Stereoscopic warehouse access optimization method and system based on path planning

Abstract

The invention relates to a three-dimensional warehouse access optimization method and system based on path planning, and the method comprises the steps: obtaining warehouse space scanning information and material scanning information of a three-dimensional warehouse, and the warehouse space scanning information comprises a goods shelf distribution state and an obstacle distribution state; the material scanning information comprises a position identifier and an access priority identifier of a to-be-accessed material; generating a path planning state feature set according to the shelf distribution state and the obstacle distribution state, wherein the path planning state feature set comprises shelf spacing features, obstacle influence range features and channel passing state features; performing access path planning processing based on the path planning state feature set and the position identifier of the to-be-accessed material to generate an initial access path sequence, and adjusting the path node sequence of the initial access path sequence according to the access priority identifier to obtain an optimized access path sequence; and sending the optimized access path sequence to a control terminal of the transfer robot so as to control the transfer robot to execute material access operation.

Description

Technical Field

[0001] The present invention relates to the field of computer control technology, and more particularly, to an optimization method and system for three-dimensional warehouse storage and retrieval based on path planning. Background Art

[0002] With the development of industrial automation, three-dimensional warehouses are widely used to improve warehousing efficiency and space utilization rate. In traditional three-dimensional warehouse storage and retrieval operations, the acquisition of warehouse space layout and material information is often not comprehensive and accurate enough, resulting in a lack of sufficient basic data support for path planning, and the planned path may not be well adapted to the actual warehouse environment.

[0003] For example, the existing technology does not obtain the warehouse space scanning information and material scanning information in detail enough, and fails to comprehensively cover key information such as the distribution state of shelves, the distribution state of obstacles, and the access priority of materials, making it difficult to comprehensively consider various factors in subsequent path planning. Another example is that in the process of path planning, the existing technology lacks the integration and utilization of multi-dimensional features, and cannot fully combine different features to generate an efficient and reasonable access path, resulting in problems such as unreasonable paths and low efficiency when the handling robot executes tasks, and it is unable to flexibly adjust the path according to the priority of materials, affecting the overall warehousing operation efficiency and resource utilization rate. Summary of the Invention

[0004] In view of this, the present invention provides an optimization method and system for three-dimensional warehouse storage and retrieval based on path planning.

[0005] An embodiment of the present invention provides an optimization method for three-dimensional warehouse storage and retrieval based on path planning, which is applied to an optimization system for three-dimensional warehouse storage and retrieval. The method includes: obtaining the warehouse space scanning information and material scanning information of the three-dimensional warehouse, where the warehouse space scanning information includes the distribution state of shelves and the distribution state of obstacles, and the material scanning information includes the position identifier and access priority identifier of the material to be stored and retrieved; generating a path planning state feature set according to the distribution state of the shelves and the distribution state of the obstacles, where the path planning state feature set includes shelf spacing features, obstacle influence range features, and channel passage state features; performing access path planning processing based on the path planning state feature set and the position identifier of the material to be stored and retrieved to generate an initial access path sequence, and adjusting the path node order of the initial access path sequence according to the access priority identifier to obtain an optimized access path sequence; sending the optimized access path sequence to the control terminal of the handling robot to control the handling robot to perform material storage and retrieval operations.

[0006] The present invention also provides a three-dimensional warehouse storage optimization system, including: a memory for storing program instructions and data; a processor coupled to the memory to execute the instructions in the memory to implement the method as described above.

[0007] The present invention also provides a computer storage medium containing instructions that, when executed on a processor, implement the above method.

[0008] In the embodiment of the present invention, by obtaining the warehouse space scanning information and material scanning information of the three-dimensional warehouse, the warehouse layout and material conditions are comprehensively and accurately grasped, providing a solid and reliable data basis for subsequent path planning to ensure that the planned path fits the actual scenario; according to the shelf distribution state and obstacle distribution state, a path planning state feature set containing multi-dimensional features is generated, which depicts the passage conditions of the warehouse space from different angles and can consider various influencing factors more carefully and comprehensively; based on this feature set and the material position identifier, the access path is planned and the order of path nodes is adjusted, which can fully combine the access priorities of materials to generate an efficient and reasonable optimized access path sequence, greatly improving the overall efficiency of material access; sending the optimized access path sequence to the handling robot control terminal can accurately control the robot to perform material access operations, making the handling process more orderly and accurate, improving the operation efficiency and stability of the three-dimensional warehouse, and ensuring the smooth execution of the material access task.

[0009] In summary, the embodiment of the present invention effectively solves the problems of incomplete information acquisition and unreasonable path planning in the prior art by comprehensively obtaining relevant information and performing multi-dimensional feature integration and path planning optimization, improving the overall efficiency of three-dimensional warehouse storage operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present invention and, together with the specification, are used to explain the principles of the present invention.

[0011] Figure 1 It is a schematic flowchart of the steps of a three-dimensional warehouse storage optimization method based on path planning provided by an embodiment of the present invention.

[0012] Figure 2 It is a structural block diagram of a three-dimensional warehouse storage optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0013] The technical solutions in the present invention will be described below in conjunction with the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention. It should be noted that the terms "first", "second", etc. in the specification of the present invention and the above-mentioned accompanying drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0014] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of an optimization method for three-dimensional warehouse storage based on path planning provided by an embodiment of the present invention. This method is applied to an optimization system for three-dimensional warehouse storage and may further include steps 110 - 140.

[0015] Step 110: Obtain the warehouse space scan information and material scan information of the three-dimensional warehouse. The warehouse space scan information includes the shelf distribution status and the obstacle distribution status, and the material scan information includes the position identifier and the access priority identifier of the material to be accessed.

[0016] In an embodiment of the present invention, in a scenario of a three-dimensional warehouse for power batteries, it is necessary to comprehensively understand the warehouse space layout and the key information of the materials to be accessed. Among them, the warehouse space scan information provides the basic environmental data for subsequent path planning, and the material scan information clarifies the task objectives. For example, in this three-dimensional warehouse for power batteries, different types of power batteries are stored in different positions, and the access priority identifier determines which batteries need to be processed first, perhaps due to urgent needs on the production line or the need for priority turnover due to long storage times, etc.

[0017] In one example, the shelf distribution status includes a set of shelf coordinates, the obstacle distribution status includes a set of obstacle coordinates, and the position identifier includes the coordinates of the material storage position. Based on this, the obtaining of the warehouse space scan information and the material scan information of the three-dimensional warehouse includes: Step 111: Perform a three-dimensional space scan of the shelf layout of the three-dimensional warehouse through a laser scanning device to generate a set of shelf coordinates and a set of obstacle coordinates. The set of shelf coordinates includes the center point coordinates and boundary coordinates of each shelf, and the set of obstacle coordinates includes the contour coordinates of each obstacle.

[0018] In this three-dimensional battery storage warehouse, the laser scanning device methodically scans the entire warehouse space. When the laser beam emitted by the laser scanning device encounters the shelves and obstacles, it will reflect back. By precisely measuring the propagation time and angle of the laser beam, the device can determine the position information of the shelves and obstacles. For example, for a cuboid shelf with a length of L, a width of W, and a height of H, the laser scanning device can accurately measure the coordinates of its center point (X0, Y0, Z0), and at the same time, determine its boundary coordinates by measuring the boundary points of the shelf. For the obstacles in the warehouse, such as temporarily placed equipment, tool racks, etc., the laser scanning device can also obtain their contour coordinates. These coordinate information are recorded in digital form, forming a set of shelf coordinates and a set of obstacle coordinates, providing accurate spatial position data for subsequent path planning.

[0019] Step 112: Invoke the radio frequency identification device to scan the electronic tag of the material to be stored or retrieved, extract the material storage position coordinates and the material urgency level identifier, and the material urgency level identifier is used to determine the access priority identifier.

[0020] In the embodiment of the present invention, each power battery is equipped with an electronic tag. The radio frequency identification device emits a radio frequency signal. When the signal approaches the electronic tag, the electronic tag is activated and returns the stored information. In this way, the radio frequency identification device can accurately extract the material storage position coordinates of each power battery to be stored or retrieved, and clarify its specific position in the warehouse. At the same time, the electronic tag also stores the material urgency level identifier, which may be preset through factors such as production plans and order priorities. For example, some power batteries for the production of urgent orders will have a high-priority material urgency level identifier, while some power batteries for regular production reserves will have a lower priority. Through the material urgency level identifier, the system can determine the access priority identifier of each power battery, providing an important basis for subsequent path planning and task scheduling.

