A woven bag production robot path planning method and system

Abstract

The application belongs to the technical field of robot path planning, and particularly relates to a woven bag production robot path planning method and system, which comprises the following steps: modeling a woven bag production workshop map environment by using a grid method, obtaining map information and storing the map information in a woven bag production robot; after modeling is completed, sending a task instruction through a production management system, receiving the instruction by the woven bag production robot and obtaining production task, starting point coordinate and ending point coordinate information by analysis; planning an initial path passing through the starting point coordinate and the ending point coordinate by the robot according to the map information and the information obtained by the analysis instruction; and performing conflict detection on the initial path planned by each robot, and performing path correction by real-time detection of obstacles through sensors installed on the robot. The application realizes accurate woven bag production robot running path planning by combining path planning, conflict detection and real-time path correction, and improves the production efficiency of the woven bag production robot.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of robot path planning, and particularly relates to a woven bag production robot path planning method and system. BACKGROUND

[0002] With the continuous development of industrial automation, robots are increasingly widely used in woven bag production. In the production process of woven bags, robots need to efficiently complete various tasks, such as carrying woven bags and stacking, and the path planning of the robots is a key technology to improve production efficiency. In order to ensure that the robots can accurately and quickly complete these tasks, accurate path planning is needed. However, how to efficiently plan the motion path of the robots in the woven bag production environment to improve production efficiency and avoid collisions during robot operation has become an important challenge. Traditional path planning methods are difficult to adapt to the complexity of the woven bag production scene, such as the existence of numerous equipment, material storage, and woven bag processing stations of different shapes and positions in the production workshop, and the use of a single path planning method cannot meet the requirements of the robots running strictly according to the rules. SUMMARY

[0003] The present application provides a woven bag production robot path planning method to solve the core problems of low production efficiency, low path planning applicability, and insufficient flexibility of the woven bag production robot in the prior art.

[0004] To achieve the above-mentioned purposes, the present application realizes the following technical solutions:

[0005] A woven bag production robot path planning method, the method comprising the following steps:

[0006] Step S10: modeling the woven bag production workshop map environment using a grid method to obtain map information and store it in each woven bag production robot;

[0007] Step S20: after modeling, sending task instructions through a production management system, each woven bag production robot receiving the instructions and obtaining production task, starting point coordinate, and end point coordinate information, and the production task information including grabbing, carrying, and stacking;

[0008] Step S30: each woven bag production robot obtains information from the map information and the parsed instructions, and plans an initial path passing through the starting point coordinate and the end point coordinate through an A* algorithm combined with a rule-based path planning method;

[0009] Step S40: conflict detection of the initial path planned by each robot, real-time detection of obstacles through sensors installed on the robots for path correction;

[0010] In the step S10, the map environment of the woven bag production workshop is modeled by using the grid method, and the map environment includes the positions and shapes of various areas in the workshop, the positions, lengths, and widths of the passages between the areas, the storage positions of the production raw materials, the positions and sizes of the storage containers and shelves, and the positions of the obstacles in the workshop.

[0011] In the step S40, the initial path planned for each robot is detected for conflicts by using a space region detection method to determine whether there is an intersection on the planned path of the woven bag production robot, and when there is an intersection, it indicates that the robots interfere with each other or collide with each other. According to the priority order of the robots or the actual situation of the workshop, the path planning method in the step S30 is adjusted to re-plan the running path.

[0012] Preferably, the step of modeling the map environment of the woven bag production workshop by using the grid method in the step S10 includes the following steps:

[0013] Determining the grid size and grid area: according to the size of the woven bag production workshop and the operation precision requirement of the woven bag production robot, the size of the grid is determined. When the area of the workshop is large and the operation precision requirement of the robot is low, a larger grid size can be selected, such as a square grid with a side length of 0.5 meters to 1 meter. When the area of the workshop is small and the operation precision requirement of the robot is high, such as the printing, sewing, and other fine processing links of the woven bag, a smaller grid size is required, such as a grid with a side length of 0.1 meters to 0.3 meters. The planar space of the woven bag production workshop is divided into regular grids of the same size according to the determined grid size. For example, for a rectangular workshop, the grids can be divided row by row and column by column from a corner of the workshop, and each grid has a unique coordinate identifier, which is represented by a two-dimensional coordinate (row number, column number).

[0014] Map information labeling: the positions and ranges of the obstacles in the workshop are determined by means of on-site scanning and measurement and manual marking. When there is an obstacle in a grid, such as equipment, pillars, accumulated raw materials, or finished products, the grid is marked as an "obstacle grid" and represented by a binary value "1". When there is no obstacle in a grid, the grid is marked as a "passage area grid" and represented by a binary value "0". When there is a special area in a grid, such as a high-temperature, high-pressure, or other dangerous area, and a high-precision operation area, such as a woven bag processing area with extremely high requirements for robot movement precision, the grid is marked as a "special area grid" and represented by an identifier or color coding.

[0015] Map information storage: the grid map information is stored in a two-dimensional array, each element of the array corresponds to a grid, and the array is stored in the storage unit of each bra production robot; in the storage unit of each robot, in addition to storing the information of the grid, some other information related to path planning can also be stored, such as the estimated value of the distance from each grid to the target position (such as the finished product storage area or the next processing station), the passing distance and time to the next grid, etc., which can be used in subsequent path planning to improve the efficiency and accuracy of path planning;

[0016] Map updating mechanism: rescan the bra production workshop every 1 hour to update the workshop map information; in addition, an event-driven updating mechanism is set, which automatically updates the grid map when an event affecting the map environment occurs in the workshop, such as the addition of production equipment, material accumulation caused by equipment failure, etc.

