, which can be obtained by at least recognizing the depth image obtained by the depth camera; i represents the three-dimensional scene feature variable. Calculate the distance between each point cloud point in the point cloud image and the three-dimensional scene feature point. The distance can be calculated by Euclidean distance and cosine similarity. Based on the distance between the point cloud point and the three-dimensional scene feature point, determine the probability of each point cloud point in the point cloud image being selected; the calculation formula for the probability of the point cloud point being selected is: Among them, P sampe (p) represents the probability of point cloud point p being selected; ρ p represents the point cloud density at point p; e * represents the exponential function; D p represents the minimum distance between the point cloud point p and the feature point of the 3D scene; σ represents the adjustment parameter; σ p Represents the Gaussian scale of the three-dimensional scene feature point with the smallest distance to the point cloud point p; based on the probability of the point cloud point being selected, the initial point cloud feature is selected, and a point cloud feature vector is constructed for each initial point cloud feature point. The point cloud feature may refer to the feature of the selected point cloud image, and the initial point cloud feature may refer to the point cloud feature selected for the first time. The initial point cloud feature needs to be processed to obtain the final point cloud feature. The point cloud feature vector can be used to reflect the information of the selected point cloud feature. For example, the point cloud feature vector can be determined by a fast point feature histogram. Specifically, the point cloud point with a probability greater than a preset probability threshold can be selected as the initial point cloud feature. The preset probability threshold may refer to a pre-set minimum value for selecting point cloud points. If it exceeds the preset probability threshold, it is selected as the initial point cloud point. The FPFH feature of the point cloud point is (X j , Y j , Z j , P sample (j), r), P sample (j) represents the probability of point cloud point j being selected, through P sample (p) calculated; X j Represents the horizontal coordinate Y of point cloud point j in the point cloud image j Represents the vertical coordinate of point cloud point j in the point cloud image; Zj Indicates the vertical coordinate of point cloud point j in the point cloud image; [*] T represents the transpose of the matrix; r represents the local neighborhood radius for calculating FPFH. Determine the three-dimensional feature vector of the three-dimensional scene feature point. The three-dimensional feature vector can be used to reflect the information of the three-dimensional scene feature point. For example, the three-dimensional feature vector can be obtained by the SIFT algorithm (feature detection algorithm). Specifically, the three-dimensional feature vector is (x i ,y i , σ i , d i ), d i Represents the feature descriptor of the 3D scene feature point i; σ i Represents the Gaussian scale of the three-dimensional scene feature point i. Calculate the feature distance between the point cloud feature vector and the three-dimensional feature vector, determine the point cloud features that match the three-dimensional scene features, and obtain a feature pair set. Specifically, the set includes multiple pairs of corresponding three-dimensional scene features and point cloud features. Specifically, the initial point cloud feature points whose feature distances are less than the preset feature distances can be used as point cloud points, and the set of point cloud points can be used as point cloud features. The preset feature distance refers to the maximum distance between the matching point cloud feature points and the three-dimensional scene feature points that is preset. When it is greater than this distance, the two points are considered to be mismatched. Based on the feature pair set, determine the transformation matrix. Based on the transformation matrix, map the point cloud image to the scene image to obtain the first fused image.

[0029] Determine scenario requirements, including: based on the scenario planning, predict the maximum number of new energy vehicles parked during the activity; the maximum number of parking refers to the maximum number of new energy vehicles parked in the parking lot during the activity. The maximum number of parking can be obtained in various feasible ways, including but not limited to prediction through historical data or deep learning models. Calculate the minimum entrance distance between the charging pile and the parking lot entrance and the minimum exit distance to the parking lot exit; the minimum entrance distance refers to the shortest distance between the charging pile and the parking lot entrance; the minimum exit distance refers to the shortest distance between the charging pile and the parking lot exit. Based on the entrance distance, the exit distance and the maximum number of parking, determine the number and location of charging piles.

[0030] Based on the activity duration, the peak period and the charging demand distribution, the power demand of the charging pile is determined. The charging demand distribution may refer to the power demand distribution of new energy vehicles. The peak period and its charging demand distribution may be obtained by analyzing historical data. For example, a charging demand curve is created based on the data to understand the demand distribution in different periods; the required average power and peak power are estimated based on the total energy demand and peak demand; the charging pile model with appropriate power is selected, and the required quantity is calculated.

