Method and system for planning motion path of bridge crane

Abstract

The invention belongs to the technical field of industrial crane control, and provides a bridge crane motion path planning method and system, and the method comprises the steps: uniformly dividing the motion environment of a bridge crane into grid units, recognizing a dynamic obstacle, endowing each grid unit with a state value, carrying out the track prediction of the dynamic obstacle, and carrying out the prediction of the motion path of the bridge crane. The method comprises the following steps: generating a time grid state matrix, constructing a time sequence passing grid set sequence, exploring a shortest path by adopting an improved A * algorithm, exploring multiple feasible paths by adopting an RRT algorithm, marking change nodes, screening the multiple feasible paths based on the shortest path, marking an initial path, and sorting an initial path set and a path node sequence; and quantitatively calculating the path tortuosity of the initial path, screening based on the path tortuosity, marking an optimal path, analyzing the load swing of the load at the change node, calculating the swing envelope range of the load, marking a risk change node, and screening to obtain an optimal path.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of industrial crane control, and particularly relates to a motion path planning method and system for a bridge crane. BACKGROUND

[0002] In a complex industrial production scene, a bridge crane undertakes a key task of material handling, and the scientific nature of its motion path planning is crucial for production efficiency improvement and operation safety guarantee. However, the current path planning technology gradually shows many deficiencies when facing a dynamically changing operation environment.

[0003] In the environment modeling stage, the prior art often relies on a single design parameter or rough field measurement to determine the spatial boundary, resulting in a deviation between the model and the actual operation environment, and a static grid division method is often used, which is difficult to reflect the dynamic changes of the environment over time. In the processing of obstacles, the traditional method has a fuzzy standard for distinguishing static and dynamic obstacles, lacks quantitative judgment basis, and has insufficient trajectory prediction accuracy for dynamic obstacles, which is easy to cause path failure risk due to obstacle movement.

[0004] In the path search process, the limitation of a single algorithm is significant. Although the improved A* algorithm can obtain the shortest path, the generated path has many redundant turns, which increases energy consumption and control complexity. Although the RRT algorithm can generate multiple feasible paths, it lacks a target-oriented strategy, resulting in low search efficiency. At the same time, the prior art often ignores the influence of the time dimension on the feasibility of the path, and does not establish a time-space related access constraint, so the planned path may conflict with dynamic obstacles at a future time.

[0005] In the path screening link, the prior art often only judges according to the path length or the number of turns, does not consider the standardized quantification of the path tortuosity, and is difficult to compare the smoothness of different paths horizontally. In addition, it does not analyze the collision risk combined with the load swing characteristics, which may cause accidents due to load swing exceeding the safety range. These problems make the existing path planning method difficult to meet the needs of complex industrial scenes in efficiency, safety and adaptability, and an integrated solution is needed to dynamically model the environment, search the path in space-time, and quantitatively analyze the risk.

[0006] In view of the above problems, the application provides a motion path planning method and system for a bridge crane. SUMMARY

[0007] In order to make up for the deficiencies of the prior art and solve at least one technical problem proposed in the background art.