[0017] Preferably, the task instruction sent by the production management system after the modeling in step S20 is completed includes raw material grabbing instruction, finished product grabbing instruction, inter-process carrying instruction, raw material stacking instruction, finished product stacking instruction, task priority instruction and task time window instruction.

[0018] Preferably, the step of planning an initial path passing through the start point coordinate and the end point coordinate of each in step S30 by A* algorithm combined with rule-based path planning method includes:

[0019] Graph structure conversion: convert the modeled grid map of the bra production workshop into a graph structure, each grid is regarded as a node in the graph, and when two adjacent grids are both "passable area grids" in the map, an edge is established between the corresponding two nodes; the edge has a weight, and the Euclidean distance between the centers of two nodes (x1, y1) and (x2, y2) is calculated as the weight of the edge, and the calculation formula is shown as formula (1):

[0020]

[0021] Wherein, d is the calculated Euclidean distance, x1 and x2 are the horizontal coordinates of the two nodes, and y1 and y2 are the vertical coordinates of the two nodes;

[0022] Determine the heuristic function: the heuristic function h(n) is used to calculate the distance from the current node n (x n ,y n ) to the end node (x goal ,y goal ), and the calculation formula is shown as formula (2):

[0023]

[0024] Wherein, x nis the x-coordinate of the current node n, y n is the y-coordinate of the current node n, x goal is the x-coordinate of the end node, y goal is the y-coordinate of the end node;

[0025] Initialization of data structure: initialize the open list and the closed list, the open list is used to store the start node, and the start node is initially placed in the open list; the closed list is used to store the nodes searched by the algorithm in the map, and the closed list is empty initially;

[0026] Path search: select node n from the open list, n is the current node, and the selected node is used as the center to search the path, and node n satisfies formula (3):

[0027] f(n) = g(n) + h(n) (3)

[0028] Wherein, f(n) is used to evaluate the priority of node n in path search, g(n) is the actual distance from the starting node to the current node n, and h(n) is the distance from the current node n to the end node calculated by the heuristic function; for the current node n, it is judged whether it is the end node, when the node n is the end node, the parent node pointer of the node n is traced back from the end node to the start node in turn, and the generated node sequence is the planned path; when the node n is not the end node, it is placed in the closed list, and all adjacent nodes m are traversed to calculate the new actual distance g'(m) from the starting node to the node m through the node n, and the calculation formula is shown in formula (4):

[0029] g'(m) = g(n) + cost(n, m) (4)

[0030] Wherein, cost(n, m) is the weight of the edge between node n and node m, the value of g'(m) is compared with the value of g(m), g(m) is the original actual distance from the starting node to the node m through the node n, when g'(m) < g(m), g(m) is updated to g'(m), and the node n is set as the parent node of the node m, and the f(m) value of the node m is updated to satisfy f(m) = g(m) + h(m); in addition, when the node m is not in the open list, it is added to the open list; when the end node is found, the parent node pointer of the end node is traced back from the end node to the start node in turn, and the generated node sequence is the planned path;

[0031] Rule setting: according to the layout characteristics and production process requirements of the woven bag production workshop, a series of regional traffic rules are set in advance; according to the position and layout of different production equipment and large facilities in the workshop, the corresponding avoidance rules are set in advance;

[0032] Path determination: judging whether the path generated by the A* algorithm meets the passing rules and avoidance rules in the rule setting step, and adjusting the part that does not meet the rules to obtain a path that meets the set rules as the actual running path of the woven bag robot.

[0033] Preferably, the initial path planned for each robot in step S40 is detected for conflicts using a spatial region detection method, and the step of judging whether the woven bag production robots intersect on the planned path includes:

[0034] Space region modeling: including woven bag production robot space and path space; for the woven bag production robot space, each woven bag production robot is regarded as an entity with a certain geometric shape, and the actual size of the robot is used to determine its occupancy range in space, and the center position of the robot and the size of the robot, including length, width and height, are represented using three-dimensional coordinates; for the running path of the woven bag production robot, it is divided into a series of consecutive nodes, each node determines a space region;

[0035] Motion space prediction: according to the current running speed and direction of the woven bag production robot, the path that the robot will pass through in the future is predicted and a corresponding prediction time window is determined, and the range space of the robot motion is calculated in the prediction time window;

[0036] Intersection judgment: using the bounding box algorithm, according to the range space of the robot motion calculated in the motion space prediction step, the size of the bounding box is calculated, and by comparing the positions and sizes of the bounding boxes of different robots, when there is overlap in any dimension in the three-dimensional space, it is judged that the robots have intersection in the future motion space.

[0037] Preferably, the step S40 corrects the path by real-time detection of obstacles by sensors installed on the robot, and when a new obstacle is detected, the position information of the new obstacle is recorded and updated to the map environment in step S10, and the step S30 is repeated for real-time path correction to obtain a new woven bag production robot running path.

[0038] The sensors installed on the robot include a laser radar sensor, a vision sensor, an ultrasonic sensor and an infrared sensor.

