Path optimization methods, devices and equipment for intelligent handling robots for power materials

Abstract

This application discloses a path optimization method, apparatus, and equipment for an intelligent handling robot for power materials. The method includes: acquiring a two-dimensional grid map containing information about the passage area of ​​the handling robot in a power warehouse, and identifying linear facilities on the two-dimensional grid map; when linear facilities are identified, generating a direction cost function based on the orthogonal direction that allows the handling robot to pass perpendicularly through the linear facility; based on the direction cost function, analyzing the cost of the handling robot passing through the linear facility from different directions, where the cost represents the time cost, overturning cost, and distance cost incurred by the handling robot when passing through the linear facility from different angles; and generating a planned path composed of continuous coordinate points in the two-dimensional grid map with the goal of minimizing the cumulative cost from the starting point to the ending point of the handling robot. That is, the planned path simultaneously considers efficiency and safety, thereby improving the stability of the handling robot during handling operations.

Description

Technical Field

[0001] This application relates to the field of path optimization technology, and in particular to path optimization methods, devices and equipment for intelligent handling robots for power materials. Background Technology

[0002] The power equipment storage center houses a large number of heavy and high-value power equipment, such as high-voltage switchgear, transformer components, and cable reels. To improve the automation level and operational safety of warehousing and logistics, handling robots with autonomous navigation capabilities have been widely introduced into power equipment warehouses, achieving efficient and automated material flow through path optimization algorithms.

[0003] Currently, the optimization of handling robot paths typically involves scanning the warehouse environment with LiDAR to construct a two-dimensional grid map containing static obstacles such as shelves and walls. Free-roaming areas in the grid map are set to low cost, while obstacle areas are set to infinite cost. After issuing a handling command to the robot, starting from the robot's current coordinates and ending at the target storage location, a series of adjacent free grids are searched in the grid map. By accumulating the cost of each grid, a geometric path with the minimum cumulative cost from the start to the end is calculated, and the handling robot is then controlled to perform the handling task based on this geometric path. However, power warehouse floors often have linear facilities such as cable trench covers for laying cables, and there are slight height differences or seams between these facilities and the ground. Common methods treat these linear facilities as traversable obstacles or flat ground. In pursuit of the shortest path, paths are easily planned that diagonally traverse these linear facilities, causing the robot's center of gravity to shift. This results in the handling robot violently shaking the transported goods when passing through these linear facilities, thus reducing the robot's stability during handling operations.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide a path optimization method, device, and equipment for an intelligent handling robot for power materials, aiming to solve the technical problem that the materials being handled by the handling robot shake violently when passing through linear facilities, thereby reducing the stability of the handling robot when performing handling operations.

[0006] To achieve the above objectives, this application proposes a path optimization method for an intelligent handling robot for electrical materials, the method comprising:

[0007] A two-dimensional grid map containing information on the access areas of handling robots in a power warehouse is obtained, and linear facility identification is performed on the two-dimensional grid map;

[0008] When the linear facility is identified, a direction cost function is generated based on the orthogonal direction through which the transport robot passes perpendicularly through the linear facility;

[0009] Based on the directional cost function, the cost of the transport robot crossing the linear facility from different directions is analyzed. The cost represents the time cost, overturning cost, and distance cost that the transport robot needs to pay when crossing the linear facility from different angles.

[0010] In the two-dimensional grid map, the optimization objective is to minimize the cumulative cost from the starting point to the ending point of the transport robot, and a planned path composed of continuous coordinate points is generated.

[0011] In one embodiment, the step of generating a direction cost function based on an orthogonal direction through which the transport robot passes perpendicularly through the linear facility when the linear facility is identified includes:

[0012] When the linear facility is identified, a cost calculation function is constructed based on the orthogonal direction through which the transport robot passes perpendicularly through the linear facility, and the current center of gravity height and chassis width of the transport robot are obtained.

[0013] The ratio of the center of gravity height to the chassis width is used as the current center of gravity coefficient of the handling robot;

[0014] Based on the centroid coefficient, the gain of the cost calculation function is adjusted for different crossing directions to generate a directional cost function.

[0015] In one embodiment, the step of adjusting the gain of the cost calculation function for different crossing directions based on the centroid coefficient to generate a directional cost function includes:

[0016] Determine the ratio of the centroid coefficient to the preset safety angle coefficient to obtain the dynamic safety angle threshold, and determine the absolute value of the angle difference between any passing direction and the orthogonal direction;

[0017] Compare the dynamic safety angle threshold with the absolute value of the angle difference;

[0018] If the absolute value of the angle difference is less than the dynamic safety angle threshold, then the centroid coefficient is used as a gain factor in the cost calculation function to generate a direction cost function.

[0019] If the absolute value of the angle difference is greater than or equal to the dynamic safety angle threshold, then the centroid coefficient and the boundary penalty term are applied as a gain factor to the cost calculation function to generate a direction cost function, wherein the boundary penalty term is the difference between the absolute value of the angle difference and the dynamic safety angle threshold.

[0020] In one embodiment, prior to the step of analyzing the cost of the transport robot traversing the linear facility from different directions based on the directional cost function, the method further includes:

[0021] Obtain the minimum turning radius of the transport robot, and extract the edge contour grid and linear obstacle area of ​​the linear facility in the two-dimensional grid map;

[0022] The linear obstacle area, and the passage area around the edge contour grid that does not exceed the minimum turning radius, are defined as the risk cost area;

[0023] The step of analyzing the cost of the transport robot traversing the linear facility from different directions based on the directional cost function further includes:

[0024] The directional cost function is mapped to the corresponding grid in the risk cost region to determine the cost of the transport robot passing through the linear facility from different directions.

[0025] In one embodiment, the step of defining the linear obstacle region and the passage area surrounding the edge contour grid that does not exceed the minimum turning radius as the risk cost region includes:

[0026] Using the edge contour grid as a reference, determine the Euclidean distance between each grid in the passage area of ​​the two-dimensional raster map and the edge contour grid;

[0027] From the grids in the passage area, select target grids whose Euclidean distance is less than or equal to the minimum turning radius, and mark them as attitude adjustment grids;

[0028] The union of the attitude adjustment grid and the grid within the linear obstacle region is used as the risk cost region representing the linear facility in the two-dimensional grid map.