[0008] The technical scheme adopted by the application to solve the technical problems is: a motion path planning method for a bridge crane, comprising: The bridge crane motion environment is evenly divided into grid units, dynamic obstacles are identified, and a state value is assigned to each grid unit, the trajectory of the dynamic obstacle is predicted, and a time grid state matrix including the state value of each grid unit at the corresponding time is generated; Based on the time grid state matrix, a time sequence of passing grid set including passing grid set is constructed, and an improved A* algorithm is used to explore the shortest path, an RRT algorithm is used to explore multiple feasible paths, a change node is marked, and multiple feasible paths are screened based on the shortest path, an initial path is marked, and an initial path set and a path node sequence are arranged; For the initial path, the path tortuosity of the initial path is quantitatively calculated, and the path tortuosity is screened based on the path tortuosity, the preferred path is marked, and the preferred path set is arranged; For the preferred path, the load swing of the load at the change node is analyzed, the swing envelope range of the load is calculated, the risk change node is marked, and the optimal path is screened; The state value is obtained in the following manner: A three-dimensional coordinate system is established for the bridge crane motion environment, the motion environment is evenly divided into grid units, the motion environment data is collected in real time and the obstacles are identified, a current time is set as an end point to set an obstacle analysis period, and in the obstacle analysis period, the distance between the coordinates of the grid units covered by the obstacles at adjacent time points is calculated. If the distance between each two adjacent time points meets the preset condition, the obstacle is determined to be a static obstacle, otherwise the obstacle is determined to be a dynamic obstacle; A state value is assigned to each grid unit, the state value of the grid unit not covered by any obstacle is 0, the state value of the grid unit covered by the static obstacle is 1, and the state value of the grid unit covered by the dynamic obstacle is 2; The time grid state matrix is obtained in the following manner: A future period with the current time as the starting point is set, the coordinates of the grid units covered by the dynamic obstacle at each time in the obstacle analysis period are obtained, the Kalman filtering algorithm is used to predict the motion trajectory of the dynamic obstacle in the future period, the coordinates of the grid units covered by the dynamic obstacle at each time in the future period are obtained, the state values of each grid unit at each time in the future period are arranged and converted to generate the time grid state matrix; The time sequence of passing grid set is obtained in the following manner: The path planning includes a starting grid unit, an ending grid unit and an obstacle safety distance, at each time in the future period, all grid units with a state value of 0 are obtained, marked as empty grid, and the grid units with a state value not equal to 0 are marked as obstacle grid. The minimum Euclidean distance between the empty grid and the obstacle grid is calculated, and if it is greater than the obstacle safety distance, the empty grid is marked as a passing grid and is included in the passing grid set; presetting an expected starting time for the bridge crane, clearing a passing grid set before the expected starting time, and arranging a time-sequential passing grid set sequence; The shortest path is obtained in the following way: Each grid center is regarded as a node, the start grid and the end grid are regarded as the start node and the end node respectively, the start passing grid set of the time-sequential passing grid set sequence is obtained, the improved A* algorithm is used to search the path in the start passing grid set, the basic shortest path is obtained, the line segment between the node and the non-adjacent subsequent node in the shortest path is analyzed to determine whether it is in the start passing grid set, the changed node is marked based on the analysis result, and the shortest path is obtained. The expected average speed is set for the bridge crane, the shortest path and the expected starting time are combined, the grid where the center of the spreader is located is marked as the instantaneous position, the instantaneous position of the bridge crane at each time in the time-sequential passing grid set sequence is calculated, if the instantaneous position is not in the passing grid set at the corresponding time, the node corresponding to the instantaneous position in the start passing grid set is cleared, the path is searched again, otherwise the shortest path is obtained. The path node sequence is obtained in the following way: The start passing grid set of the time-sequential passing grid set sequence is obtained, the RRT algorithm is used to search the path in the start passing grid set, a plurality of feasible paths are obtained, the line segment between the node and the non-adjacent subsequent node in the feasible path is analyzed to determine whether it is in the start passing grid set, the changed node is marked based on the analysis result, and the feasible path is simplified and updated. For any feasible path, the expected average speed and the expected starting time are combined to determine whether the instantaneous position is in the passing grid set at the corresponding time, if the instantaneous position is not in the passing grid set at the corresponding time, the feasible path is cleared. The actual lengths of the feasible path and the shortest path are calculated and data processed to obtain the length deviation of the feasible path, if the length deviation meets the preset condition, the feasible path is marked as the initial path, after all the obtained feasible paths are traversed, the shortest path is marked as the initial path, the nodes of the initial path are integrated to obtain the path node sequence of each initial path. The path tortuosity is obtained in the following way: For each initial path, the path node sequence of the initial path is obtained, the turning angle of the changed node is calculated by using the vector calculation method, the actual length of each initial path is calculated, and data processing is performed in combination with the number of changed nodes in the initial path to calculate the path tortuosity of the initial path. The preferred path is obtained in the following way: Calculate the average of the path tortuosity of each initial path in the initial path set as a tortuosity threshold, and filter the initial path set based on the tortuosity threshold, for each initial path, if the path tortuosity of the initial path is less than the tortuosity threshold, mark the initial path as a preferred path; Wherein, the acquisition method of the optimal path is: Based on the Lagrange equation, the motion equation of the load pendulum model is established, each change node in the preferred path is obtained, based on the expected average speed and the steering angle of the change node, the acceleration of the bridge crane control load in the horizontal plane x direction and y direction is calculated respectively, the load swing angle is solved by numerical integration, and the length of the steel wire rope dropped by the bridge crane is obtained and combined, and the swing envelope range of the load is obtained; Combined with the preferred path, the expected average speed and the expected starting time, the time when the instant position of the bridge crane reaches the change node is calculated, which is marked as the change time, and the change window is taken with the change time as the middle point, the overlapping part of the passing grid set in the change window is arranged into an overlapping grid set, if the swing envelope range of the load at the change node exceeds the overlapping grid set, the change node is a risk change node, and the optimal path of the preferred path with the minimum number of risk change nodes is obtained.