[0039] In addition, in order to achieve the above-mentioned purpose, the application also provides a woven bag production robot path planning system, which comprises:

[0040] Woven bag production workshop map modeling module: using the grid method to model the woven bag production workshop map environment, obtaining map information and storing it in each woven bag production robot;

[0041] instruction sending and receiving module: after modeling, send task instructions through the production management system, each woven bag production robot receives the instructions and analyzes to obtain production tasks, starting point coordinates and end point coordinates information, the production task information includes grabbing, carrying and stacking;

[0042] woven bag production robot path planning module: each woven bag production robot obtains information through A* algorithm combined with rule-based path planning method according to map information and parsed instructions to plan the initial path through the starting point coordinates and the end point coordinates;

[0043] path conflict detection and correction module: the initial path planned by each robot is detected for conflict, and the path is corrected through the sensor installed on the robot to detect obstacles in real time;

[0044] The grid method is used in the woven bag production workshop map modeling module to model the woven bag production workshop map environment, the map environment includes the position and shape of each area of the workshop, the position, length and width of the passageway between the areas, the storage position of the production raw materials, the position and size of the storage container and the shelf, and the position of the obstacles in the workshop;

[0045] The space region detection method is used in the path conflict detection and correction module to detect the conflict of the initial path planned by each robot, to judge whether there is an intersection of the woven bag production robot on the planned path, when there is an intersection, it means that the robots exist collision or mutual interference operation, according to the priority order of the robots or the actual situation of the workshop, the path planning method in the woven bag production robot path planning module is adjusted to re-plan the running path.

[0046] In addition, in order to achieve the above purpose, the application also provides a woven bag production robot path planning device, which comprises a memory, a processor and a A* algorithm combined with rule-based path planning algorithm and the like program stored on the memory and executable on the processor, wherein the A* algorithm combined with rule-based path planning algorithm and the like program is used to realize the steps of the woven bag production robot path planning method as described above.

[0047] Preferably, in order to achieve the above purpose, the application also provides a computer program product, which comprises a A* algorithm combined with rule-based path planning algorithm and the like program, wherein the A* algorithm combined with rule-based path planning algorithm and the like program is executed by the processor to realize the woven bag production robot path planning method as described above.

[0048] The advantages and effects of the application are as follows:

[0049] The application provides a woven bag production robot path planning method and system, which realizes accurate woven bag production robot running path planning by combining path planning, conflict detection and real-time path correction, adopts an A* algorithm combined with a rule-based path planning method, sets priorities for the production urgency of the woven bag production robot, adjusts the path in real time according to actual production requirements, optimizes the operation process, and improves the applicability and flexibility of the woven bag production robot path planning. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0051] Figure 1 It is a flowchart of a woven bag production robot path planning method of the present application.

[0052] Figure 2 It is a structure schematic diagram of a woven bag production robot path planning system of the present application.

[0053] Figure 3 It is a structure schematic block diagram of a woven bag production robot path planning electronic device of the present application. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0055] The present application provides a woven bag production robot path planning method, the composition of the method is as shown in Figure 1 The method comprises the following steps:

[0056] Step S10: The grid method is used to model the woven bag production workshop map environment, and map information is obtained and stored in each woven bag production robot.

[0057] In step S10, the grid method is used to model the woven bag production workshop map environment, and the map environment includes the position and shape of each area in the workshop, the position, length and width of the passageway between the areas, the storage position of the production raw materials, the position and size of the storage container and shelf, and the position of the obstacles in the workshop.

[0058] Specifically, the step of modeling the woven bag production workshop environment in step S10 using the grid method includes:

[0059] Determining the grid size and grid area: according to the size of the woven bag production workshop and the operation precision requirements of the woven bag production robot, the size of the grid is determined, when the area of the workshop is large and the operation precision requirement of the robot is low, a larger grid size can be selected, such as a square grid with a side length of 0.5 meters to 1 meter; when the area of the workshop is small and the operation precision requirement of the robot is high, such as the printing, sewing and other fine processing links of woven bags, a smaller grid size is required, such as a grid with a side length of 0.1 meters to 0.3 meters; the planar space of the woven bag production workshop is divided into regular grids of the same size according to the determined grid size, for example, for a rectangular workshop, the grids can be divided row by row and column by column from a corner of the workshop, each grid has a unique coordinate identifier, and a two-dimensional coordinate (row number, column number) is used to represent it;

[0060] Map information labeling: through on-site scanning and measurement and manual marking of the woven bag production workshop, the position and range of obstacles in the workshop are determined, when there are obstacles in the grid, such as equipment, pillars, accumulated raw materials or finished products, etc., the grid is marked as "obstacle grid", and a binary value "1" is used to represent the presence of obstacles; when there are no obstacles in the grid, the grid is marked as "passing area grid", and a binary value "0" is used to represent the passing area; when there are special areas in the grid, such as high-temperature, high-pressure and other dangerous areas and high-precision operation areas, such as woven bag processing areas with extremely high requirements for robot movement precision, etc., the grid is marked as "special area grid", and a special area is represented by an identifier or color coding;

[0061] Map information storage: a two-dimensional array is used to store the grid map information, each element in the array corresponds to a grid, and the array is stored in the storage unit of each woven bag production robot; in addition to storing the grid information, some other information related to path planning can also be stored in the storage unit of each robot, such as the estimated distance from each grid to the target position (such as the finished product storage area or the next processing station), the passing distance and time to the next grid, etc., which can be used in subsequent path planning to improve the efficiency and accuracy of path planning;

[0062] Map updating mechanism: the woven bag production workshop is rescanned every 1 hour to update the workshop map information; in addition, an event-driven updating mechanism is set up, which automatically updates the grid map when events that affect the map environment occur in the workshop, such as the addition of production equipment, material accumulation due to equipment failure, etc.