[0031] Determining the location of the charging piles includes: based on the sum of the minimum entrance distance and the minimum exit distance, selecting multiple charging piles as initial cluster centers; the number of the initial cluster centers is the maximum parking number; the distance of each initial cluster center is greater than the service distance of the mobile storage and charging robot; the service distance refers to the farthest moving distance set for the mobile storage and charging robot. Based on the initial cluster centers, clustering the charging piles in the parking lot to obtain multiple initial cluster clusters; the distance between the charging piles in each group of the initial cluster clusters is less than the service distance. For each of the initial cluster clusters, the average position of all the charging piles in the cluster is calculated respectively, and the average position is used as the new cluster center. Based on the new cluster center, clustering the charging piles in the parking lot again to obtain a new cluster cluster. Repeat the cluster cluster update process until the cluster cluster is stable or the maximum number of iterations is reached to obtain a clustering result. Based on the entrance distance and exit distance of the charging pile in each cluster in the clustering result, determine the score of each cluster; the entrance distance refers to the distance between the charging pile and the multiple entrances of the parking lot; the exit distance refers to the distance between the charging pile and the multiple exits of the parking lot. Based on the score of each cluster, sort the clusters in the clustering result to obtain a sorting result; the sorting result is used to indicate the convenience of the parking space at the charging pile to enter and exit the parking lot. Based on the sorting result, select the charging pile in one or more clusters as the charging pile required by the scenario, and obtain the location of the charging pile.

[0032] The average value of the charging pile scores in each cluster is taken as the score of each cluster. The calculation formula of the charging pile score is: Among them, S core represents the score; im represents the entrance variable; IM represents the total number of parking lot entrances; P im represents the probability of new energy vehicles entering from entrance im; e * represents an exponential function; λ represents the attenuation coefficient related to the parking lot size; d im，ch represents the distance between the entrance im and the charging pile ch; ch represents the charging pile variable; ex represents the exit variable; EX represents the total number of parking lot exits; P ex represents the probability of new energy vehicles leaving from exit ex; d ex，ch Indicates the distance between the exit ex and the charging station ch.

[0033] The arrangement scheme of the mobile storage and charging robots is collaboratively planned by a genetic algorithm, including: using the number of the mobile storage and charging robots as the chromosome length, the designated positions as genes, initializing the population, and obtaining a plurality of first arrangement schemes. The first arrangement scheme may refer to the parent chromosome used for genetic algorithm processing. Based on the fitness function, the fitness value of each first arrangement scheme is calculated. Based on the fitness value, a plurality of second arrangement schemes are screened by roulette. A crossover and mutation operation is performed on the second arrangement scheme to obtain a third arrangement scheme. The third arrangement scheme is used as a new first arrangement scheme, and the screening process and the crossover and mutation process are repeated until a stopping condition is reached. The stopping condition includes but is not limited to the fitness value being less than a preset fitness threshold or the number of iterations reaching a threshold. The second arrangement scheme that reaches the stopping condition is used as the arrangement scheme.

[0034] The calculation formula of the fitness function is: In the above figure, Fi represents the fitness function, the lower the value, the higher the fitness; C represents the adjustment parameter; P p represents the predicted peak power demand of electric energy; T p represents the predicted duration of the peak power; v represents the moving speed of the mobile storage and charging robot; Indicates the minimum average value of the distance between the charging pile and the designated location of the mobile charging robot; F b Indicates the capacity of the battery; T s It represents the time required for the mobile charging robot to replace batteries; M represents the number of battery packs carried by the robot; and T represents the duration of the activity.

[0035] Guiding the mobile storage robot to reach a designated location includes: determining an initial map of the target scene and an initial posture of the mobile storage robot. The initial map may refer to a map before updating the map based on the robot's travel process. The initial map may be an initial scene model, and the initial scene model is a modeling of the target scene obtained by fusing a scene image, a point cloud image, and a planning image. The initial posture may refer to the position and posture information of the robot before it moves. Environmental data is collected by sensors arranged on the mobile storage robot. Environmental data may include point cloud data, acceleration, angular velocity, and moving distance acquired by the robot. SLAM (Simultaneous Localization and Mapping) algorithm is executed to update the initial map and the initial posture to obtain a current map and a current posture. The current map refers to a map of a parking lot updated according to the environmental data acquired by the robot. The current posture refers to the current position and posture of the robot. Based on the current map and the current posture, the optimal path for the mobile storage robot to reach the designated location from the current position is calculated by a path planning algorithm; the optimal path includes multiple moving positions. The optimal path may refer to the most reasonable driving route for the robot to reach the designated location. The most reasonable may mean the fastest and safest. Based on the optimal path, the mobile storage robot is controlled to reach the next moving position and obtain a new current position. The next moving position may refer to the position of the next point that the robot needs to reach on the optimal path. Determine whether the distance between the new current position and the designated position is less than a preset distance threshold; if so, determine that the mobile storage robot has reached the designated position and stop iterating; if not, repeat the operation of updating the current position based on the current map and the current posture. The preset distance threshold may refer to a preset minimum error distance when the robot reaches the designated position.