[0021] Step 113: Perform coordinate alignment processing on the set of shelf coordinates and the set of obstacle coordinates to obtain the warehouse space scan information, and associate and store the material storage position coordinates and the material urgency level identifier as the material scan information.

[0022] After obtaining the shelf coordinate set and the obstacle coordinate set, coordinate alignment processing is required. This is because during the scanning process of the laser scanning device, due to factors such as the accuracy of the device itself and the installation position, there may be certain deviations in the obtained coordinate data. Through a preset coordinate alignment algorithm (such as a coordinate alignment algorithm based on the least squares method, which calculates the translation, rotation, and scaling relationships between two sets of coordinates to accurately align the two sets of coordinates), the shelf coordinate set and the obstacle coordinate set are aligned to ensure that they can accurately reflect the actual layout of the warehouse space in the same coordinate system, thereby obtaining accurate warehouse space scanning information. For the coordinates of the material storage location and the identification of the material urgency level, the system associates and stores them according to a specific storage structure (such as an associated table structure in a database) to form the material scanning information. In this way, in subsequent processing, the system can conveniently locate the corresponding material storage location coordinates according to the material urgency level identification, providing convenience for path planning and task allocation.

[0023] Step 120: Generate a path planning state feature set according to the shelf distribution state and the obstacle distribution state, where the path planning state feature set includes a shelf spacing feature, an obstacle influence range feature, and a channel passage state feature.

[0024] In a power battery three-dimensional warehouse, in order to plan an efficient and safe access path, multiple factors need to be comprehensively considered, and generating a path planning state feature set is a process of quantifying and integrating these factors. The shelf spacing feature reflects the spatial distance between shelves, which has an important impact on the smooth passage of the handling robot; the obstacle influence range feature defines the area to be avoided around the obstacle to ensure that the handling robot does not collide; the channel passage state feature describes the passable situation of the preset channel area. These feature sets together provide comprehensive environmental information for path planning.

[0025] As an implementation method, the generating of the path planning state feature set according to the shelf distribution state and the obstacle distribution state includes: Step 121: Calculate the minimum spacing between adjacent shelves according to the shelf coordinate set to generate the shelf spacing feature.

[0026] In this power battery stereoscopic warehouse, based on the acquired shelf coordinate set, the system will calculate the minimum spacing between adjacent shelves. For two adjacent shelves A and B, the minimum spacing is determined by calculating the distance between their boundary coordinates. The boundary coordinates of shelf A are (X1A, Y1A, Z1A), (X2A, Y2A, Z2A), and the boundary coordinates of shelf B are (X1B, Y1B, Z1B), (X2B, Y2B, Z2B). Through the preset distance calculation algorithm (such as the Euclidean distance algorithm in three-dimensional space, calculating the straight-line distance between two points), the distance values ​​in multiple different directions are calculated, and then the minimum value is selected as the minimum spacing between adjacent shelves. The minimum spacing is an important value of the shelf spacing feature, which reflects the minimum space required for the handling robot to pass between shelves, which is of great significance for judging whether the path is feasible.

[0027] Step 122: Perform contour expansion processing on the obstacle coordinate set to determine the expansion area coordinates of each obstacle, and generate the obstacle influence range features based on the expansion area coordinates.

[0028] When dealing with the obstacle influence range, it is necessary to perform contour expansion processing on the obstacle coordinate set. Because the handling robot needs to maintain a certain safe distance from the obstacle during actual operation, the path cannot be planned only according to the actual outline of the obstacle. The contour coordinates of each obstacle are expanded through a preset contour expansion algorithm (such as an expansion algorithm based on morphological operations, which expands outward according to a certain expansion coefficient based on the obstacle contour). For example, for an irregularly shaped obstacle, its contour is expanded outward by a certain distance d through the expansion algorithm to obtain the expanded contour coordinates. The area enclosed by these coordinates is the expansion area of ​​the obstacle. Based on these expanded area coordinates, the obstacle influence range features are generated, which clarifies the areas that the handling robot needs to avoid when planning the path to ensure safe operation.

[0029] Step 123: traverse the preset channel area of ​​the stereoscopic warehouse, detect the overlapping area with the shelf coordinate set and the obstacle coordinate set in the preset channel area, and generate the channel traffic status feature according to the position and area of ​​the overlapping area.

[0030] In a power battery automated storage and retrieval system (AS / RS), the preset aisle areas are important references for the planned paths. The system traverses these preset aisle areas and compares them with the set of shelf coordinates and the set of obstacle coordinates. Through a preset overlap detection algorithm (such as an overlap detection algorithm based on coordinate ranges to determine whether there is an intersection between two areas in terms of spatial coordinates), the overlap situations between the preset aisle areas and the shelves and obstacles are detected. If there is an overlapping area, the system further calculates the position and area of this overlapping area. For example, the overlapping area may be located at a certain position in the aisle, and its area size also affects the passage capacity of the aisle. Based on this information, the aisle passage status features are generated, which can be represented by different metrics, such as the passability of the aisle (high, medium, low), the position of the area that needs to be avoided, etc., providing information on the aisle passage conditions for path planning.

[0031] Step 124: Combine the shelf spacing features, the obstacle influence range features, and the aisle passage status features into the path planning status feature set.

[0032] Integrate the calculated shelf spacing features, obstacle influence range features, and aisle passage status features. These three features describe the passage conditions of the warehouse space from different aspects. Combining them together forms a complete path planning status feature set, which can be stored in a specific data structure (such as a structure array, with each feature as a member variable of the structure) so that these feature information can be conveniently called and processed during the subsequent path planning process, providing comprehensive data support for generating safe and efficient access paths.

[0033] Step 130: Based on the path planning status feature set and the location identifier of the material to be accessed, perform access path planning processing to generate an initial access path sequence, and adjust the order of the path nodes of the initial access path sequence according to the access priority identifier to obtain an optimized access path sequence.

[0034] In a power battery automated storage and retrieval system (AS / RS), after having the path planning status feature set and the location identifier of the material to be accessed, the access path can be planned and optimized. First, an initial access path sequence is generated to plan a preliminary access path for the handling robot; then, the order of the path nodes is adjusted according to the access priority identifier to meet the access priority requirements of different materials.

[0035] In an alternative technical solution, the performing access path planning processing based on the path planning status feature set and the location identifier of the material to be accessed to generate an initial access path sequence includes: Step 131: Extract the passageway traffic status features from the path planning status feature set to generate a traffic weight map, where the nodes in the traffic weight map represent the connection points between the shelves, and the edge weights represent the passageway traffic difficulty.

[0036] In this scenario, the passageway traffic status features are extracted from the path planning status feature set. According to the information contained in the passageway traffic status features, such as the passability of the passageway and the areas that need to be avoided, a traffic weight map is generated. For each connection point between the shelves, it is used as a node in the traffic weight map; while the edge weights are determined according to the passageway traffic difficulty. For example, if there are no obstacles in a certain passageway area and the shelf spacing is large, the traffic difficulty is low, then the corresponding edge weight is set to a smaller value; on the contrary, if there are partially overlapping areas in the passageway or the shelf spacing is small, the traffic difficulty is high, and the edge weight is set to a larger value. In this way, the generated traffic weight map can intuitively reflect the traffic difficulty of different areas in the warehouse, providing a basis for subsequent path search.

[0037] Step 132: Taking the current position of the handling robot as the starting point and the shelf coordinates corresponding to the position identifier of the material to be stored or retrieved as the target point, search for the target traffic path in the traffic weight map to generate a candidate path set.

[0038] When the handling robot is performing a task, its current position is known. And the shelf coordinates corresponding to the position identifier of the material to be stored or retrieved are also clear. Taking the node corresponding to the current position of the handling robot as the starting point and the node corresponding to the target shelf coordinates as the end point, use a preset path search algorithm (such as the A* algorithm, by comprehensively considering the distance from the node to the starting point and the estimated distance from the node to the end point, search for the optimal path in the graph) to perform path search in the traffic weight map. During the search process, the algorithm will select the appropriate path branches according to the edge weights, generating multiple possible target traffic paths, and these paths form a candidate path set. The candidate path set contains multiple path options from the starting point to the target point, providing a basis for subsequent screening and optimization.

[0039] Step 133: Perform safety spacing verification on each path in the candidate path set according to the shelf spacing feature and the obstacle influence range feature, and eliminate the paths with safety conflicts to obtain the verified path set.