[0063] Step S20: After modeling is completed, send task instructions through the production management system, and each woven bag production robot receives the instructions and parses the production task, starting point coordinates and end point coordinates information. The production task information includes grabbing, transporting and stacking.

[0064] Specifically, the task instructions sent by the production management system after modeling is completed in step S20 include:

[0065] Raw material grabbing instructions: When the production task starts, the robot is instructed to grab the raw materials needed for woven bag production, such as plastic particles and fiber filaments, from a specific location in the raw material storage area. The instruction includes specific location information of the raw material storage area, including the starting point location determined by area number, shelf location, and layer number and bin position on the shelf, etc. The end point location is near the feed inlet of the processing equipment (such as a drawing machine or a weaving machine), and this location information is also accurate to the specific device coordinates and feed inlet direction;

[0066] Finished product grabbing instructions: After the woven bag is processed, the robot receives the instruction to grab the finished woven bag. The starting point location is the output location of the finished product on the production line, such as a specific location on the conveyor belt after the sewing process or a temporary storage area after quality testing. The end point location is a designated storage location in the finished product storage area, including storage shelf number, layer number and specific bin position, etc. to accurately place the finished product in the right place;

[0067] Inter-process transportation instructions: During the production of woven bags, the robot needs to transport the woven bags from one processing equipment to another, such as from the weaving machine to the printing machine for pattern printing. The instruction specifies the starting point of the robot as the discharge port location of the current processing equipment (weaving machine), which can be determined by the three-dimensional coordinates of the equipment and the offset of the discharge port relative to the main body of the equipment. The end point location is the feed inlet location of the next processing equipment (printing machine), which also has accurate coordinates and direction information;

[0068] Raw material stacking instructions: The starting point of raw material stacking is the raw material unloading area or the temporary storage area after preliminary processing. The instruction specifies the stacking rules of raw materials, such as partitioned stacking of different types of raw materials and maximum stacking height of each type of raw material. The end point location is a designated area in the raw material warehouse, determined by the layout coordinates and area division of the warehouse;

[0069] Finished product stacking instruction: when the robot stacks the finished product woven bag, the starting point position given by the instruction is the position of the finished product conveyor belt or temporary storage area, which can be determined by the area coordinates of the workshop and the specific position of the conveyor belt, and the end point position is the stacking position of the finished product storage area, including the number of layers, the number of woven bags placed in each row and column, and the overall shape of the stacking (such as cuboid, cube, etc.) and other information, so that the robot can stack according to the specified manner;

[0070] Task priority instruction: according to the urgency of production, the busy degree of equipment and other factors, the priority of each task is assigned, and the robot obtains the priority information of the task when receiving the task, for example, the woven bag production task required for urgent production is assigned a higher priority, and the robot will give priority to the path of high-priority tasks when planning the path, and when the path conflicts, the robot path corresponding to the low-priority task is adjusted first;

[0071] Task time window instruction: each task has a time window requirement, for example, in a certain woven bag processing process, the robot needs to carry the woven bag from one device to another within a certain time period to ensure the continuity of the production process, and this time window can be clearly defined by the start time and end time. When planning the path, the robot needs to consider its own movement speed and distance to ensure that the task can be completed within the specified time window. If the time requirement cannot be met, the robot needs to adjust the path according to the task requirements.

[0072] Step S30: Each woven bag production robot obtains information from the map information and the parsed instructions, and plans an initial path through the starting point coordinates and the end point coordinates through the A* algorithm combined with the rule-based path planning method.

[0073] Specifically, the step of planning an initial path through the starting point coordinates and the end point coordinates in step S30 through the A* algorithm combined with the rule-based path planning method includes:

[0074] Graph structure conversion: convert the modeled woven bag production workshop grid map into a graph structure, and each grid is regarded as a node in the graph. When two adjacent grids are both "passable area grids" in the map, an edge is established between the corresponding two nodes. The edge has a weight, and the Euclidean distance between the centers of two nodes (x1, y1) and (x2, y2) is calculated as the weight of the edge, and the calculation formula is shown as formula (1):

[0075]

[0076] wherein d is the calculated Euclidean distance, x1 and x2 are the horizontal coordinates of the two nodes, and y1 and y2 are the vertical coordinates of the two nodes.