[0029] In one embodiment, after the step of defining the linear obstacle region and the passage area around the edge contour grid that does not exceed the minimum turning radius as the risk cost region, the method further includes:

[0030] Establish a local speed-limiting layer aligned with the coordinates of the two-dimensional grid map, and obtain the shock absorption parameters of the handling robot;

[0031] Mark the forced deceleration zone corresponding to the risk cost area in the local speed limit layer;

[0032] Based on the vibration damping parameters, the maximum speed threshold of the transport robot in the forced deceleration zone is determined, so as to control the speed of the transport robot in the forced deceleration zone to be less than or equal to the maximum speed threshold.

[0033] In one embodiment, before the step of determining the maximum speed threshold of the transport robot in the forced deceleration zone based on the shock absorption parameters, the method further includes:

[0034] Obtain the current direction of travel of the transport robot, and determine the absolute value of the angle difference between the current direction of travel and the orthogonal direction;

[0035] The step of determining the maximum speed threshold of the transport robot in the forced deceleration zone based on the vibration damping parameters further includes:

[0036] Based on the vibration damping parameters and the absolute value of the angle, the maximum speed threshold of the transport robot in the forced deceleration zone is determined from a preset speed mapping relationship. The speed mapping relationship satisfies the following condition: when the absolute value of the angle difference increases, the maximum speed threshold decreases; when the vibration damping capability characterized by the vibration damping parameters weakens, the maximum speed threshold also decreases.

[0037] In one embodiment, the step of generating a direction cost function based on an orthogonal direction through which the transport robot passes perpendicularly through the linear facility when the linear facility is identified further includes:

[0038] When the linear facility is identified, it is semantically classified to determine the type of the linear facility, which includes at least one of cable trench cover, metal joint or rubber speed bump;

[0039] Based on the preset parameter set corresponding to the type, and the orthogonal direction that makes the transport robot pass perpendicularly through the linear facility, a direction cost function is generated. The preset parameter set includes at least one of the following: basic cost value, safety angle coefficient, or boundary violation penalty intensity.

[0040] Furthermore, to achieve the above objectives, this application also proposes a path optimization device for an intelligent power material handling robot, the path optimization device comprising:

[0041] The acquisition module is used to acquire a two-dimensional grid map containing information on the passage area of ​​the handling robots in the power warehouse, and to perform linear facility identification on the two-dimensional grid map.

[0042] The generation module is used to generate a direction cost function based on the orthogonal direction that causes the transport robot to pass perpendicularly through the linear facility when the linear facility is identified;

[0043] The analysis module is used to analyze the cost value of the transport robot when it passes through the linear facility from different directions based on the directional cost function. The cost value represents the time cost, overturning cost and distance cost that the transport robot needs to pay when it passes through the linear facility from different angles.

[0044] The planning module is used to generate a planned path composed of continuous coordinate points in the two-dimensional grid map, with the goal of minimizing the cumulative cost from the starting point to the ending point of the transport robot.

[0045] In addition, to achieve the above objectives, this application also proposes a path optimization device for an intelligent power material handling robot. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the path optimization method for the intelligent power material handling robot as described above.

[0046] One or more technical solutions proposed in this application have at least the following technical effects:

[0047] By acquiring a two-dimensional grid map containing information about the passage area and identifying linear structures within the grid map, the transport robot can perceive the presence of special structures such as cable trench covers, rather than vaguely classifying them as ordinary free areas. Since the left and right wheels rise or fall almost simultaneously when the robot travels perpendicular to the linear structure, the pitch angle changes, but the roll angle changes very little, thus avoiding lateral tipping moments. Therefore, when identifying linear structures, a directional cost function is generated based on the orthogonal direction that allows the transport robot to pass perpendicularly through the linear structure, assigning differentiated costs to different crossing angles. This directional cost function is then used to... This application uses a function to analyze the time, rollover, and distance costs incurred by a transport robot when traversing linear facilities from different directions. This quantifies directional constraints into optimization values ​​that can participate in path search, making the planned path more inclined to choose perpendicular crossings when crossing linear facilities. Finally, in a two-dimensional grid map, the optimization objective is to minimize the cumulative cost from the start to the end point, generating a planned path composed of continuous coordinate points. This ensures that the planned path, while considering global efficiency, automatically favors perpendicular crossings of linear facilities, reducing the likelihood of the transport robot rolling over and thus improving the stability of the robot during transport operations. In other words, this application determines the cost of the transport robot traversing linear facilities from different angles by generating a directional cost function, and based on this cost, prioritizes orthogonal directions that maintain vehicle stability, avoiding center-of-gravity disturbances and material swaying caused by oblique crossings of linear facilities, thereby improving the stability of the transport robot during transport operations. Attached Figure Description

[0048] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A flowchart illustrating the path optimization method for the intelligent handling robot for power materials in this application;

[0051] Figure 2 A flowchart illustrating the path optimization method for the intelligent handling robot for power materials in this application, as provided in Embodiment 2.

[0052] Figure 3 A flowchart illustrating the path optimization method for the intelligent handling robot for power materials in this application (Example 3).

[0053] Figure 4 This is a schematic diagram of the module structure of the path optimization device for the intelligent handling robot for power materials in an embodiment of this application;

[0054] Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the path optimization method of the intelligent handling robot for power materials in the embodiments of this application.

[0055] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0056] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0057] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific embodiments.

[0058] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a power warehouse management platform. The following description uses a power warehouse management platform as an example to illustrate this embodiment and the subsequent embodiments.

[0059] Based on this, embodiments of this application provide a path optimization method for an intelligent handling robot for power materials, referring to... Figure 1 , Figure 1 This is a flowchart illustrating an embodiment of the path optimization method for the intelligent handling robot for power materials in this application.

[0060] In this embodiment, the path optimization method of the intelligent power material handling robot includes steps S10~S40:

[0061] Step S10: Obtain a two-dimensional grid map containing information on the passage area of ​​the handling robots in the power warehouse, and perform linear facility identification on the two-dimensional grid map;

[0062] It should be noted that a power warehouse is a specialized storage facility used for storing, transferring, or maintaining power system equipment. A handling robot is a mobile robot with autonomous navigation and carrying capabilities, used to automatically transport materials or equipment within a power warehouse. A two-dimensional grid map is a digital map representation method that discretizes the environmental space into regular grid cells. Each grid cell represents a corresponding physical area on the warehouse floor, and the grid's numerical state indicates whether the area is accessible free space, impassable obstacles, or an unknown area. Linear facilities are elongated, continuous, and directional structured obstacles or functional areas in a two-dimensional grid map, with their length significantly greater than their width, such as cable trenches, conveyor belts, tracks, firebreaks, or maintenance access roads.