[0040] Optionally, for each path in the candidate path set, the system performs a safety distance verification based on the shelf spacing feature and the obstacle influence range feature. For each point on the path, it checks whether the distances from the point to the shelves and obstacles meet the safety requirements. For example, based on the shelf spacing feature, it determines whether the path will cause the handling robot to collide with the shelves during passage; based on the obstacle influence range feature, it checks whether the path will enter the expansion area of the obstacle. If there is a safety conflict, that is, the distance does not meet the safety requirements, then this path will be excluded. After such verification, a verified path set is obtained, and the paths in this set meet the safety requirements, providing a guarantee for further selecting the optimal path.

[0041] Step 134: Select the path with the shortest path length and the fewest number of turns from the verified path set as the initial access path sequence; In the verified path set, an optimal path needs to be selected as the initial access path sequence. The selection criteria are the shortest path length and the fewest number of turns. The path length can be determined by calculating the sum of the distances between the nodes on the path, and the number of turns is counted by detecting the number of changes in the path direction. By traversing each path in the verified path set, calculating its path length and the number of turns, and then comparing them. For example, for path rA and path rB, calculate their path lengths LrA and LrB, and the number of turns TrA and TrB respectively. If LrA < LrB and TrA < TrB, then path rA is a better choice. In this way, the path that meets the conditions is selected from the verified path set as the initial access path sequence, planning a relatively efficient path for the preliminary action of the handling robot.

[0042] In another alternative technical solution, adjusting the path node order of the initial access path sequence according to the access priority identifier to obtain an optimized access path sequence includes: Step 135: Parse the access priority identifier to determine the sorting of the access urgency levels of multiple materials to be accessed.

[0043] In the power battery three-dimensional warehouse, there may be multiple materials to be accessed at the same time, and each material has a corresponding access priority identifier. The system parses these access priority identifiers and determines the sorting of the access urgency levels of multiple materials to be accessed according to the preset parsing rules (such as sorting according to the numbers in the identifier, the level identifier, etc.). For example, there are three power batteries to be accessed, and their access priority identifiers are high, medium, and low respectively. By parsing, it is determined that their access urgency level sorting is that the power battery with high priority is accessed first, followed by the medium priority, and finally the low priority. This sorting provides a basis for subsequent adjustment of the path node order.

[0044] Step 136: Detect whether the initial access path sequence contains multiple material location nodes. If so, perform path insertion processing on the multiple material location nodes according to the access urgency sorting.

[0045] This step aims to check whether there are multiple material location nodes in the initial access path sequence. If there are, it means that this path needs to access multiple materials simultaneously. According to the previously determined access urgency sorting, perform path insertion processing on the multiple material location nodes. For example, the initial access path sequence originally passes through the location nodes of Material 1, Material 2, and Material 3 in sequence. However, after parsing, the access priority of Material 2 is higher than that of Material 1. Then, the system will insert the location node of Material 2 into a more appropriate position in the path in advance, so that the handling robot can access the material with a higher priority first. Through this path insertion processing, adjust the order of the path nodes to meet the access priority requirements of the materials.

[0046] Step 137: Calculate the difference between the total length of the inserted path and the original path length. If the difference exceeds the preset difference, re - allocate the access order of the material location nodes so that the difference is less than the preset difference.

[0047] After performing the path insertion processing, calculate the difference between the total length of the inserted path and the original path length. The calculation of the total path length can be obtained by accumulating the distances between each node on the path. The preset difference is a threshold set according to the actual situation, used to determine whether the change in the path length is within an acceptable range. If the difference exceeds the preset difference, it means that the change in the path length is too large and may affect the handling efficiency. At this time, the system will re - allocate the access order of the material location nodes. By trying different permutations and combinations (such as using the greedy algorithm, each time selecting the node permutation method that increases the path length the least), find a new access order so that the difference between the total length of the re - arranged path and the original path length is less than the preset difference, while meeting the access priority of the materials and trying to maintain the efficiency of the path as much as possible.

[0048] Step 138: Update the node connection relationship of the initial access path sequence according to the re - allocated access order to generate the optimized access path sequence.

[0049] After determining the new access order of the material location nodes, update the node connection relationship of the initial access path sequence according to this order. This means adjusting the connection method and order between each node on the path, so that the handling robot can pass through each material location node in sequence according to the new order. Through this update, generate the optimized access path sequence, which not only meets the access priority requirements of the materials, but also optimizes in terms of path length and efficiency, providing a more reasonable path planning for the actual operation of the handling robot.

[0050] In an exemplary design concept, updating the node connection relationship of the initial access path sequence according to the reallocated access order to generate the optimized access path sequence includes: Step 1381: Generate a priority weight value for each material location node according to the sorting of the access urgency, and the priority weight value is positively correlated with the access urgency.

[0051] Under this design concept, according to the previously determined sorting of access urgency, generate a priority weight value for each material location node. For example, a material location node with a high access urgency is given a higher priority weight value; a material location node with a low access urgency is given a lower priority weight value. These priority weight values can be generated through a preset weight assignment algorithm (such as linear assignment according to the sorting order, the earlier the sorting, the higher the weight value) to ensure that the priority weight value is positively correlated with the access urgency and provide a quantitative basis for subsequent path evaluation and adjustment.

[0052] Step 1382: Extract the three-dimensional coordinate set of all material location nodes in the initial access path sequence, and generate a node distance feature vector according to the Euclidean distance matrix of the three-dimensional coordinate set.

[0053] Extract the three-dimensional coordinate set of all material location nodes from the initial access path sequence. For each material location node, its coordinates can be expressed as (X, Y, Z). According to these coordinates, through a preset Euclidean distance calculation algorithm (in three-dimensional space, the Euclidean distance formula for calculating the distance between two points (X1, Y1, Z1) and (X2, Y2, Z2) is √[(X2 - X1)^2 + (Y2 - Y1)^2 + (Z2 - Z1)^2], that is, calculate the sum of the squares of the differences in the three dimensions of the two node coordinates and then take the square root to get the distance), generate a node distance matrix, and each element in this matrix represents the distance between two material location nodes. Then, take each row or each column in this matrix as a vector to generate a node distance feature vector, which reflects the distance relationship between material location nodes and is of great significance for subsequent path evaluation and optimization.

[0054] Step 1383: Generate a weighted path evaluation matrix based on the node distance feature vector and the priority weight value. Each element in the weighted path evaluation matrix represents the comprehensive path cost between two nodes, and the comprehensive path cost is calculated by linearly superimposing the node distance and the priority weight difference.

[0055] Combine the node distance feature vector and the priority weight value to generate a weighted path evaluation matrix. For each element in the weighted path evaluation matrix, its value is calculated by the linear superposition of the node distance and the priority weight difference. For example, for nodes i and j, the node distance is dij, and the priority weight values are wi and wj respectively. The comprehensive path cost Cij can be calculated by the formula Cij = α * dij + β * |wi - wj| (that is, the comprehensive path cost is the node distance multiplied by a weight coefficient α, plus the absolute value of the priority weight difference multiplied by another weight coefficient β. α and β are weight coefficients preset according to the actual situation). Calculate the value of each element in the weighted path evaluation matrix in this way. This matrix comprehensively considers the influence of node distance and priority weight on the path cost, providing a more accurate evaluation basis for subsequent path optimization.

[0056] Step 1384: Perform topological sorting on the material position nodes according to the weighted path evaluation matrix to generate a set of candidate node access sequences, and the topological sorting satisfies the constraint of minimizing the comprehensive path cost between nodes.

[0057] Optionally, use a preset topological sorting algorithm (such as the Kahn algorithm, which continuously finds nodes with an in-degree of 0, removes them from the graph, and updates the in-degrees of other nodes to obtain a topological sorting result that satisfies a certain order) to perform topological sorting on the material position nodes based on the weighted path evaluation matrix. In this process, the sorting algorithm aims to minimize the comprehensive path cost between nodes, tries different node permutation orders, and finds an optimal topological sorting method. For example, for a series of material position nodes, the algorithm analyzes the connection relationships and comprehensive path costs between each node from the weighted path evaluation matrix, determines which nodes should be visited first and which nodes should be visited later to minimize the comprehensive path cost of the entire path. Through this topological sorting, a set of candidate node access sequences is generated, and each access sequence in this set is obtained under the constraint of minimizing the comprehensive path cost between nodes, providing multiple feasible solutions for further screening the optimal path.

[0058] Step 1385: Traverse the set of candidate node access sequences, calculate the total path length and the number of turns corresponding to each candidate sequence, and generate a turning penalty coefficient in combination with the safety threshold of the shelf spacing feature.