[0077] Determine heuristic function: the heuristic function h(n) is used to calculate the distance from the current node n(x n ,y n ) to the end node(x goal ,y goal ), the calculation formula is shown in equation (2):

[0078]

[0079] Wherein, x n is the horizontal coordinate of the current node n, y n is the vertical coordinate of the current node n, x goal is the horizontal coordinate of the end node, y goal is the vertical coordinate of the end node;

[0080] Initialize data structure: initialize the open list and the closed list, the open list is used to store the start node, initially the start node is put into the open list; the closed list is used to store the nodes searched by the algorithm in the map, initially the closed list is empty;

[0081] Path search: select node n from the open list, n is the current node, search the path centered on the selected node, node n satisfies equation (3):

[0082] f(n)=g(n)+h(n) (3)

[0083] Wherein, f(n) is used to evaluate the priority of node n in path search, g(n) is the actual distance from the start node to the current node n, h(n) is the distance from the current node n to the end node calculated by the heuristic function; for the current node n, judge whether it is the end node, when node n is the end node, backtrack through the parent node pointer of node n, from the end node to the start node in turn, the generated node sequence is the planned path; when node n is not the end node, it is put into the closed list, calculate the new actual distance g'(m) from the start node through node n to node m, the calculation formula is shown in equation (4):

[0084] g'(m)=g(n)+cost(n,m) (4)

[0085] wherein cost(n,m) is the weight of the edge between node n and node m, the value of g'(m) is compared with the value of g(m), g(m) is the original actual distance from the starting node to node m through node n, when g'(m) < g(m), g(m) is updated as g'(m), and node n is set as the parent node of node m, and the f(m) value of node m is updated to satisfy f(m) = g(m) + h(m); in addition, when node m is not in the open list, it is added to the open list; when the terminal node is found, the parent node pointer of the terminal node is traced back from the terminal node to the starting node in sequence, and the generated node sequence is the planned path;

[0086] Rule setting: according to the layout characteristics and production process requirements of the woven bag production workshop, a series of area passing rules are set in advance; according to the position and layout of different production equipment and large facilities in the workshop, the corresponding avoidance rules are set in advance;

[0087] Path determination: whether the path generated by the A* algorithm meets the passing rules and avoidance rules in the rule setting step is judged, and the part that does not meet the setting rules is adjusted to obtain a path that meets the setting rules as the actual running path of the woven bag robot.

[0088] Step S40: Conflict detection is performed on the initial path planned for each robot, and the path is corrected in real time by detecting obstacles through sensors installed on the robot.

[0089] The conflict detection on the initial path planned for each robot in step S40 adopts a space region detection method to determine whether there is an intersection on the planned path of the woven bag production robot, and when there is an intersection, it indicates that the robots exist collision or mutual interference, and the path planning method in step S30 is adjusted according to the priority order of the robots or the actual situation of the workshop to re-plan the running path.

[0090] Specifically, the conflict detection on the initial path planned for each robot in step S40 adopts a space region detection method to determine whether there is an intersection on the planned path of the woven bag production robot, and the step includes:

[0091] Space region modeling: including woven bag production robot space and path space; for the woven bag production robot space, each woven bag production robot is regarded as a solid entity with a certain geometric shape, and the occupied range of the robot in space is determined according to the actual shape and size of the robot, and the center position of the robot and the size of the robot, including length, width and height, are represented by three-dimensional coordinates; for the running path of the woven bag production robot, it is divided into a series of continuous nodes, and each node determines a space region;

[0092] Motion space prediction: according to the current running speed and direction of the woven bag production robot, the path that the robot will pass through in the future is predicted, and a corresponding prediction time window is determined, and the range space of the robot motion is calculated in the prediction time window;

[0093] Intersection judgment: using the bounding box algorithm, the size of the bounding box is calculated according to the range space of the robot motion calculated in the motion space prediction step, and the positions and sizes of the bounding boxes of different robots are compared. When there is an overlap in any dimension in the three-dimensional space, it is judged that there is an intersection in the future motion space of the robot.

[0094] Among them, the sensor installed on the robot detects the obstacle in real time to correct the path, and when a new obstacle is detected, the position information of the new obstacle is recorded and updated to the map environment in step S10. Repeat step S30 to correct the path in real time to obtain a new woven bag production robot running path; The sensor installed on the robot includes a laser radar sensor, a visual sensor, an ultrasonic sensor and an infrared sensor, etc.

[0095] In addition, the application also provides a woven bag production robot path planning system, please refer to Figure 2 , the woven bag production robot path planning system comprises:

[0096] Woven bag production workshop map modeling module: the map environment of the woven bag production workshop is modeled by using the grid method, and the map information is obtained and stored in each woven bag production robot;

[0097] Instruction sending and receiving module: after modeling, send task instructions through the production management system, and each woven bag production robot receives the instructions and analyzes to obtain production task, starting point coordinate and ending point coordinate information, and the production task information includes grabbing, carrying and stacking;

[0098] Woven bag production robot path planning module: each woven bag production robot obtains information according to the map information and the analyzed instructions, and plans the initial path passing through the starting point coordinate and the ending point coordinate through A* algorithm combined with the rule-based path planning method;

[0099] Path conflict detection and correction module: the initial path planned by each robot is detected for conflict, and the path is corrected by real-time detection of obstacles through the sensor installed on the robot;

[0100] Among them, the woven bag production workshop map modeling module models the map environment of the woven bag production workshop by using the grid method, and the map environment includes the position and shape of each area in the workshop, the position, length and width of the passageway between the areas, the storage position of the production raw materials, the position and size of the storage container and the shelf, and the position of the obstacles in the workshop.

[0101] The path conflict detection and correction module detects the initial path planned for each robot using a space region detection method to determine whether there is an intersection on the planned path of the woven bag production robot, and when there is an intersection, it indicates that the robots may collide or interfere with each other. According to the priority order of the robots or the actual situation of the workshop, the path planning method in the woven bag production robot path planning module is adjusted to re-plan the running path.