[0063] In the specific implementation, based on edge detection and probabilistic Hough transform of image processing technology, the two-dimensional grid map is first regarded as a grayscale image, and the edge contours of all obstacles in the map are extracted using the Canny edge detection operator. Then, the probabilistic Hough transform is applied to the extracted edge image. Since the probabilistic Hough transform algorithm can effectively detect straight line segments in the image, the length and continuity of the detected straight line segments can be analyzed, and the set of straight line segments with a length exceeding a certain pixel threshold and a width within a certain range can be marked as long strip obstacle regions (the representation of linear facilities in the two-dimensional grid map) to quickly identify long strip obstacle regions in the two-dimensional grid map.

[0064] Alternatively, for slightly curved or irregularly shaped long and narrow obstacle regions, geometric feature extraction based on clustering and principal component analysis can be used. First, the raster map is traversed, and density-based clustering algorithms or connected component labeling algorithms are used to cluster adjacent obstacle raster cells into independent obstacle objects. Then, for each obstacle object, the covariance matrix of the coordinates of all its constituent raster cells is calculated. Next, eigenvalue decomposition is performed on the covariance matrix to obtain eigenvalues ​​and corresponding eigenvectors, where the direction of the eigenvector corresponding to the largest eigenvalue is the main extension direction of the obstacle. Then, the ratio of the largest eigenvalue to the smallest eigenvalue is calculated. If this ratio is greater than a preset aspect ratio threshold, the clustered object is determined to be a long and narrow obstacle region with extension characteristics.

[0065] Step S20: When the linear facility is identified, a direction cost function is generated based on the orthogonal direction through which the transport robot passes perpendicularly through the linear facility;

[0066] It should be noted that the orthogonal direction is perpendicular to the extension direction of the linear facility. When the handling robot traverses the linear facility along this direction, its body axis forms a 90-degree angle with the linear facility, which helps maintain center of gravity stability, reduces the risk of tipping over, and simplifies attitude adjustment. The direction cost function is a mathematical function used to quantify the risks or energy consumption incurred by the handling robot traversing the linear facility at a specific angle.

[0067] Understandably, by determining orthogonal directions, the probability of the robot's two (or more) wheels simultaneously contacting the edge of an obstacle can be maximized, or the time difference between the two wheels contacting the obstacle can be minimized. When the robot travels perpendicular to a speed bump or cable groove, the left and right wheels rise or fall almost simultaneously. Although the pitch angle of the robot body changes, the change in roll angle is minimal, thus avoiding the generation of lateral tipping moments.

[0068] Understandably, by establishing a directional cost function with orthogonal crossing as the optimal direction, the physical stability requirements of the handling robot can be quantified into a specific numerical cost that the path search algorithm can handle. Since the center of gravity projection of the handling robot is prone to deviating from the support surface when crossing narrow structures at an angle (such as the edge of a cable trench), there is a risk of tipping over. However, vertical crossing can maintain the stability of the robot to the greatest extent. Therefore, encoding this physical law into a mathematical function (directional cost function) can enable the power warehouse management platform to understand the risk differences of different crossing postures when planning paths. This allows it to prioritize the solution that better conforms to the dynamic safety criteria among multiple feasible paths, thereby avoiding operational accidents caused by ignoring the crossing posture.

[0069] Step S30: Based on the directional cost function, analyze the cost value of the transport robot when it passes through the linear facility from different directions. The cost value represents the time cost, overturning cost, and distance cost that the transport robot needs to pay when it passes through the linear facility from different angles.

[0070] It should be noted that the cost represents the price (including time cost, rollover cost, and distance cost) that a transport robot must pay when traversing a linear facility from different angles. The resistance value is related to the deviation between the transport robot's direction of travel and its ideal orthogonal direction.

[0071] Understandably, by analyzing the cost of crossing different directions, the theoretical advantages of the directional cost function can be transformed into a basis for actual path selection. Furthermore, during the path search process, the actual directional angle of each candidate path crossing linear facilities can be dynamically evaluated, and the corresponding cost can be calculated to directly reflect the safety margin of the crossing behavior. Therefore, even if a path is slightly shorter in geometric distance, if its crossing direction deviates too much from the orthogonal direction, resulting in an excessively high cost, the path will be automatically avoided by the algorithm. This ensures that the final generated path not only meets the accessibility requirements but also has optimal attitude safety at key crossing points, thereby achieving synergistic optimization of safety and efficiency in complex warehouse environments.

[0072] Step S40: In the two-dimensional grid map, with the goal of minimizing the cumulative cost from the starting point to the ending point of the transport robot, a planned path composed of continuous coordinate points is generated.

[0073] It should be noted that path optimization is the sum of the costs associated with all grid cells along the planned path (especially those traversing linear facility areas) from the starting point to the end point; that is, the goal of path optimization is to find the feasible path that minimizes this cumulative cost. The planned path is used to guide the transport robot to move safely and efficiently from the starting point to the end point.

[0074] Understandably, generating a planned path with the goal of minimizing cumulative cost can seamlessly integrate safety constraints (including the orthogonal crossing preference of linear facilities) into the global path planning decision framework. Furthermore, this cumulative cost not only includes traditional costs such as path length and obstacle avoidance distance, but also incorporates the attitude safety cost reflected by the direction cost function, thus forming a multi-dimensional comprehensive evaluation system. Under this system, the planned path is the safest and most efficient path in a global sense, not necessarily the optimal path at the geometric level. Therefore, when the handling robot executes its task according to this path, it can effectively complete material transfer and automatically adopt the most stable attitude when traversing high-risk linear areas, thereby reducing the risk of rollover and equipment damage during operation and improving the stability of the handling robot during handling operations.