[0036] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for collaborative deployment of mobile storage and charging robots based on specific scenarios, characterized in that: include: Identify the characteristic parameters of the target scene by fusing the scene image, point cloud image and planning image; The characteristic parameters include the area, shape and obstacle position of the scene; wherein, when image fusion is performed, the point cloud features of the point cloud image are obtained by extracting the scene features of the scene image; Determine the scenario requirements according to the scenario planning of the target scenario; the scenario planning includes the duration of the activity, peak hours, vehicle ratio and number of participants; the scenario requirements include the number, location and power requirements of charging piles; According to the characteristic parameters and the scenario requirements, collaboratively plan the layout of the mobile storage and charging robots; the layout plan includes the number of mobile storage and charging robots, designated locations, and the number and power of battery packs carried by each mobile storage and charging robot; According to the arrangement plan, the mobile storage and charging robot is guided to reach a designated position; the designated position refers to a service waiting position allocated to the mobile storage and charging robot in the arrangement plan.

2. The method for collaborative deployment of mobile storage and charging robots based on specific scenarios according to claim 1 is characterized in that: Identify the characteristic parameters of the target scene, including: Extracting features from the planning image to obtain planning features; Aligning the scene image and the point cloud image and fusing them to obtain a first fused image; Aligning the planning image with the first fused image and fusing them to obtain a second fused image; Performing image reconstruction based on the second fused image to obtain a reconstructed scene model; Extracting features from the reconstructed scene model to obtain reconstruction feature parameters; Based on the difference between the planning feature and the reconstruction feature parameter, the reconstruction scene model and the reconstruction feature parameter are adjusted to obtain an initial scene model and the feature parameter.

3. The method for collaboratively deploying mobile storage and charging robots based on specific scenarios according to claim 2 is characterized in that: The first fused image is obtained, including: Extracting features from the scene image to obtain scene features; Projecting the scene features into 3D space to obtain three-dimensional scene features; Calculating the distance between each point cloud point in the point cloud image and a feature point of the three-dimensional scene; Based on the distance between the point cloud point and the feature point of the three-dimensional scene, the probability of each point cloud point in the point cloud image being selected is determined; the calculation formula for the probability of the point cloud point being selected is: Among them, P sample (p) represents the probability of point cloud point p being selected; ρ p represents the point cloud density at point p; e * represents the exponential function; D p represents the minimum distance between the point cloud point p and the feature point of the 3D scene; σ represents the adjustment parameter; σ p The Gaussian scale representing the 3D scene feature point with the smallest distance to the point cloud point p; Based on the probability of point cloud points being selected, the initial point cloud features are selected, and the point cloud feature vector of each initial point cloud feature point is constructed; Determine a three-dimensional feature vector of the three-dimensional scene feature point; Calculating a feature distance between the point cloud feature vector and the three-dimensional feature vector, determining a point cloud feature that matches the three-dimensional scene feature, and obtaining a feature pair set; Based on the set of feature pairs, determining a transformation matrix; Based on the transformation matrix, the point cloud image is mapped onto the scene image to obtain the first fused image.

4. The method for collaborative deployment of mobile storage and charging robots based on specific scenarios according to claim 1 is characterized in that: Determine scenario requirements, including: Based on the scenario planning, predict the maximum number of new energy vehicles parked during the activity; the maximum number of parking vehicles refers to the maximum number of new energy vehicles parked in the parking lot during the activity; Calculate the minimum entrance distance between the charging pile and the parking lot entrance and the minimum exit distance between the charging pile and the parking lot exit; the minimum entrance distance refers to the shortest distance between the charging pile and the parking lot entrance; the minimum exit distance refers to the shortest distance between the charging pile and the parking lot exit; Determine the number and location of charging piles based on the entrance distance, the exit distance and the maximum parking number; Based on the activity duration, the peak period and the charging demand distribution, a power demand of the charging pile is determined.