[0059] Optionally, a detailed analysis is performed on each candidate order in the set of candidate node access orders. For each candidate order, calculate its corresponding total path length by accumulating the distances between each pair of nodes on the path. At the same time, count the number of turns in the path, which can be achieved by detecting changes in the path direction. Then, combine with the safety threshold of the shelf spacing feature to generate a turning penalty coefficient. For example, if the shelf spacing is small, turning in the path planning may pose a greater risk, and at this time, the turning penalty coefficient will be set higher; conversely, if the shelf spacing is large, the impact of turning on safety and efficiency is relatively small, and the turning penalty coefficient will be set lower. The turning penalty coefficient can be determined by a preset coefficient generation algorithm (such as determining the coefficient size according to the ratio of the shelf spacing to the preset standard spacing), and it will be used for subsequent weighted correction of the total path length to more comprehensively evaluate the quality of the path.

[0060] Step 1386: Use the turning penalty coefficient to perform weighted correction on the total path length, generate a corrected path evaluation value, and select the candidate order with the smallest corrected path evaluation value as the target access order.

[0061] In this step, the generated turning penalty coefficient is used to perform weighted correction on the total path length. For the total path length Ltotal corresponding to each candidate order, multiply it by the turning penalty coefficient k to obtain the corrected path evaluation value M, that is, M = k * Ltotal (i.e., the corrected path evaluation value is the total path length multiplied by the turning penalty coefficient). In this way, the impact of the number of turns on the path is quantified into the path evaluation. Such calculations are performed for each candidate order in the set of candidate node access orders to obtain a series of corrected path evaluation values. Then, select the candidate order corresponding to the smallest corrected path evaluation value as the target access order. This target access order is considered to be the optimal access order after comprehensively considering factors such as path length, number of turns, and shelf spacing, providing the correct node order for subsequent generation of an optimized access path sequence.

[0062] Step 1387: Extract the corresponding node connection relationships based on the target access order, and insert the coordinates of the non-material nodes in the initial access path sequence between adjacent material nodes according to the shortest path search algorithm to generate an intermediate transition path segment.

[0063] In an embodiment of the present invention, according to the determined target access order, the corresponding node connection relationships are extracted. These connection relationships clarify the order in which the handling robot should pass through each material position node. Then, for the non-material node coordinates in the initial access path sequence, such as some intermediate transition points or auxiliary points passed by the handling robot in the path, the shortest path search algorithm (such as the Dijkstra algorithm, which continuously finds the node closest to the starting point and has not been visited, and updates its distance to other nodes to find the shortest path) is used to insert them between adjacent material nodes. For example, between material node A and material node B, an optimal path is found through the shortest path search algorithm, and the non-material node coordinates are inserted according to this optimal path to generate an intermediate transition path segment. The generated intermediate transition path segment not only ensures that the handling robot can pass through each material node according to the target access order, but also realizes path optimization in the connection of non-material nodes, laying a foundation for finally generating an optimized access path sequence.

[0064] Step 1388: Perform obstacle collision detection on the intermediate transition path segment. If it is detected that there is a spatial overlap area between the intermediate transition path segment and the obstacle influence range feature, then use the passing weight value of the channel passing state feature to adjust the path detour parameter; update the node coordinates of the intermediate transition path segment according to the adjusted path detour parameter, and recalculate the total path length and the number of turns until the preset linkage constraint conditions are met.

[0065] In this step, strict obstacle collision detection is performed on the generated intermediate transition path segment. Through a preset collision detection algorithm (such as a collision detection algorithm based on coordinate range, which checks whether the points on the path are within the coordinate range of the inflated area of the obstacle), it is determined whether there is a spatial overlap area between the intermediate transition path segment and the obstacle influence range feature. If it is detected that there is an overlap area, it means that the path may collide with the obstacle and needs to be adjusted. At this time, the passing weight value of the channel passing state feature is used to adjust the path detour parameter. For example, if the channel passing state feature shows that the passing weight of a certain area is high, indicating that the passing difficulty of this area is large, then the path detour parameter will be adjusted accordingly to make the path avoid this area as much as possible. According to the adjusted path detour parameter, update the node coordinates of the intermediate transition path segment to change the direction of the path. Then, recalculate the total path length and the number of turns, and check again whether the preset linkage constraint conditions are met. The preset linkage constraint conditions may include that the total path length cannot exceed a certain threshold, and the number of turns is within a reasonable range, etc. This process is continuously repeated until the path meets the preset linkage constraint conditions, ensuring that the generated path is both safe and efficient.

[0066] Step 1389: Connect the nodes of the updated intermediate transition path segments in the target access order to generate the optimized access path sequence, where the order and coordinates of the node connections are aligned with the three-dimensional coordinates of the warehouse space scan information.

[0067] After the intermediate transition path segments have been adjusted multiple times to meet the preset linkage constraint conditions, connect their respective nodes in the target access order. Ensure that the order of node connections is consistent with the previously determined target access order, and that the coordinates of the nodes are precisely aligned with the three-dimensional coordinates of the warehouse space scan information. For example, connect the nodes in the adjusted intermediate transition path segments in sequence according to the target access order to form a complete path. During the connection process, carefully check the coordinates of each node to match them with the three-dimensional coordinates in the warehouse space scan information, ensuring the accuracy and feasibility of the path in the actual warehouse space. Through such operations, an optimized access path sequence is finally generated. This sequence comprehensively considers various factors such as material access priority, path length, number of turns, obstacle avoidance, etc., providing an efficient, safe, and accurate access path for the handling robot.

[0068] Step 140: Send the optimized access path sequence to the control terminal of the handling robot to control the handling robot to perform material access operations.

[0069] After generating the optimized access path sequence, it is necessary to transfer it to the control terminal of the handling robot so that the handling robot can perform material access tasks according to the planned path. The optimized access path sequence contains detailed path information for the handling robot to run in the warehouse. After receiving this sequence, the control terminal can convert it into specific control instructions to direct the actions of the handling robot.

[0070] As an embodiment, the sending the optimized access path sequence to the control terminal of the handling robot to control the handling robot to perform material access operations includes: Step 141: Convert the optimized access path sequence into a robot motion control instruction set, where the motion control instruction set includes travel direction instructions, speed adjustment instructions, and steering angle instructions.

[0071] This step aims to parse and transform the optimized access path sequence to generate a motion control instruction set suitable for the control terminal of the handling robot. For each path node in the optimized access path sequence, according to its coordinates and the relationship with adjacent nodes, the traveling direction instruction is calculated. For example, through the coordinate difference between two adjacent nodes, the traveling direction of the handling robot from one node to the next can be determined. The speed adjustment instruction is determined according to the characteristics of the path and the actual situation of the warehouse. For example, the speed can be appropriately increased in a wide passage area, while it needs to be decreased in an area close to the shelves or obstacles. The steering angle instruction is generated by analyzing the turning situation of the path, and the angle that the handling robot needs to turn is determined according to the change amount of the connection direction between adjacent nodes. These instructions are sorted and encoded to form a motion control instruction set, which can precisely control the movement of the handling robot and make it run according to the optimized access path sequence.

[0072] Step 142: During the traveling process of the handling robot, environmental change data is collected in real time, and the environmental change data includes the positions of newly added obstacles and shelf displacement data.

[0073] During the process of the handling robot performing tasks, it is very important to collect environmental change data in real time because the warehouse environment may change at any time. Through various sensors installed on the handling robot, such as lidar, cameras, etc., the surrounding environment is monitored in real time. The lidar can detect the positions of newly added obstacles. By emitting laser beams and receiving reflected signals, the contour and position coordinates of the obstacles are determined. The camera can assist in identifying and positioning the obstacles, and at the same time can also detect whether the shelves have shifted. If the shelves have moved due to certain reasons, the camera can capture this change and transmit the relevant data to the control system of the handling robot. These positions of newly added obstacles and shelf displacement data constitute the environmental change data, providing a basis for subsequent correction of the optimized access path sequence.

[0074] Step 143: According to the environmental change data, the optimized access path sequence is corrected to generate a real-time obstacle avoidance path, and the real-time obstacle avoidance path is superimposed on the motion control instruction set.

[0075] According to the collected environmental change data, the optimized access path sequence is corrected in a timely manner. In a preferred embodiment, the correcting the optimized access path sequence according to the environmental change data to generate a real-time obstacle avoidance path includes: Step 1431: Match the positions of the newly added obstacles with the obstacle influence range features in the path planning state feature set to determine whether the newly added obstacles are located within the preset avoidance area of the current path node.