[0009] A bridge crane motion path planning system, comprising the following modules: An environment modeling module: uniformly dividing the motion environment of the bridge crane into grid units, identifying dynamic obstacles and assigning a state value to each grid unit, predicting the trajectory of the dynamic obstacles, and generating a time grid state matrix including the state value of each grid unit at the corresponding time; A basic planning module: constructing a time sequence passing grid set sequence including a passing grid set based on the time grid state matrix, exploring the shortest path using an improved A* algorithm, exploring multiple feasible paths using an RRT algorithm, marking change nodes and filtering multiple feasible paths based on the shortest path, marking initial paths and arranging the initial path set and path node sequence; A preliminary filtering module: for the initial path, quantitatively calculating the path tortuosity of the initial path, and filtering based on the path tortuosity, marking the preferred path and arranging it into a preferred path set; A load selection module: for the preferred path, analyzing the load swing of the load at the change node, calculating the swing envelope range of the load, marking the risk change node, and filtering to obtain the optimal path.

[0010] The beneficial effects of the present application are as follows: 1、The present application can accurately grasp the dynamic obstacle information by uniformly dividing the motion environment into grid units and generating a time grid state matrix, providing a reliable basis for path planning. The time sequence of the passing grid set sequence is constructed, combined with the improved A* algorithm and RRT algorithm, which can not only ensure that the shortest path is found, but also explore multiple feasible paths, and after screening, a more reasonable initial path is obtained, which effectively improves the comprehensiveness and accuracy of path planning and reduces the crane operation risk.

[0011] 2、The present application can remove unreasonable paths by quantitatively calculating the tortuosity of the initial path and screening, obtain a more straight preferred path, reduce the energy consumption and time cost in the crane operation process, analyze the swing of the load at the change node and mark the risk change node, and screen out the optimal path, which can greatly reduce the safety hidden danger caused by the load swing, ensure the safe and stable operation of the bridge crane, improve the overall operation efficiency and quality, and has high practical value. BRIEF DESCRIPTION OF DRAWINGS

[0012] The present application will be further described below with reference to the accompanying drawings.

[0013] Figure 1 is a step flow chart of a bridge crane motion path planning method according to an embodiment of the present application; Figure 2 is a module architecture diagram of a bridge crane motion path planning system according to an embodiment of the present application. DETAILED DESCRIPTION

[0014] In order to make the technical means, creative features, purposes and effects achieved by the present application easy to understand, the present application will be further described below with reference to the specific embodiments.

[0015] Embodiment 1:

[0016] Please refer to Figure 1 , a bridge crane motion path planning method according to an embodiment of the present application, comprising the following steps: S1: uniformly divide the bridge crane motion environment into grid units, identify dynamic obstacles and assign a state value to each grid unit, predict the trajectory of the dynamic obstacle, and generate a time grid state matrix including the state value of each grid unit at the corresponding time; Grid modeling of the bridge crane motion environment, specifically, the three-dimensional space boundary of the motion environment is determined based on the physical motion limit of the bridge crane, the three-dimensional space boundary includes the length boundary, the width boundary and the height boundary, the length boundary is the limit position of the two ends of the trolley track, the width boundary is the motion limit of the trolley on the trolley track, and the height boundary is the lowest position and the highest position of the lifting appliance, the three-dimensional space boundary is obtained through the motion limit parameters in the bridge crane design manual and the actual space size obtained by the on-site laser scanning; Establish a three-dimensional coordinate system for the motion environment, divide the motion environment evenly into several grid units, and use the coordinates of the center point of each grid unit in the three-dimensional coordinate system as the coordinates of the grid unit, marked as ; The system uses a laser radar point cloud and high-definition camera fusion method to collect motion environment data in real time. The motion environment data includes laser point cloud data and motion environment images. Based on the motion environment data, obstacles are identified in the motion environment and the 3D outlines of the identified obstacles are projected into the corresponding grid cells. An obstacle analysis period is set with the current time as the end point. During this period, obstacles identified based on the motion environment data are analyzed. For any obstacle in this period, the distance between the coordinates of the grid cells covered by the obstacle at adjacent moments is calculated and marked as the obstacle's position change. If the position change of the obstacle at every two adjacent moments in the obstacle analysis period is less than or equal to the preset position change threshold, the obstacle is judged to be a static obstacle; otherwise, the obstacle is judged to be a dynamic obstacle; For example, static obstacles include plant columns, fixed shelves, ground equipment, etc., and dynamic obstacles include other cranes, forklifts, personnel, etc. It should be noted that neither static obstacles nor dynamic obstacles include the bridge crane itself and the load it transports; Assign a state value to each grid cell ∈{0,1,2}, The coordinates are The state value of the grid cell, for the grid cell not covered by any obstacles, the state value of the grid cell =0, for the grid cells covered by static obstacles, the state value of the grid cells , for the grid cells covered by dynamic obstacles, the state value of the grid cell ,The state value of the grid cell can be transformed as the dynamic obstacle moves over time; To predict the trajectory of dynamic obstacles, a future time period is set starting from the current moment. The duration of the future time period is the expected maximum passage time of the bridge crane in the motion environment. The coordinates of the grid cells covered by the dynamic obstacle at each moment in the obstacle analysis period are obtained. The Kalman filter algorithm is used to predict the motion trajectory of the dynamic obstacle in the future time period, and the coordinates of the grid cells covered by the dynamic obstacle at each moment in the future time period are obtained. The state values of each grid cell at each time in the future period are arranged and converted to generate a time grid state matrix M(t, g), wherein t represents the tth time in the future period, t e [1, T], T represents the total number of times in the future period, and the matrix element represents the state value of the grid cell g at the tth time in the future period, and the coordinates of the grid cell g are ; It should be noted that the role of this step is to construct a space-time dynamic model of the motion environment of the bridge crane, to provide accurate environmental data support for subsequent path planning, to introduce Kalman filtering to predict the trajectory of the dynamic obstacle, and to generate a time grid state matrix, to realize the fusion modeling of spatial grid and time sequence, to break through the limitations of traditional static environment modeling, and to lay a foundation for path planning; S2: based on the time grid state matrix, a time sequence of passing grid set sequence is constructed, and an improved A* algorithm is used to explore the shortest path, an RRT algorithm is used to explore multiple feasible paths, a change node is marked, and multiple feasible paths are screened based on the shortest path, an initial path is marked, and an initial path set and a path node sequence are arranged; Based on the obtained time grid state matrix, the bridge crane is preliminarily planned; Specifically, the path planning basic parameters are set, the path planning basic parameters include the starting grid cell, the ending grid cell and the obstacle safety distance d, wherein the starting grid cell is the grid cell where the center of the hoist of the bridge crane is located at the current time, the ending grid cell is the grid cell where the center of the hoist is located when the bridge crane delivers the load to the specified position, the grid cell where the center of the hoist is located is marked as the instant position during the delivery of the load, and the obstacle safety distance d is preset by the person skilled in the art based on the parameters of the bridge crane itself and the volume of the load; Based on the state value of the grid cell and the obstacle safety distance d, a time sequence of passing grid set sequence is constructed; Specifically, at each time in the future period, all grid cells with a state value of 0 are obtained, marked as empty grids, and grid cells with a state value other than 0 are marked as obstacle grids, and the minimum Euclidean distance between the empty grids and the obstacle grids is calculated, if the minimum Euclidean distance is greater than the obstacle safety distance, it is judged that the empty grid can pass, the empty grid is marked as a passing grid and is included in the passing grid set; The time sequence of passing grid set sequence is obtained by arranging the passing grid set at each time in the future period according to the time sequence, and the expected starting time of the bridge crane is preset, the passing