[0102] The woven bag production robot path planning system provided by the present application adopts the woven bag production robot path planning method in the above embodiment, which can solve the technical problems of low production efficiency, low path planning applicability and insufficient flexibility of the woven bag production robot. Compared with the prior art, the woven bag production robot path planning system provided by the present application has the same beneficial effects as the woven bag production robot path planning method provided by the above embodiment, and the other technical features of the woven bag production robot path planning system are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0103] The present application provides a woven bag production robot path planning device, which comprises at least one processor and a memory in communication with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the woven bag production robot path planning method in the above embodiment one.

[0104] Reference will now be made to Figure 3 which shows a structure diagram of a woven bag production robot path planning device suitable for implementing the embodiments of the present application. The woven bag production robot path planning device in the embodiments of the present application can include but is not limited to mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players) and the like, and fixed terminals such as digital TVs, desktop computers and the like. Figure 3 The woven bag production robot path planning device shown is only an example and should not limit the functions and use range of the embodiments of the present application.

[0105] Figure 3The woven bag production robot path planning apparatus shown can include a processing system 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read only memory (ROM) 1002 or a program loaded from a storage system 1003 into a random access memory (RAM) 1004. In the RAM 1004, various programs and data required for the operation of the woven bag production robot path planning apparatus are also stored. The processing system 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input system 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output system 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the storage system 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication system 1009. The communication system 1009 can allow the woven bag production robot path planning apparatus to communicate with other devices wirelessly or by wire to exchange data. Although the woven bag production robot path planning apparatus having various systems is shown in the figure, it should be understood that all the systems shown are not required to be implemented or possessed. More or less systems can be alternatively implemented or possessed.

[0106] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network through a communication system, or installed from the storage system 1003, or installed from the ROM 1002. When the computer program is executed by the processing system 1001, the above-mentioned functions defined in the methods of embodiments of the present disclosure are performed.

[0107] The application provides a woven bag production robot path planning device, which adopts the woven bag production robot path planning method in the above embodiment, and can solve the technical problems of low production efficiency, low path planning applicability and insufficient flexibility of the woven bag production robot. Compared with the prior art, the woven bag production robot path planning device provided by the application has the same beneficial effects as the woven bag production robot path planning method provided by the above embodiment, and other technical features in the woven bag production robot path planning device are the same as the features disclosed in the previous embodiment method, which will not be repeated here.

[0108] Parts of the application can be implemented in hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0109] The application also provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to realize the steps of the woven bag production robot path planning method.

[0110] The computer program product provided by the application can solve the technical problems of low production efficiency, low path planning applicability and insufficient flexibility of the woven bag production robot. Compared with the prior art, the computer program product provided by the application has the same beneficial effects as the woven bag production robot path planning method provided by the above embodiment, which will not be repeated here.

[0111] The above only describes some embodiments of the application, and does not limit the patent scope of the application. Any equivalent structural transformation, direct / indirect application in other related technical fields based on the technical concept of the application, and the content of the specification and drawings are included in the patent protection scope of the application.