[0076] Compare the detected position information of the newly added obstacle with the obstacle influence range feature in the path planning status feature set. For each node on the current path, there is a preset avoidance area, which is preset according to factors such as the size and running speed of the handling robot to ensure that the robot will not collide with obstacles during operation. Through a preset matching algorithm (such as a coordinate range-based matching algorithm, to determine whether the coordinates of the newly added obstacle are within the coordinate range of the preset avoidance area), it is determined whether the newly added obstacle is located within the preset avoidance area of the current path node. If it is within this area, it means that the path needs to be adjusted to avoid the newly added obstacle.

[0077] Step 1432: If it is within the preset avoidance area, recalculate the detour path based on the contour coordinates of the newly added obstacle, and insert the detour path before the subsequent node of the current path node.

[0078] When it is determined that the newly added obstacle is within the preset avoidance area, calculate the detour path based on the contour coordinates of the newly added obstacle. Use a preset path calculation algorithm (such as a variant of the A* algorithm, to find the optimal path while considering avoiding obstacles), with the current path node as the starting point and a subsequent node of this node as the ending point, recalculate a detour path while avoiding the newly added obstacle. For example, determine the impassable area around it according to the contour coordinates of the newly added obstacle, and then search for an optimal path from the current path node to the subsequent node in the remaining passable space. After calculating the detour path, insert it before the subsequent node of the current path node. In this way, when the handling robot runs to the current path node, it will avoid the newly added obstacle according to the detour path and then return to the original path to continue moving forward.

[0079] Step 1433: If the shelf displacement data causes the target shelf coordinates to shift, update the end position of the optimized storage and retrieval path sequence according to the shifted shelf coordinates, and replan the local path from the current position to the updated end position.

[0080] If the collected shelf displacement data indicates that the coordinates of the target shelf have shifted, then it is necessary to update the end position of the optimized storage and retrieval path sequence in a timely manner. According to the shifted shelf coordinates, use them as the new end coordinates. Then, with the current position of the handling robot as the starting point and the updated end position as the ending point, replan a local path, which can be achieved by using the preset path search algorithm (such as the Dijkstra algorithm) again. Considering the warehouse environment and other obstacles, find an optimal path from the current position to the new end position, so as to ensure that the handling robot can still accurately reach the target shelf for material storage and retrieval operations after the shelf position changes.

[0081] Step 1434: Stitch the detour path or the local path with the unaffected original path segment to generate the real-time obstacle avoidance path.

[0082] Further, stitch the detour path generated according to the newly added obstacle or the local path re-planned according to the displacement of the shelf with the original path segment that is not affected by the environmental change. During the stitching process, ensure the coherence and rationality of the path. For example, when inserting the detour path, connect the starting point of the detour path to the current path node, and connect the ending point of the detour path to the subsequent node of this node on the original path to form a complete real-time obstacle avoidance path. For the re-planned local path, a similar method is also used to stitch it with the original path segment, so that the handling robot can smoothly run along this real-time obstacle avoidance path, avoid the newly added obstacles and accurately reach the target shelf. Finally, overlay the generated real-time obstacle avoidance path onto the motion control instruction set and update the control instructions to ensure that the handling robot runs according to the new path.

[0083] Step 144: Execute the updated motion control instruction set through the control terminal of the handling robot to complete the material access operation.

[0084] It can be understood that after the control terminal of the handling robot receives the updated motion control instruction set, it starts to execute these instructions to complete the material access operation. In an optional embodiment, the step of executing the updated motion control instruction set through the control terminal of the handling robot to complete the material access operation includes: Step 1441: Continuously monitor the pose state data of the handling robot when it travels along the real-time obstacle avoidance path, and the pose state data includes real-time coordinates, traveling speed, and steering angle.

[0085] During the process of the handling robot running along the real-time obstacle avoidance path, continuously monitor its pose state data through various sensors installed on the robot. For example, use a positioning sensor to obtain the real-time coordinates of the handling robot in real time to accurately determine its position in the warehouse space; monitor the traveling speed through a speed sensor to ensure that the robot runs according to the speed adjustment instruction; use an angle sensor to detect the steering angle to ensure that the steering operation of the robot meets the instruction requirements. These pose state data are transmitted back to the control terminal in real time to provide data support for subsequent deviation comparison and path adjustment.

[0086] Step 1442: Compare the deviation between the pose state data and the expected pose data in the motion control instruction set. If the deviation exceeds the fault tolerance threshold, pause the execution of the current instruction and activate a path re-planning request.

[0087] Compare the pose state data monitored in real time with the expected pose data preset in the motion control instruction set. For parameters such as real-time coordinates, traveling speed, and steering angle, there are corresponding expected values. Through a preset deviation calculation algorithm (such as calculating the absolute value of the difference between the actual value and the expected value), calculate the deviation between the pose state data and the expected pose data. If the deviation exceeds the preset fault tolerance threshold, it indicates that the running state of the handling robot does not match the expectation, and there may be running risks. At this time, the control terminal will suspend the execution of the current instruction to prevent the robot from continuing to run along the wrong path, and activate a path replanning request to start a new round of path planning process to ensure that the robot can complete the task safely and accurately.

[0088] Step 1443: In response to the path replanning request, generate a revised motion control instruction set based on the latest environmental change data and the current pose state data.

[0089] After receiving the path replanning request, the system will generate a revised motion control instruction set based on the latest collected environmental change data and the pose state data of the current handling robot. In an alternative embodiment, the generating a revised motion control instruction set based on the latest environmental change data and the current pose state data in response to the path replanning request includes: Step 14431: Extract the coordinates of the newly added obstacle positions and the target shelf coordinates after the shelf displacement from the environmental change data, and extract the real-time coordinates, traveling speed, and steering angle from the current pose state data.

[0090] Accurately extract the coordinates of the newly added obstacle positions and the target shelf coordinates after the shelf displacement from the collected environmental change data. These coordinate information are key data for replanning the path and can reflect the changes in the current warehouse environment. At the same time, extract the real-time coordinates, traveling speed, and steering angle of the handling robot from the current pose state data. These data represent the current running state of the robot. By accurately extracting these data, it provides a comprehensive information basis for subsequent path planning and instruction generation.

[0091] Step 14432: Align the coordinates of the newly added obstacle positions with the real-time coordinates in the space coordinate system to generate a relative position vector of the obstacle, and update the path end position based on the target shelf coordinates after the shelf displacement.

[0092] Align the coordinates of the newly added obstacle position with the real-time coordinates of the handling robot in the spatial coordinate system. Through a preset coordinate alignment algorithm (such as translation and rotation operations to make the two coordinate systems in the same reference system), generate the relative position vector of the obstacle, which can accurately represent the direction and distance of the newly added obstacle relative to the current position of the handling robot, providing important spatial relationship information for path planning. At the same time, update the end position of the path according to the target shelf coordinates after the shelf displacement to ensure that the target of the path is the correct target shelf.

[0093] Step 14433: Calculate the projection distance and lateral offset between the newly added obstacle and the current traveling direction according to the relative position vector of the obstacle. If the projection distance is less than the preset safety distance threshold and the lateral offset is within the path width range, it is determined as a path conflict area.

[0094] Calculate the projection distance and lateral offset between the newly added obstacle and the current traveling direction of the handling robot using the relative position vector of the obstacle. The projection distance can be obtained through vector projection operations (such as projecting the relative position vector of the obstacle onto the current traveling direction vector to calculate the projection length), and the lateral offset can be calculated through the vertical component of the vector. Compare the calculated projection distance with the preset safety distance threshold, and at the same time check whether the lateral offset is within the path width range. If the projection distance is less than the preset safety distance threshold and the lateral offset is within the path width range, it indicates that the handling robot may collide with the newly added obstacle when moving forward along the current path. Determine this area as a path conflict area and adjust the path accordingly.

[0095] Step 14434: Based on the contour coordinates of the path conflict area, use the window method to generate candidate obstacle avoidance path segments in the neighborhood space of the real-time coordinates, and calculate the combined constraint conditions of each candidate obstacle avoidance path segment in combination with the traveling speed and the steering angle.

[0096] Optionally, based on the contour coordinates of the path conflict area, use the window method to generate candidate obstacle avoidance path segments in the neighborhood space of the real-time coordinates of the handling robot. The window method is a method for searching for paths in the local space. By setting a window range, search for feasible path segments within this range. During the process of generating candidate obstacle avoidance path segments, calculate the combined constraint conditions of each candidate obstacle avoidance path segment in combination with the traveling speed and steering angle of the handling robot. For example, considering the speed and steering ability of the robot, determine the maximum speed limit, minimum turning radius and other constraint conditions for each candidate path segment to ensure that the generated path segments can avoid obstacles and meet the movement ability of the robot.