[0075] This embodiment provides a path optimization method for an intelligent handling robot for power materials. By acquiring a two-dimensional grid map containing information about the passage area and identifying linear facilities on the two-dimensional grid map, the handling robot can perceive the presence of special structures such as cable trench covers, rather than vaguely classifying them as ordinary free areas. Since the left and right wheels rise or fall almost simultaneously when the robot travels perpendicular to the linear facility, although the pitch angle changes, the roll angle changes very little, thus avoiding the generation of lateral overturning moments. Therefore, when identifying linear facilities, a direction cost function is generated based on the orthogonal direction that allows the handling robot to pass perpendicularly through the linear facility. Differentiated cost values ​​are assigned to different crossing angles. Based on this directional cost function, the cost value of the transport robot crossing linear facilities from different directions is analyzed. This quantifies the directional constraints into optimization values ​​that can participate in path search, making the planned path more inclined to choose perpendicular crossings when crossing linear facilities. Finally, in a two-dimensional grid map, the optimization objective is to minimize the cumulative cost value from the start point to the end point, generating a planned path composed of continuous coordinate points. This allows the planned path to automatically favor perpendicular crossings of linear facilities while considering global efficiency, reducing the possibility of the transport robot tipping over when crossing linear facilities, thereby improving the stability of the transport robot during transport operations. In other words, this application determines the cost value of the transport robot crossing linear facilities from different angles by generating a directional cost function, and based on this cost value, prioritizes orthogonal directions that maintain vehicle stability, avoiding center of gravity disturbances and material swaying caused by oblique crossings of linear facilities, thereby improving the stability of the transport robot during transport operations.

[0076] Based on Embodiment 1 of this application, in Embodiment 2 of this application, the content that is the same as or similar to that in Embodiment 1 can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S20 also includes steps S01 to S03:

[0077] Step S01: When the linear facility is identified, a cost calculation function is constructed based on the orthogonal direction through which the transport robot passes perpendicularly through the linear facility, and the current center of gravity height and chassis width of the transport robot are obtained.

[0078] Step S02: The ratio of the center of gravity height to the chassis width is used as the current center of gravity coefficient of the handling robot;

[0079] Step S03: Based on the centroid coefficient, adjust the gain of the cost calculation function for different crossing directions to generate a directional cost function.

[0080] It should be noted that the cost calculation function is a monotonically increasing function. That is, the smaller the angle between the input angle (the planned orientation of the handling robot) and the normal vector (orthogonal direction), the closer the output resistance value of the function is to the basic passage cost value; conversely, as the angle increases (i.e. the deviation from the orthogonal direction is more serious), the output resistance value increases exponentially or significantly.

[0081] It should be noted that the basic passage cost represents the fundamental path cost (such as distance or time cost) that must be incurred even when traversing at the optimal angle. The shape control parameters, on the other hand, are used to adjust the rate at which the drag value increases with angular deviation, determining the algorithm's sensitivity to angular deviation.

[0082] Understandably, by establishing a cost calculation function, an anisotropic cost field can be constructed in the path search space, guiding the algorithm to automatically avoid high-risk diagonal crossing paths.

[0083] It should be noted that the center of gravity height refers to the vertical height of the robot's center of gravity relative to the ground, as well as the center of gravity of the goods being transported. The chassis width is the lateral distance between the robot's left and right drive wheels or support wheels. The center of gravity coefficient characterizes its stability margin during lateral movement; a larger coefficient indicates a taller and slimmer robot. Gain adjustment is an operation that scales or non-linearly corrects the output of the cost calculation function based on the center of gravity coefficient, allowing robots with different physical characteristics to exhibit differentiated safety preferences in path planning.

[0084] Understandably, by constructing a cost calculation function with orthogonal crossing as the optimal goal, and combining the real-time center of gravity height and chassis width of the handling robot to generate a center of gravity coefficient, the cost can be adjusted to dynamically integrate the physical stability characteristics of the handling robot into the cost system of path planning. However, since different load states or different models of robots have different anti-tipping capabilities, if a uniform crossing cost standard is adopted, it may lead to high-center-of-gravity robots being planned with unsafe diagonal crossing paths, while low-center-of-gravity robots may be overly conservatively restricted. Therefore, by using the center of gravity coefficient to achieve dynamic cost adjustment according to the actual situation, the path planning can ensure the operational safety of high-risk robots without causing unnecessary efficiency losses to stable models.

[0085] In practical implementation, real-time estimation technology based on multi-sensor fusion can be used. When the robot starts or performs minor maneuvers, the power warehouse management platform can use the angular acceleration data collected by the inertial measurement unit and the torque data fed back by the motor, combined with the dynamic model, and use the extended Kalman filter algorithm to estimate the total center of gravity height of the handling robot in real time. Since the chassis width is stored as a known constant in the system parameters, the power warehouse management platform can divide the estimated center of gravity height by the chassis width in real time to obtain the dynamic high center of gravity coefficient. Then, the high center of gravity coefficient is used as the gain factor and assigned to the drag value calculation function to obtain the direction cost function.

[0086] Optionally, based on the lookup calculation technology of the material database, a detailed database of power materials can be pre-established in the power warehouse management platform, containing the three-dimensional dimensions, weight, and center of gravity position information of each material. After the robot receives the handling instruction and confirms that it will grab a specific material, the power warehouse management platform reads the center of gravity parameters of the material, combines them with the center of gravity parameters of the handling robot body, calculates the height of the composite center of gravity using the torque balance principle, and then directly uses the ratio of the composite height to the chassis width to calculate the high center of gravity coefficient. The high center of gravity coefficient is then used as the gain ratio and assigned to the drag value calculation function to obtain the directional cost function.

[0087] Optionally, step S20 further includes:

[0088] When the linear facility is identified, it is semantically classified to determine the type of the linear facility, which includes at least one of cable trench cover, metal joint or rubber speed bump;

[0089] Based on the preset parameter set corresponding to the type, and the orthogonal direction that makes the transport robot pass perpendicularly through the linear facility, a direction cost function is generated. The preset parameter set includes at least one of the following: basic cost value, safety angle coefficient, or boundary violation penalty intensity.