Claims

1. A path planning method for a woven bag production robot, characterized in that: The method comprises the following steps: Step S10: Modeling the map environment of the woven bag production workshop using a grid method, obtaining map information and storing it in each woven bag production robot; Step S20: After the modeling is completed, the production management system sends a task instruction. Each woven bag production robot receives the instruction and parses it to obtain the production task, starting point coordinates, and end point coordinates. The production task information includes grasping, handling, and palletizing. Step S30: Each woven bag production robot plans its own initial path passing through the starting point coordinates and the end point coordinates based on the map information and the information obtained from the parsed instructions using the A* algorithm combined with a rule-based path planning method; Step S40: performing collision detection on the initial path planned by each robot, and performing path correction by detecting obstacles in real time through sensors installed on the robot; In step S10, the grid method is used to model the map environment of the woven bag production workshop. The map environment includes the location and shape of each area of ​​the workshop, the location, length, and width of the passages between the areas, the storage location of the production raw materials, the location and size of the storage containers and shelves, and the location of obstacles in the workshop; The steps of planning the initial paths passing through the starting point coordinates and the end point coordinates by using the A* algorithm combined with the rule-based path planning method in step S30 include: Graph structure conversion: The grid map of the modeled woven bag production workshop is converted into a graph structure. Each grid is regarded as a node in the graph. When two adjacent grids are both "passable area grids" in the map, an edge is established between the corresponding two nodes. The edge has a weight, and the Euclidean distance between the centers of the two nodes (x1, y1) and (x2, y2) is calculated as the edge weight. The calculation formula is shown in formula (1): Where d is the calculated Euclidean distance, x1 and x2 are the horizontal coordinates of the two nodes, and y1 and y2 are the vertical coordinates of the two nodes; Determine the heuristic function: The heuristic function h(n) is used to calculate the n ,y n ) to the end node (x goal ,y goal ) is calculated as shown in formula (2): Among them, x n is the horizontal coordinate of the current node n, y n is the vertical coordinate of the current node n, x goal is the horizontal coordinate of the end node, y goal is the ordinate of the end node; Initialize the data structure: Initialize the open list and closed list. The open list is used to store the starting node. Initially, the starting node is placed in the open list. The closed list is used to store the nodes that have been searched by the algorithm in the map. Initially, the closed list is empty. Path search: Select node n from the open list, n is the current node, and search for a path with the selected node as the center. Node n satisfies formula (3): f(n)=g(n)+h(n) (3) Among them, f(n) is used to evaluate the priority of node n in the path search, g(n) is the actual distance from the starting node to the current node n, and h(n) is the distance from the current node n to the end node calculated by the heuristic function; for the current node n, determine whether it is the end node. When node n is the end node, backtrack through the parent node pointer of node n, and backtrack from the end to the starting point in sequence. The generated node sequence is the planned path; when node n is not the end node, it is placed in the closed list, and all adjacent nodes m are traversed to calculate the new actual distance g'(m) from the starting node through node n to node m. The calculation formula is shown in formula (4): g'(m)=g(n)+cost(n,m) (4) where cost(n,m) is the weight of the edge between node n and node m. Compare the value of g’(m) with the value of g(m), where g(m) is the original actual distance from the starting node through node n to node m. When g’(m) < g(m), update g(m) to g’(m), set node n as the parent node of node m, and at the same time update the f(m) value of node m to satisfy f(m) = g(m) + h(m), where h(m) is the original actual distance from the starting node through node n to node m calculated by the heuristic function; in addition, when node m is not in the open list, add it to the open list; when the end node is found, backtrack through the parent node pointer of the end node, backtracking from the end node to the starting node in turn, and the generated node sequence is the planned path; Rule setting: According to the layout characteristics and production process requirements of the woven bag production workshop, a series of area access rules are preset in advance; according to the positions and layouts of different production equipment and large facilities in the workshop, corresponding avoidance rules are preset in advance; Path determination: Judge whether the path generated by the A* algorithm conforms to the access rules and avoidance rules in the rule setting step, and adjust the non-conforming parts to obtain a path that conforms to the set rules as the actual running path of the woven bag robot; In step S40, the space area detection method is used to detect conflicts in the initial paths planned for each robot, and it is judged whether there is an intersection in the planned paths of the woven bag production robots. When there is an intersection, it means that there may be collisions or mutual interference between the robots. Adjust the path planning method in step S30 according to the priority order of the robots or the actual situation of the workshop to re-plan the running path.

2. A woven bag production robot path planning method according to claim 1, characterized in that: The steps of using the grid method to model the map environment of the woven bag production workshop in step S10 include: Determine the grid size and grid area: Determine the size of the grid according to the floor area of the woven bag production workshop and the operation accuracy requirements of the woven bag production robot, and divide the planar space of the woven bag production workshop into regular grids of the same size according to the determined grid size; Map information annotation: Determine the positions and ranges of obstacles in the workshop through on-site scanning measurement and manual marking in the woven bag production workshop. When there is an obstacle in the grid, the grid is marked as an "obstacle grid", and the binary value "1" is used to indicate the presence of an obstacle; when there is no obstacle in the grid, the grid is marked as a "passage area grid", and the binary value "0" is used to indicate the passage area; when there is a special area in the grid, the grid is marked as a "special area grid", and an identifier or color coding is used to represent the special area; Map information storage: Use a two-dimensional array to store the grid map information, each element in the array corresponds to a grid, and the array is stored in the storage unit of each woven bag production robot; Map update mechanism: Rescan the woven bag production workshop after a set period of time to update the workshop map information; in addition, set an event-driven update mechanism to automatically update the grid map when an event that affects the map environment occurs in the workshop.

3. A woven bag production robot path planning method according to claim 1, characterized in that: After the modeling is completed in step S20, the task instructions sent by the production management system include raw material grabbing instructions, finished product grabbing instructions, inter-process transportation instructions, raw material stacking instructions, finished product stacking instructions, task priority instructions and task time window instructions.

4. A woven bag production robot path planning method according to claim 1, characterized in that: In step S40, the conflict detection of the initial path planned by each robot is performed using a spatial region detection method, and the step of determining whether there is an intersection on the planned path of the woven bag production robot includes: Spatial region modeling: This includes the woven bag production robot space and path space. For the woven bag production robot space, each woven bag production robot is considered as an entity with a certain geometric shape. The range of its occupation in space is determined based on the robot's actual external dimensions. The robot's center position and dimensions, including length, width, and height, are represented using three-dimensional coordinates. The woven bag production robot's operating path is divided into a series of continuous nodes, with each node defining a spatial region. Motion space prediction: Based on the current speed and direction of the woven bag production robot, the path the robot will take in the future is predicted and a corresponding prediction time window is determined. Within the predicted time window, the range of the robot's motion is calculated; Intersection judgment: Using the bounding box algorithm, the size of the bounding box is calculated based on the range of robot movement calculated in the motion space prediction step. By comparing the positions and sizes of the bounding boxes of different robots, when there is overlap in any dimension in the three-dimensional space, it is determined that the robots have an intersection in the future motion space.

5. A woven bag production robot path planning method according to claim 1, characterized in that: In step S40, obstacles are detected in real time by sensors installed on the robot to perform path correction. When a new obstacle is detected, the location information of the new obstacle is recorded and updated to the map environment in step S10. Step S30 is repeated to perform real-time path correction to obtain a new operation path of the woven bag production robot.

6. A woven bag production robot path planning method according to claim 5, characterized in that: The sensors installed on the robot include lidar sensors, visual sensors, ultrasonic sensors and infrared sensors.