[0097] Step 14435: Use the target shelf coordinates after the shelf displacement as the end constraint. Based on the joint constraint conditions, use a preset search algorithm to screen for the locally optimal path that meets the end reachability condition in the candidate obstacle avoidance path segments, and extract the path node sequence of the locally optimal path.

[0098] Use the target shelf coordinates after the shelf displacement as the end constraint condition to ensure that the generated path can finally reach the correct target shelf. Based on the previously calculated joint constraint conditions, use a preset search algorithm (such as an improved version of the A* algorithm that simultaneously considers the constraint conditions and the reachability of the path to the end) to screen in the candidate obstacle avoidance path segments. This algorithm will evaluate each candidate path segment to determine whether it meets the end reachability condition, that is, under the given constraint conditions, whether it is possible to reach the target shelf from the current position through this path segment. After screening, find the locally optimal path that meets the conditions and extract the path node sequence of this locally optimal path. This node sequence clarifies the various position points that the handling robot needs to pass through to reach the target shelf while avoiding obstacles, providing a path basis for generating subsequent motion control instructions.

[0099] Step 14436: Calculate the travel direction instruction based on the coordinate differences between adjacent nodes in the path node sequence, generate a speed adjustment instruction based on the ratio of the adjacent node spacing to the preset maximum speed, and determine the steering angle instruction based on the change in the direction angle of the line connecting the nodes.

[0100] For the extracted path node sequence, determine the travel direction instruction by calculating the coordinate differences between adjacent nodes. For example, for adjacent nodes A (X1, Y1, Z1) and node B (X2, Y2, Z2), the travel direction vector from node A to node B can be obtained through the coordinate differences (X2 - X1, Y2 - Y1, Z2 - Z1), and then the travel direction instruction for the handling robot can be determined. Generate a speed adjustment instruction based on the ratio of the adjacent node spacing to the preset maximum speed. If the adjacent node spacing is large and within the safe range, to improve efficiency, the speed adjustment instruction may make the robot run at a higher speed; conversely, if the spacing is small or the surrounding environment is complex, the speed adjustment instruction will reduce the running speed of the robot. Determine the steering angle instruction by calculating the change in the direction angle of the line connecting the nodes. When the handling robot moves from one node to the next, the direction angle of the line connecting the nodes changes, and based on this change amount, the angle that the robot needs to turn can be accurately calculated, thus generating the steering angle instruction. These instructions precisely control the movement of the handling robot from different aspects, enabling it to run safely and efficiently along the planned path.

[0101] Step 14437: Perform timing synchronization processing on the travel direction instruction, the speed adjustment instruction, and the steering angle instruction to generate an instruction queue containing timestamps, and perform acceleration smoothing filtering on the instruction queue and the real-time speed in the current pose state data to obtain the corrected motion control instruction set.

[0102] Perform timing synchronization processing on the calculated travel direction instruction, speed adjustment instruction, and steering angle instruction. This is to ensure the consistency and coordination of these instructions in terms of time, enabling the handling robot to execute various actions in the correct order and at the right time. By adding timestamps, mark the time points at which each instruction should be executed to generate an instruction queue containing timestamps. Then, perform acceleration smoothing filtering on this instruction queue and the real-time speed in the current pose state data. Acceleration smoothing filtering is to avoid overly drastic changes in instructions and make the movement of the robot smoother. For example, through a preset filtering algorithm (such as a weighted moving average filtering algorithm, which calculates the weighted average of the current speed and the speed instructions in the instruction queue), adjust the speed instructions in the instruction queue to make their transition to the real-time speed of the robot smoother and reduce the impact of speed mutations on the running stability of the robot. After such processing, the corrected motion control instruction set is obtained, which can control the movement of the handling robot more precisely and smoothly, ensuring that it accurately executes the material access and storage tasks in a complex warehouse environment.

[0103] Step 1444: Control the handling robot to resume travel according to the corrected motion control instruction set until it reaches the target shelf position for material access and storage operations; After the control terminal of the handling robot receives the corrected motion control instruction set, it controls the robot to resume travel according to these instructions. The handling robot will precisely adjust its own motion state according to the travel direction instruction, speed adjustment instruction, and steering angle instruction in the instruction set. During travel, it continuously operates according to the instructions until it successfully reaches the target shelf position. When it arrives at the target shelf, the handling robot will perform material access and storage operations using devices such as a robotic arm according to a preset operation process. For example, the robotic arm will adjust its telescopic length and angle according to the height of the shelf and the position of the material to accurately grasp or place the power battery and complete the material access and storage tasks.

[0104] As a non-limiting embodiment, after sending the optimized access path sequence to the control terminal of the handling robot, the method further includes: - Receiving in real time the pose verification data fed back by the handling robot, where the pose verification data includes real-time three-dimensional coordinates, traveling yaw angle, and shelf alignment error; - Comparing the real-time three-dimensional coordinates point by point with the expected node coordinates in the optimized access path sequence. If the coordinate deviation of three consecutive nodes exceeds a preset threshold, then respond to the path backtracking instruction; - Controlling the handling robot to return to the previous correct path node according to the path backtracking instruction, and adjusting the grasping pose parameters of the robotic arm based on the shelf alignment error; - Recalculating the local path from the previous correct path node to the target shelf, and generating a compensation steering instruction set by integrating the traveling yaw angle, and superimposing the compensation steering instruction set on the corresponding nodes of the optimized access path sequence; - After the handling robot reaches the target shelf, verifying the relative distance between the shelf and the end of the robotic arm through a laser ranging device. If the relative distance exceeds the safe operation range, adjusting the telescopic length and clamping angle of the robotic arm based on the relative distance.

[0105] During the task execution of the handling robot, the control center receives in real time the pose verification data fed back by the robot, which is crucial for monitoring the running state of the robot and ensuring the accurate execution of the task. The real-time three-dimensional coordinates can accurately reflect the position of the robot in the warehouse space, the traveling yaw angle reflects whether the traveling direction of the robot is accurate, and the shelf alignment error is used to judge the alignment situation between the robot and the target shelf. Compare the real-time three-dimensional coordinates point by point with the expected node coordinates in the optimized access path sequence. Through a preset comparison algorithm (such as calculating the absolute value of the coordinate difference and comparing it with the preset threshold), check the deviation between the actual position and the expected position of each node. If the coordinate deviation of three consecutive nodes exceeds the preset threshold, it indicates that there may be a large deviation in the running path of the robot. At this time, respond to the path backtracking instruction. Control the robot to return to the previous correct path node according to the path backtracking instruction to correct the path deviation.

[0106] Meanwhile, adjust the grasping pose parameters of the robotic arm based on the shelf alignment error. For example, if the shelf alignment error is large, it means that the angle or position between the robotic arm and the shelf is inaccurate. It is necessary to adjust parameters such as the rotation angle and telescopic length of the robotic arm according to the direction and magnitude of the error to ensure that the material can be accurately grasped. Recalculate the local path from the previous correct path node to the target shelf, considering the current position and direction of the robot (integrating the traveling yaw angle), and use a preset path calculation algorithm (such as the A* algorithm) to generate a new local path. According to this new path, generate a compensation steering instruction set, which can correct the traveling direction deviation of the robot and enable it to reach the target shelf smoothly. Superimpose the compensation steering instruction set on the corresponding nodes of the optimized access path sequence to update the path information.

[0107] After the handling robot reaches the target shelf, the relative distance between the shelf and the end of the robotic arm is accurately measured by a laser ranging device. If the relative distance exceeds the safe operation range, it indicates that the current state of the robotic arm may not be able to safely access and store materials. At this time, adjust the telescopic length and clamping angle of the robotic arm based on the relative distance. For example, if the distance is too large, increase the telescopic length of the robotic arm; if the distance is too small, decrease the telescopic length, and adjust the clamping angle according to factors such as the shape and weight of the material to ensure that the material can be stably and safely grasped or placed.