[0090] It should be noted that semantic classification is a process of identifying and categorizing the function or physical type of linear facilities based on their geometric features, material properties, elevation changes, or prior knowledge. Cable trench covers are detachable plate-like structures that cover power cable trenches. Metal joints are metal strips or gaps formed in warehouse floors due to construction segmentation or equipment installation; their hard surfaces may be accompanied by millimeter-level steps, generating high-frequency impacts on robot chassis. Rubber speed bumps are flexible raised structures installed at specific locations in passageways to force vehicle deceleration. The preset parameter set is a pre-configured set of control parameters for each type of linear facility, used to customize the behavioral characteristics of the directional cost function. The base cost value is the minimum output value of the directional cost function when orthogonally crossing (angle difference of 0), reflecting the inherent crossing risk baseline of this type of linear facility. The safety angle coefficient is a proportional constant used to calculate the dynamic safety angle threshold; different types of linear facilities have different safety angle coefficients due to differences in edge steepness or impact characteristics. The boundary penalty intensity is an additional penalty gain factor applied to the cost value when the crossing angle exceeds the safety angle threshold.

[0091] Understandably, cable trench covers, metal joints, and rubber speed bumps differ significantly in their physical characteristics (height, hardness, edge steepness) and their impact mechanisms on robots. If a unified cost model is used, it will be difficult to take into account the safety requirements of various facilities. For example, applying too strict constraints to soft speed bumps will reduce efficiency, while insufficient constraints on rigid metal joints can easily cause equipment damage. Therefore, by semantically classifying the identified linear facilities and generating directional cost functions based on the corresponding types and loading differentiated preset parameter sets, refined path safety control can be achieved.

[0092] Understandably, by using a semantically driven parameter adaptation mechanism, the directional cost function can acquire scene awareness capabilities. This allows for strong constraints on high-risk facilities (such as high speed bumps) while maintaining appropriate leniency for low-risk facilities (such as flat cover plates). Furthermore, by working in conjunction with the robot's own center of gravity coefficient, a two-dimensional safety assessment system based on both the environment type and the robot's state is formed. This improves the environmental adaptability, risk differentiation, and task execution reliability of path planning in complex power warehouse environments.

[0093] Furthermore, step S03 also includes:

[0094] Determine the ratio of the centroid coefficient to the preset safety angle coefficient to obtain the dynamic safety angle threshold, and determine the absolute value of the angle difference between any passing direction and the orthogonal direction;

[0095] Compare the dynamic safety angle threshold with the absolute value of the angle difference;

[0096] If the absolute value of the angle difference is less than the dynamic safety angle threshold, then the centroid coefficient is used as a gain factor in the cost calculation function to generate a direction cost function.

[0097] If the absolute value of the angle difference is greater than or equal to the dynamic safety angle threshold, then the centroid coefficient and the boundary penalty term are applied as a gain factor to the cost calculation function to generate a direction cost function, wherein the boundary penalty term is the difference between the absolute value of the angle difference and the dynamic safety angle threshold.

[0098] It should be noted that the safety angle coefficient is calibrated based on the parameters of typical handling robots and warehouse environment experience to reflect the basic tolerance of the handling robot for the safety of its crossing posture. The dynamic safety angle threshold is the maximum permissible crossing angle deviation dynamically calculated based on the ratio of the current center of gravity coefficient of the handling robot to the preset safety angle coefficient. The gain multiplier is a coefficient used to proportionally amplify the output result of the cost calculation function. The out-of-bounds penalty term is an additional penalty introduced when the absolute value of the angle difference exceeds the dynamic safety angle threshold.

[0099] Understandably, since the dynamic safety angle threshold is inversely proportional to the center of gravity coefficient, a high center of gravity robot can obtain a more relaxed crossing tolerance under light load or no load conditions, while automatically tightening the safety boundary under heavy load and high center of gravity conditions. This achieves adaptive adjustment of the safety boundary according to the actual situation. At the same time, the introduction of the boundary penalty term can also make the direction cost function have obvious nonlinear leap characteristics at the safety angle boundary, which can effectively prevent the selection of critical or excessive dangerous paths, thereby ensuring the operational safety of the handling robot when crossing linear facilities in complex industrial scenarios such as power warehouses.

[0100] In practical implementation, the ratio of the center of gravity coefficient to a preset reference constant can be calculated first to obtain the dynamic safety angle threshold. The physical meaning of this threshold is: for a robot at this center of gravity height, what is the maximum safe angle allowed to deviate from the orthogonal direction? That is, the higher the center of gravity, the smaller the threshold, and the lower the tolerance for angle deviation. Next, the power warehouse management platform calculates the absolute value of the angle difference between the input angle and the orthogonal direction. If the absolute value of the angle difference is within the safety threshold range, the power warehouse management platform only multiplies the center of gravity coefficient as a linear gain on the resistance value, moderately increasing the cost. Once the absolute value of the angle difference exceeds the safety threshold, the power warehouse management platform will also introduce an extra penalty term, which is proportional to or a higher power of the excess angle difference (determined according to requirements). The final directional cost function value is the product of these three (or two) factors, thus ensuring that when the handling robot is heavily loaded and has a high center of gravity, it abandons any path where even a small attempt at diagonal crossing would lead to a surge in cost, and chooses a longer, orthogonal crossing route.

[0101] Based on Embodiments 1 and 2 of this application, the same or similar content in Embodiment 3 of this application can be referred to the above description, and will not be repeated hereafter. Please refer to [the above description]. Figure 3 Before step S30, the path optimization method for the intelligent handling robot for power materials further includes steps S1 to S2:

[0102] Step S1: Obtain the minimum turning radius of the transport robot, and extract the edge contour grid and linear obstacle area of ​​the linear facility in the two-dimensional grid map;

[0103] Step S2: The linear obstacle area and the passage area around the edge contour grid that does not exceed the minimum turning radius are defined as the risk cost area.

[0104] It should be noted that the minimum turning radius is the smallest turning trajectory radius that a handling robot can achieve while maintaining continuous movement without slippage or structural interference. The specific value depends on the robot's chassis structure (such as Ackerman steering, differential steering, omnidirectional movement, etc.) and the current travel speed. The edge contour mesh is the set of all accessible adjacent grid cells that constitute the boundary of a linear facility in a 2D grid map. The linear obstacle region is the set of inaccessible grid cells identified as part of the linear facility body in the 2D grid map. The risk cost region is a specific area assigned an additional passage cost during path planning.