7. A woven bag production robot path planning system, characterized in that: The path planning method for a woven bag production robot according to claim 1 comprises: Woven bag production workshop map modeling module: Use the grid method to model the woven bag production workshop map environment, obtain map information and store it in each woven bag production robot; Instruction sending and receiving module: After the model is completed, the task instructions are sent through the production management system. Each woven bag production robot receives the instructions and parses them to obtain the production task, starting point coordinates, and end point coordinates. The production task information includes grasping, handling, and palletizing; Woven bag production robot path planning module: Each woven bag production robot plans its own initial path through the starting and ending coordinates based on map information and information obtained from parsing instructions using the A* algorithm combined with a rule-based path planning method; Path conflict detection and correction module: performs conflict detection on the initial path planned by each robot, and uses sensors installed on the robot to detect obstacles in real time and make path corrections; In the map modeling module of the woven bag production workshop, the raster method is used to model the map environment of the woven bag production workshop. The map environment includes the positions and shapes of each area in the workshop, the positions, lengths, and widths of the channels between areas, the storage positions of production raw materials, the positions and sizes of storage containers and shelves, and the positions of obstacles in the workshop. The steps of planning the initial paths passing through the starting coordinates and ending coordinates respectively in the path planning module of the woven bag production robot by combining the A* algorithm with the rule-based path planning method include: Graph structure conversion: Convert the raster map of the modeled woven bag production workshop into a graph structure. Each raster is regarded as a node in the graph. When two adjacent rasters are both "passable area rasters" in the map, an edge is established between the corresponding two nodes; the edge has a weight, and the Euclidean distance between the centers of two nodes (x1, y1) and (x2, y2) is calculated as the weight of the edge. The calculation formula is shown in Equation (1): where d is the calculated Euclidean distance, x1 and x2 are the abscissas of the two nodes, and y1 and y2 are the ordinates of the two nodes. Determine the heuristic function: The heuristic function h(n) is used to calculate the n ,y n ) to the end node (x goal ,y goal ) is calculated as shown in formula (2): Among them, x n is the horizontal coordinate of the current node n, y n is the vertical coordinate of the current node n, x goal is the horizontal coordinate of the end node, y goal is the ordinate of the end node; Initialize the data structure: Initialize the open list and the closed list. The open list is used to store the starting node, and the starting node is put into the open list initially; the closed list is used to store the nodes that have been searched by the algorithm in the map, and the closed list is empty initially. Path search: Select a node n from the open list. Node n is the current node. Search for a path centered on the selected node. Node n satisfies Equation (3): f(n) = g(n) + h(n) (3) where f(n) is used to evaluate the priority of node n in the path search, g(n) is the actual distance from the starting node to the current node n, and h(n) is the distance from the current node n to the ending node calculated by the heuristic function; for the current node n, judge whether it is the ending node. When node n is the ending node, trace back through the parent node pointer of node n, and trace back from the end to the start in turn. The generated node sequence is the planned path; when node n is not the ending node, put it into the closed list, traverse all adjacent nodes m, and calculate the new actual distance g’(m) from the starting node through node n to node m. The calculation formula is shown in Equation (4): g'(m) = g(n) + cost(n,m) (4) where cost(n,m) is the weight of the edge between node n and node m. Compare the value of g’(m) with the value of g(m). g(m) is the original actual distance from the starting node through node n to node m. When g’(m) < g(m), update g(m) to g’(m), set node n as the parent node of node m, and at the same time update the f(m) value of node m to satisfy f(m) = g(m) + h(m), where h(m) is the original actual distance from the starting node through node n to node m calculated by the heuristic function; in addition, when node m is not in the open list, add it to the open list; when the ending node is found, trace back through the parent node pointer of the ending node, and trace back from the end to the start in turn. The generated node sequence is the planned path. Rule setting: Based on the layout characteristics and production process requirements of the woven bag production workshop, a series of regional access rules are pre-set; based on the location and layout of different production equipment and large facilities in the workshop, corresponding avoidance rules are pre-set; Path determination: Determine whether the path generated by the A* algorithm complies with the passage rules and avoidance rules in the rule setting step, and adjust the non-compliant parts to obtain a path that complies with the set rules as the actual operation path of the bag-weaving robot; The path conflict detection and correction module performs conflict detection on the initial path planned by each robot using a spatial area detection method to determine whether there is an intersection on the planned path of the woven bag production robot. When there is an intersection, it indicates that there is a collision or mutual interference between the robots. The path planning method in the path planning module of the woven bag production robot is adjusted according to the priority order of the robots or the actual situation of the workshop to re-plan the operation path.

8. A robot path planning device for woven bag production, characterized in that: The path planning device for a woven bag production robot comprises: A memory, a processor, and an A* algorithm combined with a rule-based path planning algorithm program stored in the memory and runnable on the processor. When the A* algorithm combined with the rule-based path planning algorithm program is executed by the processor, a woven bag production robot path planning method as described in any one of claims 1 to 6 is implemented.

9. A computer program product, characterized in that The computer program product includes a program of an A* algorithm combined with a rule-based path planning algorithm. When the program of the A* algorithm combined with a rule-based path planning algorithm is executed by a processor, a path planning method for a woven bag production robot is implemented as described in any one of claims 1 to 6.

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