[0108] As a non-limiting embodiment, after sending the optimized access path sequence to the control terminal of the handling robot, the method further includes: monitoring the remaining battery power of the handling robot and the priority weights of the unexecuted materials in the task queue. When the remaining battery power is lower than a preset threshold, extract the position of the target material with the highest priority weight among the unexecuted materials; calculate the reachable path radius based on the Euclidean distance between the target material position and the current position of the handling robot and the remaining battery power, and screen the path nodes within the reachable path radius in the optimized access path sequence; generate an emergency task sub-path based on the screened path nodes, and freeze the material access tasks with priority weights lower than a preset value; when the handling robot executes the emergency task sub-path, collect real-time battery voltage fluctuation data. If the voltage drop rate exceeds the historical average, reduce the traveling speed and shorten the operation interval of the robotic arm; after completing the material access corresponding to the emergency task sub-path, generate a low battery warning signal and update the path planning status feature set of the unexecuted materials in the task queue.

[0109] During the continuous execution of the material access task by the handling robot, the system will monitor the remaining battery power of the robot and the priority weights of the unexecuted materials in the task queue in real time. The remaining battery power is a key factor affecting the continuous operation of the robot, while the priority weight of the material determines the execution order of the task.

[0110] When it is detected that the remaining battery power is lower than the preset threshold, it indicates that the battery power is already low, and it is necessary to prioritize important tasks to ensure that the robot can complete key work before the battery runs out. At this time, the target material position with the highest priority weight is extracted from the unexecuted materials. By comparing the priority weights of each material in the task queue (using a preset comparison algorithm, such as sorting according to the weight value), the position information corresponding to the material with the highest weight is found. According to the Euclidean distance between the target material position and the current position of the handling robot (calculated by the Euclidean distance formula in three-dimensional space, that is, calculating the square root of the sum of the squares of the coordinate differences between two points to obtain the distance), the reachable path radius is calculated in combination with the remaining battery power. The reachable path radius refers to the maximum distance range within which the robot can safely reach the target material under the current battery power. A preset calculation algorithm (such as based on the relationship between battery power and energy consumption model, combined with distance to calculate the reachable radius) is used to determine the reachable path radius. The path nodes within the reachable path radius are screened out from the optimized access path sequence. By comparing the distance between each path node and the current position of the robot with the reachable path radius (using a preset screening algorithm to judge whether the distance is less than or equal to the radius), the qualified path nodes are screened out. An emergency task sub-path is generated based on these screened path nodes, and this sub-path is the task path that the robot gives priority to execute in the case of low battery power.

[0111] Meanwhile, to ensure that the battery power can support the completion of the emergency task, the material access tasks with priority weights lower than the preset value are frozen, and the execution of these tasks is suspended. When the handling robot executes the emergency task sub-path, the battery voltage fluctuation data is collected in real time. Through the voltage sensor installed on the battery system, the battery voltage value is obtained in real time, and the voltage drop rate is calculated. The current voltage drop rate is compared with the historical average value (using a preset comparison algorithm to judge whether the current rate is greater than the historical average value). If the voltage drop rate exceeds the historical average value, it indicates that the battery is consumed too fast. To extend the battery life, the traveling speed of the robot is reduced and the operation interval of the robotic arm is shortened. Reducing the traveling speed can reduce the energy consumption of the robot, and shortening the operation interval of the robotic arm can also reduce the energy consumption, thus alleviating the problem of rapid battery power decline to a certain extent. When the material access corresponding to the emergency task sub-path is completed, the system generates a low-battery warning signal to remind the operator to handle the battery power problem in time.

[0112] Synchronously, the path planning status feature set of the unexecuted materials in the task queue is updated. Because the change of battery power and the execution situation of some tasks may affect the path planning of subsequent tasks, it is necessary to re-evaluate and update the path planning status feature set (including shelf spacing features, obstacle influence range features, and channel passage status features, etc.) to provide accurate environmental information and task priority information for subsequent path planning, so as to arrange the work tasks of the handling robot more reasonably.

[0113] In the embodiments of the present invention, three-dimensional space scanning and radio frequency identification can be combined to perform digital modeling of the warehouse environment and collection and processing of material information, so as to ensure accurate and consistent shelf coordinates and obstacle contour data. The coordinates of obstacles can also be extended by the morphological dilation algorithm in existing computer vision to generate a safety buffer to avoid collisions; the geometric distance calculation method is used to analyze the shelf spacing and channel overlapping areas, so as to construct a comprehensive set of state characteristics for path planning.

[0114] In the embodiments of the present invention, based on the idea of existing graph search algorithms, such as heuristic path planning technology, node traversal and path generation can be performed in a weighted access graph, so as to screen the initial access path sequence through safety distance verification. In the stage of adjusting the sequence, based on the existing sorting optimization technology, topological sorting processing is carried out through priority weight assignment and distance feature vector integration, so as to minimize the path cost and optimize the node order; then combined with the shortest path search algorithm, intermediate nodes are inserted, and an optimized sequence is generated through obstacle collision detection and access weight adjustment.

[0115] For real-time control, with the help of existing sensor fusion systems and dynamic path correction frameworks, real-time obstacle avoidance instructions can be generated through coordinate matching and conflict area identification, so as to apply motion control algorithms in the control terminal to achieve smooth speed and direction adjustment. The embodiments of the present invention can also use existing industrial robot control protocols for instruction timing synchronization and filtering processing to ensure the efficient execution of tasks by handling robots. Thus, those skilled in the art can clearly and completely handle environmental changes and priority requirements, improving access efficiency and system reliability.

[0116] The embodiments of the present invention comprehensively and accurately grasp the warehouse layout and material conditions by obtaining the warehouse space scanning information and material scanning information of the automated storage and retrieval system, providing a solid and reliable data basis for subsequent path planning to ensure that the planned path fits the actual scenario; generating a set of path planning state characteristics containing multi-dimensional features based on the shelf distribution state and obstacle distribution state, depicting the access conditions of the warehouse space from different angles and considering various influencing factors more carefully and comprehensively; performing access path planning based on this set of characteristics and material position identification and adjusting the order of path nodes, which can fully combine the access priorities of materials to generate an efficient and reasonable optimized access path sequence, greatly improving the overall efficiency of material access; sending the optimized access path sequence to the handling robot control terminal can accurately control the robot to perform material access operations, making the handling process more orderly and accurate, improving the operation efficiency and stability of the automated storage and retrieval system, and ensuring the smooth execution of material access tasks.

[0117] In summary, the embodiments of the present invention effectively solve the problems of incomplete information acquisition and unreasonable path planning in the prior art by comprehensively acquiring relevant information and performing multi-dimensional feature integration and path planning optimization, thereby improving the overall efficiency of the storage and retrieval operations in the three-dimensional warehouse.

[0118] Further, Figure 2 FIG. shows a structural block diagram of a three-dimensional warehouse storage optimization system 300, including: a memory 310 for storing program instructions and data; a processor 320 coupled to the memory 310 to execute the instructions in the memory 310 to implement the above method.

[0119] Further, a computer storage medium is also provided, containing instructions that, when executed on a processor, implement the above method.

[0120] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A three-dimensional warehouse storage and retrieval optimization method based on path planning, characterized in that Including: Obtaining the warehouse space scanning information and material scanning information of the stereoscopic warehouse, where the warehouse space scanning information includes the shelf distribution state and the obstacle distribution state, and the material scanning information includes the position identifier and the access priority identifier of the material to be stored and retrieved; Generating a path planning state feature set according to the shelf distribution state and the obstacle distribution state, where the path planning state feature set includes shelf spacing features, obstacle influence range features, and channel passage state features; Performing access path planning processing based on the path planning state feature set and the position identifier of the material to be stored and retrieved, generating an initial access path sequence, and adjusting the path node order of the initial access path sequence according to the access priority identifier to obtain an optimized access path sequence; Sending the optimized access path sequence to the control terminal of the handling robot to control the handling robot to perform material storage and retrieval operations.

2. The method according to claim 1, wherein The shelf distribution state includes a shelf coordinate set, the obstacle distribution state includes an obstacle coordinate set, the position identifier includes the material storage position coordinates, and obtaining the warehouse space scanning information and material scanning information of the stereoscopic warehouse includes: Performing three-dimensional space scanning on the shelf layout of the stereoscopic warehouse through a laser scanning device to generate a shelf coordinate set and an obstacle coordinate set, where the shelf coordinate set includes the center point coordinates and boundary coordinates of each shelf, and the obstacle coordinate set includes the contour coordinates of each obstacle; Invoking a radio frequency identification device to scan the electronic tag of the material to be stored and retrieved, and extracting the material storage position coordinates and the material urgency identifier, where the material urgency identifier is used to determine the access priority identifier; Performing coordinate alignment processing on the shelf coordinate set and the obstacle coordinate set to obtain the warehouse space scanning information, and associatively storing the material storage position coordinates and the material urgency identifier as the material scanning information.