[0105] Understandably, by combining the minimum turning radius of the transport robot with the geometric boundary of the linear facility, a risk cost region containing the edge contour and the surrounding area with limited mobility can be constructed. This can effectively reflect the difficulty of posture adjustment caused by spatial constraints when the transport robot approaches or traverses the linear facility. If this region is ignored, a path that seems feasible but cannot actually be turned may be generated, resulting in task failure or collision. By marking geometrically feasible but kinematically infeasible areas as high-risk areas in advance, potential deadlocks or difficult operation points can be avoided at the global stage.

[0106] Understandably, the risk cost region can also provide a scope limitation for the subsequent directional cost function. That is, directional-sensitive costs are only applied within this region, avoiding unnecessary planning burdens on ordinary passage areas far from linear facilities, thus improving the efficiency and practicality of the algorithm.

[0107] Optionally, step S30 may also be:

[0108] The directional cost function is mapped to the corresponding grid in the risk cost region to determine the cost of the transport robot passing through the linear facility from different directions.

[0109] Understandably, by accurately mapping the directional cost function to the corresponding grid in the risk cost region, cost assessment can be achieved in both spatial location and traversal direction. This not only identifies where the danger lies but also determines the safest traversal posture. Setting the entire risk region to the same high cost might lead to excessive detours and sacrifice efficiency. Conversely, completely ignoring directional factors could generate geometrically feasible but posture-unstable traversal paths. Therefore, fine-grained orientation-position coupling modeling preserves reasonable path flexibility while ensuring safety. Furthermore, since the mapping only applies to the identified risk cost region, it avoids the computational overhead of orientation-sensitive calculations across the entire map, ensuring real-time performance and accuracy. Consequently, it can effectively improve the path safety, execution feasibility, and task completion efficiency of handling robots traversing linear facilities in structured environments such as power warehouses.

[0110] Furthermore, step S2 also includes:

[0111] Using the edge contour grid as a reference, determine the Euclidean distance between each grid in the passage area of ​​the two-dimensional raster map and the edge contour grid;

[0112] From the grids in the passage area, select target grids whose Euclidean distance is less than or equal to the minimum turning radius, and mark them as attitude adjustment grids;

[0113] The union of the attitude adjustment grid and the grid within the linear obstacle region is used as the risk cost region representing the linear facility in the two-dimensional grid map.

[0114] It should be noted that Euclidean distance is the geometric distance from the center point of any grid cell in the passage area to the center point of the nearest edge contour grid cell. The target grid is the passage area grid cell that satisfies the condition that the Euclidean distance ≤ the minimum turning radius. The attitude adjustment grid represents the potential limitations on the orientation adjustment of the handling robot in such areas.

[0115] Understandably, by using the edge contour mesh as a benchmark and comparing Euclidean distance with the minimum turning radius to filter the attitude adjustment mesh, and merging it with the linear obstacle area to form a risk-cost region, we can accurately characterize the high-risk areas where the handling robot is unable to make effective turns due to space constraints when approaching linear facilities. If the linear obstacle itself is considered a no-go zone and its surrounding maneuver buffer zone is ignored, the path planning may generate a geometrically feasible path that gets stuck in actual execution due to the inability to make the necessary turns. By identifying the risk-cost region, it can be avoided in the global stage. In addition, the construction of the risk-cost region is based on the handling robot's own minimum turning radius and edge contour, which has strong adaptability and can be applied to the collaborative operation of different robot models in the same warehouse environment. Therefore, the executability of the path and the success rate of the task are improved.

[0116] Optionally, after step S2, the path optimization method for the intelligent power material handling robot further includes:

[0117] Establish a local speed-limiting layer aligned with the coordinates of the two-dimensional grid map, and obtain the shock absorption parameters of the handling robot;

[0118] Mark the forced deceleration zone corresponding to the risk cost area in the local speed limit layer;

[0119] Based on the vibration damping parameters, the maximum speed threshold of the transport robot in the forced deceleration zone is determined, so as to control the speed of the transport robot in the forced deceleration zone to be less than or equal to the maximum speed threshold.

[0120] It should be noted that the local speed limit layer is an auxiliary data layer superimposed on the 2D grid map, strictly aligned with the map coordinates, to store the maximum permissible operating speed corresponding to each grid. Vibration damping parameters are physical or engineering parameters reflecting the vibration reduction capabilities of the robot's suspension system or chassis. Areas explicitly marked in the local speed limit layer as requiring speed restrictions, and the forced deceleration zone, are spatially defined and correspond to the risk-bearing area. The maximum speed threshold is the upper limit speed value allowed for the robot within the forced deceleration zone. This threshold is dynamically calculated based on the vibration damping parameters to ensure that the vibration amplitude and attitude changes are within a safe range when the robot traverses the edges of linear facilities or uneven areas.

[0121] Understandably, by establishing a local speed-limiting layer aligned with the coordinates of a two-dimensional grid map, and dynamically setting the maximum speed threshold of the forced deceleration zone in conjunction with the vibration damping parameters of the handling robot, coordinated control of path safety and operational stability is achieved. Since linear facilities (such as cable trench cover joints and track edges) are often accompanied by slight height differences or abrupt changes in rigidity, high-speed crossing can easily cause severe vibrations, which not only affect the safety of the load but may also damage precision sensors or cause positioning drift. By establishing a local speed-limiting layer aligned with the coordinates of a two-dimensional grid map, and dynamically setting the maximum speed threshold of the forced deceleration zone in conjunction with the vibration damping parameters of the handling robot, coordinated control of path safety and operational stability is achieved.

[0122] Understandably, by quantifying shock absorption capability into a speed limit and precisely binding it to risk areas in space, deceleration control has a clear physical basis and scenario specificity. Furthermore, this speed limiting mechanism, together with the direction cost function and the risk cost area, forms a three-in-one safety closed loop of path, attitude, and speed, enabling path planning to avoid high-risk crossing directions, reserving maneuver space in the cost map, and using a speed limiting layer, thereby ensuring the smooth crossing process of the transport robot.