5. The method for collaboratively deploying mobile storage and charging robots based on specific scenarios according to claim 4 is characterized in that: Determining the location of the charging station includes: Based on the sum of the minimum entrance distance and the minimum exit distance, multiple charging piles are selected as initial cluster centers; the number of the initial cluster centers is the maximum parking number; the distance of each initial cluster center is greater than the service distance of the mobile storage and charging robot; the service distance refers to the farthest moving distance of the mobile storage and charging robot; Based on the initial cluster center, the charging piles in the parking lot are clustered to obtain a plurality of initial clusters; the distance between the charging piles in each group of the initial clusters is less than the service distance; For each of the initial clusters, respectively calculate the average position of all charging piles in the cluster, and use the average position as a new cluster center; Based on the new cluster center, the charging piles in the parking lot are clustered again to obtain a new cluster; Repeat the clustering update process until the clustering is stable or the maximum number of iterations is reached to obtain the clustering result; Determine the score of each cluster based on the entrance distance and exit distance of the charging pile in each cluster in the clustering result; the entrance distance refers to the distance between the charging pile and multiple entrances of the parking lot; the exit distance refers to the distance between the charging pile and multiple exits of the parking lot; Based on the score of each cluster, the clusters in the cluster result are sorted to obtain a sorting result; the sorting result is used to indicate the convenience of the parking space at the charging pile to enter and exit the parking lot; Based on the sorting result, charging piles in one or more clusters are selected as charging piles required by the scenario, and the locations of the charging piles are obtained.

6. The method for collaborative deployment of mobile storage and charging robots based on specific scenarios according to claim 5 is characterized in that: The average value of the charging pile scores in each cluster is taken as the score of each cluster. The calculation formula of the charging pile score is: Among them, S core represents the score; im represents the entrance variable; IM represents the total number of parking lot entrances; P im represents the probability of new energy vehicles entering from entrance im; e * represents an exponential function; λ represents the attenuation coefficient related to the parking lot size; d im，ch represents the distance between the entrance im and the charging pile ch; ch represents the charging pile variable; ex represents the exit variable; EX represents the total number of parking lot exits; P ex represents the probability of new energy vehicles leaving from exit ex; d ex，ch Indicates the distance between the exit ex and the charging station ch.

7. The method for collaborative deployment of mobile storage and charging robots based on specific scenarios according to claim 1 is characterized in that: The deployment plan of the mobile storage and charging robot is collaboratively planned through genetic algorithms, including: Using the number of the mobile storage and charging robots as the chromosome length and the designated positions as genes, the population is initialized to obtain a plurality of first arrangement schemes; Based on the fitness function, calculating the fitness value of each first arrangement scheme; Based on the fitness value, multiple second arrangement schemes are screened by roulette; Performing a crossover mutation operation on the second arrangement scheme to obtain a third arrangement scheme; The third arrangement scheme is used as a new first arrangement scheme, and the screening process and the crossover mutation process are repeated until a stop condition is reached; The second arrangement scheme that reaches the stopping condition is used as the arrangement scheme.

8. The method for collaboratively deploying mobile storage and charging robots based on specific scenarios according to claim 7 is characterized in that: The calculation formula of the fitness function is: In the above figure, Fi represents the fitness function, the lower the value, the higher the fitness; C represents the adjustment parameter; P p represents the predicted peak power demand of electric energy; T p represents the predicted duration of the peak power; v represents the moving speed of the mobile storage and charging robot; Indicates the minimum average value of the distance between the charging pile and the designated location of the mobile charging robot; E b Indicates the capacity of the battery; T s It represents the time required for the mobile charging robot to replace batteries; M represents the number of battery packs carried by the robot; and T represents the duration of the activity.

9. The method for collaborative deployment of mobile storage and charging robots based on specific scenarios according to claim 1, characterized in that: Guiding the mobile storage and charging robot to reach a designated location, including: Determining an initial map of the target scene and an initial position and pose of the mobile storage robot; Collecting environmental data through sensors arranged on the mobile storage robot; Execute the SLAM algorithm, update the initial map and the initial pose, and obtain the current map and the current pose; Based on the current map and the current position, calculating the optimal path for the mobile storage robot to reach the designated location from the current location through a path planning algorithm; the optimal path includes multiple mobile locations; Based on the optimal path, controlling the mobile storage robot to reach the next moving position and obtaining a new current position; Determine whether the distance between the new current position and the designated position is less than a preset distance threshold; if so, determine that the mobile storage robot has reached the designated position and stop iteration; if not, repeat the operation of updating the current position based on the current map and the current posture.

10. The method for collaboratively deploying mobile storage and charging robots based on specific scenarios according to claim 9, characterized in that: The initial map is an initial scene model, and the initial scene model is a modeling of the target scene obtained by fusing a scene image, a point cloud image, and a planning image.

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