3. The method according to claim 2, wherein The generating a path planning state feature set according to the shelf distribution state and the obstacle distribution state includes: Calculating the minimum distance between adjacent shelves according to the shelf coordinate set to generate the shelf spacing feature; Performing contour dilation processing on the obstacle coordinate set to determine the dilation area coordinates of each obstacle, and generating the obstacle influence range feature based on the dilation area coordinates; Traversing the preset channel area of the stereoscopic warehouse, detecting the overlapping areas between the preset channel area and the shelf coordinate set and the obstacle coordinate set, and generating the channel passage state feature according to the position and area of the overlapping areas; Combining the shelf spacing feature, the obstacle influence range feature, and the channel passage state feature into the path planning state feature set.

4. The method according to claim 1, wherein The performing access path planning processing based on the path planning state feature set and the position identifier of the material to be stored and retrieved to generate an initial access path sequence includes: Extracting the channel passage state feature from the path planning state feature set to generate a passage weight map, where the nodes in the passage weight map represent the connection points between shelves, and the edge weights represent the channel passage difficulty; Taking the current position of the transport robot as the starting point and the shelf coordinates corresponding to the location identifier of the material to be stored and retrieved as the target point, the target travel path is searched in the travel weight map to generate a set of candidate paths; Performing a safety distance check on each path in the candidate path set according to the shelf spacing characteristics and the obstacle influence range characteristics, eliminating paths with safety conflicts, and obtaining a checked path set; Selecting a path with the shortest path length and the least number of turns from the verified path set as the initial access path sequence; The step of adjusting the order of the path nodes of the initial access path sequence according to the access priority identifier to obtain an optimized access path sequence includes: Parsing the access priority identifier to determine the access urgency ranking of multiple materials to be accessed; Detecting whether the initial access path sequence includes multiple material location nodes, and if so, performing path insertion processing on the multiple material location nodes according to the access urgency sorting; Calculate the difference between the total length of the path after insertion and the original path length, and if the difference exceeds a preset difference, reallocate the access order of the material location nodes so that the difference is less than the preset difference; The node connection relationship of the initial access path sequence is updated according to the reallocated access order to generate the optimized access path sequence.

5. The method according to claim 1, characterized in that, The step of sending the optimized access path sequence to a control terminal of a transport robot to control the transport robot to perform a material access operation includes: Converting the optimized access path sequence into a robot motion control instruction set, wherein the motion control instruction set includes a travel direction instruction, a speed adjustment instruction, and a steering angle instruction; Collecting environmental change data in real time during the movement of the transport robot, the environmental change data including the location of new obstacles and shelf displacement data; Modifying the optimized access path sequence according to the environmental change data, generating a real-time obstacle avoidance path, and superimposing the real-time obstacle avoidance path to the motion control instruction set; The updated motion control instruction set is executed by the control terminal of the transport robot to complete the material storage and retrieval operation.

6. The method according to claim 5, wherein The step of modifying the optimized access path sequence according to the environmental change data to generate a real-time obstacle avoidance path includes: Matching the position of the newly added obstacle with the obstacle influence range feature in the path planning state feature set to determine whether the newly added obstacle is located in a preset avoidance area of ​​the current path node; If the obstacle is within the preset avoidance area, recalculate the detour path based on the contour coordinates of the newly added obstacle, and insert the detour path before the subsequent node of the current path node; If the shelf displacement data causes the target shelf coordinates to shift, then the end position of the optimized access path sequence is updated according to the offset shelf coordinates, and a local path from the current position to the updated end position is replanned; The detour path or the local path is spliced ​​with the unaffected original path segment to generate the real-time obstacle avoidance path.

7. The method according to claim 6, characterized in that, Executing the updated motion control instruction set through the control terminal of the handling robot to complete the material access operation, including: When the handling robot travels along the real-time obstacle avoidance path, continuously monitor the pose state data of the handling robot, where the pose state data includes real-time coordinates, traveling speed, and steering angle; Compare the deviation between the pose state data and the expected pose data in the motion control instruction set. If the deviation exceeds the fault tolerance threshold, pause the execution of the current instruction and activate a path replanning request; In response to the path replanning request, generate a corrected motion control instruction set based on the latest environmental change data and the current pose state data; Control the handling robot to resume traveling according to the corrected motion control instruction set until it reaches the target shelf position for material access operation; The generating the corrected motion control instruction set based on the latest environmental change data and the current pose state data in response to the path replanning request includes: Extract the coordinates of the newly added obstacle positions and the target shelf coordinates after the shelf displacement from the environmental change data, and extract the real-time coordinates, traveling speed, and steering angle from the current pose state data; Align the coordinates of the newly added obstacle positions with the real-time coordinates in the space coordinate system to generate the relative position vector of the obstacle, and update the path end position based on the target shelf coordinates after the shelf displacement; Calculate the projection distance and lateral offset between the newly added obstacle and the current traveling direction according to the relative position vector of the obstacle. If the projection distance is less than the preset safety distance threshold and the lateral offset is within the path width range, it is determined as a path conflict area; Based on the contour coordinates of the path conflict area, use the window method to generate candidate obstacle avoidance path segments in the neighborhood space of the real-time coordinates, and calculate the combined constraint conditions for each candidate obstacle avoidance path segment in combination with the traveling speed and the steering angle; Use the target shelf coordinates after the shelf displacement as the end constraint. Based on the combined constraint conditions, use a preset search algorithm to screen the local optimal path that meets the end reachability condition from the candidate obstacle avoidance path segments, and extract the path node sequence of the local optimal path; Calculate the traveling direction instruction according to the coordinate difference between adjacent nodes in the path node sequence, generate the speed adjustment instruction based on the ratio of the distance between adjacent nodes to the preset maximum speed, and determine the steering angle instruction according to the change amount of the direction angle of the connection line between nodes; Perform timing synchronization processing on the traveling direction instruction, the speed adjustment instruction, and the steering angle instruction to generate an instruction queue containing timestamps, and perform acceleration smoothing filtering processing on the instruction queue and the real-time speed in the current pose state data to obtain the corrected motion control instruction set.

8. The method according to claim 5, characterized in that The updating the node connection relationship of the initial access path sequence according to the reallocated access order to generate the optimized access path sequence includes: Generate the priority weight value of each material position node according to the sorting of the access urgency, and the priority weight value is positively correlated with the access urgency; Extract the three-dimensional coordinate set of all material position nodes in the initial access path sequence, and generate a distance feature vector between nodes based on the Euclidean distance matrix of the three-dimensional coordinate set; Generate a weighted path evaluation matrix based on the distance feature vector between nodes and the priority weight value. Each element in the weighted path evaluation matrix represents the comprehensive path cost between two nodes, and the comprehensive path cost is calculated by linearly superimposing the node distance and the priority weight difference; Perform topological sorting on the material position nodes according to the weighted path evaluation matrix to generate a set of candidate node access orders, and the topological sorting satisfies the constraint of minimizing the comprehensive path cost between nodes; Traverse the set of candidate node access orders, calculate the total path length and the number of turns corresponding to each candidate order, and generate a turning penalty coefficient in combination with the safety threshold of the shelf spacing feature; Use the turning penalty coefficient to perform weighted correction on the total path length to generate a corrected path evaluation value, and select the candidate order with the smallest corrected path evaluation value as the target access order; Extract the corresponding node connection relationship based on the target access order, and insert the coordinates of non-material nodes in the initial access path sequence between adjacent material nodes according to the shortest path search algorithm to generate an intermediate transition path segment; Perform obstacle collision detection on the intermediate transition path segment. If it is detected that there is a spatial overlap area between the intermediate transition path segment and the obstacle influence range feature, adjust the path detour parameter using the passing weight value of the channel passing state feature; Update the node coordinates of the intermediate transition path segment according to the adjusted path detour parameter, and recalculate the total path length and the number of turns until the preset linkage constraint conditions are met; Connect the updated intermediate transition path segments according to the target access order to generate the optimized access path sequence, and the order and coordinates of the node connection are aligned with the three-dimensional coordinates of the warehouse space scan information.

9. A three-dimensional warehouse storage optimization system, characterized in that, Including: A memory for storing program instructions and data; A processor for being coupled with the memory and executing the instructions in the memory to implement the method according to any one of claims 1-8.

10. A computer storage medium, characterized in that, Containing instructions, when the instructions are executed on the processor, implementing the method according to any one of claims 1-8.

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