[0123] Optionally, the step of determining the maximum speed threshold of the transport robot in the forced deceleration zone based on the shock absorption parameters may further include:

[0124] Extract the maximum vertical height value from the height attribute of the linear obstacle region;

[0125] Obtain the pre-calibrated maximum permissible vertical vibration acceleration from the chassis shock absorption parameters of the transport robot;

[0126] Based on a preset vehicle dynamics impact model, a positive correlation function relationship is established between the driving speed and the maximum vertical height value and the vertical vibration acceleration.

[0127] The maximum vertical height value and the maximum permissible vertical vibration acceleration are substituted into the positive correlation function to solve for the maximum traffic speed threshold of the forced deceleration zone.

[0128] Optionally, before the step of determining the maximum speed threshold of the handling robot in the forced deceleration zone based on the shock absorption parameters, the method further includes:

[0129] Obtain the current direction of travel of the transport robot, and determine the absolute value of the angle difference between the current direction of travel and the orthogonal direction;

[0130] The step of determining the maximum speed threshold of the transport robot in the forced deceleration zone based on the vibration damping parameters further includes:

[0131] Based on the vibration damping parameters and the absolute value of the angle, the maximum speed threshold of the transport robot in the forced deceleration zone is determined from a preset speed mapping relationship. The speed mapping relationship satisfies the following condition: when the absolute value of the angle difference increases, the maximum speed threshold decreases; when the vibration damping capability characterized by the vibration damping parameters weakens, the maximum speed threshold also decreases.

[0132] It should be noted that the current direction of travel is the actual direction of movement of the handling robot when approaching or traversing linear facilities. The speed mapping relationship is a pre-established multidimensional function or lookup table used to jointly determine the maximum speed threshold based on the shock absorption parameters and the absolute value of the angle difference.

[0133] It is understandable that by introducing the absolute value of the angle difference between the current direction of travel and the orthogonal direction, and using the absolute value of the angle difference together with the shock absorption parameters as the decision basis for the maximum speed threshold, it is possible to achieve fine control of the dynamic risks during the crossing process.

[0134] Understandably, the path planning phase guides the robot to traverse orthogonally to reduce cost, while the execution phase dynamically adjusts the speed based on the actual traversal angle to ensure safe passage even with minor deviations.

[0135] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the path optimization method of the intelligent handling robot for power materials in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0136] This application also provides a path optimization device for an intelligent handling robot for power materials; please refer to [reference needed]. Figure 4 The path optimization device for the intelligent handling robot for power materials includes:

[0137] The acquisition module 10 is used to acquire a two-dimensional grid map containing information on the passage area of ​​the handling robots in the power warehouse, and to perform linear facility identification on the two-dimensional grid map.

[0138] The generation module 20 is used to generate a direction cost function based on the orthogonal direction that causes the transport robot to pass perpendicularly through the linear facility when the linear facility is identified;

[0139] Analysis module 30 is used to analyze the cost value of the transport robot when it passes through the linear facility from different directions based on the directional cost function. The cost value represents the time cost, overturning cost and distance cost that the transport robot needs to pay when it passes through the linear facility from different angles.

[0140] The planning module 40 is used to generate a planned path composed of continuous coordinate points in the two-dimensional grid map, with the goal of minimizing the cumulative cost from the starting point to the ending point of the transport robot.

[0141] The path optimization device for the intelligent power material handling robot provided in this application adopts the path optimization method for the intelligent power material handling robot in the above embodiments, which can solve the technical problem of severe shaking of the materials being handled by the handling robot when passing through linear facilities, thereby reducing the stability of the handling robot when performing handling operations. Compared with the prior art, the beneficial effects of the path optimization device for the intelligent power material handling robot provided in this application are the same as the beneficial effects of the path optimization method for the intelligent power material handling robot provided in the above embodiments, and other technical features in the path optimization device for the intelligent power material handling robot are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0142] This application provides a path optimization device for an intelligent power material handling robot. The path optimization device for the intelligent power material handling robot includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the path optimization method for the intelligent power material handling robot in the first embodiment described above.

[0143] The following is for reference. Figure 5 This document illustrates a schematic diagram of a path optimization device suitable for implementing the intelligent power material handling robot of the embodiments of this application. The path optimization device for the intelligent power material handling robot in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The path optimization device for the intelligent handling robot for power materials shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0144] like Figure 5 As shown, the path optimization device for the intelligent power material handling robot may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the path optimization device for the intelligent power material handling robot. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the path optimization device of the intelligent power material handling robot to exchange data with other devices wirelessly or via wired communication. Although the figure shows a path optimization device for an intelligent power material handling robot with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0145] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0146] The path optimization device for the intelligent power material handling robot provided in this application, employing the path optimization method for the intelligent power material handling robot in the above embodiments, can solve the technical problem of severe shaking of the materials being handled by the handling robot when passing through linear facilities, thereby reducing the stability of the handling robot during handling operations. Compared with the prior art, the beneficial effects of the path optimization device for the intelligent power material handling robot provided in this application are the same as the beneficial effects of the path optimization method for the intelligent power material handling robot provided in the above embodiments, and other technical features in the path optimization device for the intelligent power material handling robot are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0147] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0148] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0149] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the path optimization method of the intelligent handling robot for power materials in the above embodiments.

[0150] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0151] The aforementioned computer-readable storage medium may be included in the path optimization device of the intelligent power material handling robot; or it may exist independently and not be installed in the path optimization device of the intelligent power material handling robot.

[0152] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the path optimization device of the intelligent power material handling robot, enable the path optimization device of the intelligent power material handling robot to implement the path optimization method of the intelligent power material handling robot.

[0153] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0154] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0155] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0156] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the path optimization method of the intelligent power material handling robot described above. This solves the technical problem of severe shaking of the materials being handled by the robot when passing through linear facilities, thereby reducing the robot's stability during handling operations. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the path optimization method of the intelligent power material handling robot provided in the above embodiments, and will not be repeated here.

[0157] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the path optimization method for the intelligent handling robot for power materials as described above.

[0158] The computer program product provided in this application can solve the technical problem that the materials being transported by the handling robot shake violently when passing through linear facilities, thereby reducing the stability of the handling robot during handling operations. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the path optimization method for the intelligent handling robot of power materials provided in the above embodiments, and will not be repeated here.

[0159] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.

Claims

1. A path optimization method for an intelligent handling robot for electrical materials, characterized in that, The method includes: A two-dimensional grid map containing information on the access areas of handling robots in a power warehouse is obtained, and linear facility identification is performed on the two-dimensional grid map; When the linear facility is identified, a direction cost function is generated based on the orthogonal direction through which the transport robot passes perpendicularly through the linear facility; Based on the directional cost function, the cost of the transport robot crossing the linear facility from different directions is analyzed. The cost represents the time cost, overturning cost, and distance cost that the transport robot needs to pay when crossing the linear facility from different angles. In the two-dimensional grid map, the optimization objective is to minimize the cumulative cost from the starting point to the ending point of the transport robot, and a planned path composed of continuous coordinate points is generated.

2. The method as described in claim 1, characterized in that, The step of generating a direction cost function based on the orthogonal direction through which the transport robot passes perpendicularly through the linear facility when the linear facility is identified includes: When the linear facility is identified, a cost calculation function is constructed based on the orthogonal direction through which the transport robot passes perpendicularly through the linear facility, and the current center of gravity height and chassis width of the transport robot are obtained. The ratio of the center of gravity height to the chassis width is used as the current center of gravity coefficient of the handling robot; Based on the centroid coefficient, the gain of the cost calculation function is adjusted for different crossing directions to generate a directional cost function.

3. The method as described in claim 2, characterized in that, The step of adjusting the gain of the cost calculation function for different crossing directions based on the centroid coefficient to generate a directional cost function includes: Determine the ratio of the centroid coefficient to the preset safety angle coefficient to obtain the dynamic safety angle threshold, and determine the absolute value of the angle difference between any passing direction and the orthogonal direction; Compare the dynamic safety angle threshold with the absolute value of the angle difference; If the absolute value of the angle difference is less than the dynamic safety angle threshold, then the centroid coefficient is used as a gain factor in the cost calculation function to generate a direction cost function. If the absolute value of the angle difference is greater than or equal to the dynamic safety angle threshold, then the centroid coefficient and the boundary penalty term are applied as a gain factor to the cost calculation function to generate a direction cost function, wherein the boundary penalty term is the difference between the absolute value of the angle difference and the dynamic safety angle threshold.

4. The method as described in claim 1, characterized in that, Before the step of analyzing the cost of the transport robot traversing the linear facility from different directions based on the directional cost function, the method further includes: Obtain the minimum turning radius of the transport robot, and extract the edge contour grid and linear obstacle area of ​​the linear facility in the two-dimensional grid map; The linear obstacle area, and the passage area around the edge contour grid that does not exceed the minimum turning radius, are defined as the risk cost area; The step of analyzing the cost of the transport robot traversing the linear facility from different directions based on the directional cost function further includes: The directional cost function is mapped to the corresponding grid in the risk cost region to determine the cost of the transport robot passing through the linear facility from different directions.

5. The method as described in claim 4, characterized in that, The step of defining the linear obstacle region and the passage area surrounding the edge contour grid that does not exceed the minimum turning radius as the risk cost region includes: Using the edge contour grid as a reference, determine the Euclidean distance between each grid in the passage area of ​​the two-dimensional raster map and the edge contour grid; From the grids in the passage area, select target grids whose Euclidean distance is less than or equal to the minimum turning radius, and mark them as attitude adjustment grids; The union of the attitude adjustment grid and the grid within the linear obstacle region is used as the risk cost region representing the linear facility in the two-dimensional grid map.

6. The method as described in claim 4, characterized in that, After the step of defining the linear obstacle region and the passage area around the edge contour grid that does not exceed the minimum turning radius as the risk cost region, the method further includes: Establish a local speed-limiting layer aligned with the coordinates of the two-dimensional grid map, and obtain the shock absorption parameters of the handling robot; Mark the forced deceleration zone corresponding to the risk cost area in the local speed limit layer; Based on the vibration damping parameters, the maximum speed threshold of the transport robot in the forced deceleration zone is determined, so as to control the speed of the transport robot in the forced deceleration zone to be less than or equal to the maximum speed threshold.

7. The method as described in claim 6, characterized in that, Before the step of determining the maximum speed threshold of the handling robot in the forced deceleration zone based on the vibration damping parameters, the method further includes: Obtain the current direction of travel of the transport robot, and determine the absolute value of the angle difference between the current direction of travel and the orthogonal direction; The step of determining the maximum speed threshold of the transport robot in the forced deceleration zone based on the vibration damping parameters further includes: Based on the vibration damping parameters and the absolute value of the angle, the maximum speed threshold of the transport robot in the forced deceleration zone is determined from a preset speed mapping relationship. The speed mapping relationship satisfies the following condition: when the absolute value of the angle difference increases, the maximum speed threshold decreases; when the vibration damping capability characterized by the vibration damping parameters weakens, the maximum speed threshold also decreases.

8. The method as described in claim 1, characterized in that, The step of generating a direction cost function based on the orthogonal direction through which the transport robot passes perpendicularly through the linear facility when the linear facility is identified further includes: When the linear facility is identified, it is semantically classified to determine the type of the linear facility, which includes at least one of cable trench cover, metal joint or rubber speed bump; Based on the preset parameter set corresponding to the type, and the orthogonal direction that makes the transport robot pass perpendicularly through the linear facility, a direction cost function is generated. The preset parameter set includes at least one of the following: basic cost value, safety angle coefficient, or boundary violation penalty intensity.

9. A path optimization device for an intelligent handling robot for power materials, characterized in that, The device includes: The acquisition module is used to acquire a two-dimensional grid map containing information on the passage area of ​​the handling robots in the power warehouse, and to perform linear facility identification on the two-dimensional grid map. The generation module is used to generate a direction cost function based on the orthogonal direction that causes the transport robot to pass perpendicularly through the linear facility when the linear facility is identified; The analysis module is used to analyze the cost value of the transport robot when it passes through the linear facility from different directions based on the directional cost function. The cost value represents the time cost, overturning cost and distance cost that the transport robot needs to pay when it passes through the linear facility from different angles. The planning module is used to generate a planned path composed of continuous coordinate points in the two-dimensional grid map, with the goal of minimizing the cumulative cost from the starting point to the ending point of the transport robot.

10. A path optimization device for an intelligent handling robot for electrical materials, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the path optimization method for the intelligent handling robot for power materials as described in any one of claims 1 to 6.

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