grid set before the expected starting time is removed in the time sequence of passing grid set sequence, and after the removal, the time sequence of passing grid set sequence takes the passing grid set at the expected starting time as the starting point; The starting passing grid set of the time sequence of passing grid set sequence is obtained, and an improved A* algorithm is used to search for a path in the starting passing grid set; Specifically, the center point of each grid unit is regarded as a node, where the starting grid unit and the ending grid unit are the starting node and the ending node respectively, and the cost function is set : ; Where n represents the node, It represents the actual path length from the starting node to the current node n, which is obtained by accumulating the Euclidean distances between the adjacent nodes from the starting node to the current node n. is the heuristic function, which represents the estimated cost from the current node n to the end node, obtained by calculating the Euclidean distance between node n and the end node; Create an open list and a closed list. The open list is used to store the nodes to be explored. Initially, it only contains the starting node. The closed list is used to store the nodes that have been explored. It is initially empty. Create a predecessor node matrix prev to record the previous node of each node on the basic shortest path. Initially, all elements are set to null. Select a cost function from an open list The grid cell with the smallest value is used as the current node and is moved from the open list to the closed list. For all adjacent nodes m of the current node n, if node m does not belong to the starting pass grid set or is already in the closed list, node m is skipped. If node m is not skipped and is not in the open list, the cost function of node m is calculated and m is added to the open list. After traversing all adjacent nodes m, the node m with the smallest cost function is selected from the open list as the current node and is moved from the open list to the closed list. If the current node is the end node, the path is traced back through the predecessor node matrix prev, the algorithm ends, and the basic shortest path is obtained; Determine in sequence whether the line segment between the starting node and the non-adjacent subsequent node in the basic shortest path is within the starting point passable grid set. For any non-adjacent subsequent node, if both are in the set, remove the node between the starting node and the non-adjacent subsequent node in the basic shortest path, and continue analyzing the next non-adjacent subsequent node until a portion of the line segment between the starting node and the non-adjacent subsequent node exceeds the starting point passable grid set. Mark this non-adjacent subsequent node as a changed node, and continue analyzing the non-adjacent subsequent nodes of the changed node with the changed node as the starting point to find subsequent changed nodes until the end node is reached. This simplifies the basic shortest path structure and obtains the shortest path. It should be noted that the change node represents the node where the angle needs to be changed in the planned path; The expected average speed is set for the bridge crane, the shortest path is obtained in combination with the expected starting time, the instantaneous positions of the bridge crane at each time on the time sequence passing grid set sequence are calculated, it is judged whether the instantaneous positions are in the passing grid set at the corresponding time, if not, the node corresponding to the instantaneous position is cleared in the starting passing grid set of the time sequence passing grid set sequence, the path search is re-performed based on the starting passing grid set after the clearance, if all the instantaneous positions are in the passing grid set at the corresponding time, the shortest path is obtained, and the actual length of the shortest path is calculated; The starting passing grid set of the time sequence passing grid set sequence is obtained, the rapid exploration random tree (RRT) algorithm is used to perform path search in the starting passing grid set, specifically, the starting node is taken as a root node, a random tree T is initialized, and only the starting node and the parent node index are included in the tree, a node is randomly sampled in all the most edge nodes of the starting passing grid set and is marked as a sampling node, in order to improve the efficiency of the expansion to the terminal point, a target bias strategy is introduced, that is, there is a probability p of directly taking the terminal node as a sampling point; The node closest to the sampling point in the random tree is found in terms of the Euclidean distance and is marked as a nearest node, a new node is expanded from the nearest node to the sampling point, the expansion step is set according to the grid size, it is checked whether there is a node between the line segment from the nearest node to the new node that does not belong to the starting passing grid set, if not, the new node is determined as a feasible node, otherwise, the new node is discarded, the feasible node is added to the random tree, and the parent node thereof is recorded as the nearest node; If the new node is the terminal node, it is considered that the terminal point has been reached, the feasible path is obtained by backtracking the parent nodes, the RRT algorithm is independently run multiple times, the sampling points are different each time, and different feasible paths are obtained; Similarly, it is sequentially judged whether the line segment between the starting node and the non-adjacent subsequent node in the feasible path is in the starting passing grid set, for any non-adjacent subsequent node, if all are in, the nodes between the starting node and the non-adjacent subsequent node in the feasible path are cleared, and the next non-adjacent subsequent node is continuously analyzed until the line segment between the starting node and the non-adjacent subsequent node exists and exceeds the starting passing grid set, the non-adjacent subsequent node is marked as a changed node, the changed node is taken as the starting point, the non-adjacent subsequent node of the changed node is continuously analyzed, the subsequent changed node is searched, and the terminal node is reached, the feasible path structure is simplified, and multiple feasible paths are obtained; For any feasible path, the instantaneous positions of the bridge crane at each time on the time sequence passing grid set sequence are calculated in combination with the obtained feasible path, the expected average speed and the expected starting time, it is judged whether the instantaneous positions are in the passing grid set at the corresponding time, if all the instantaneous positions are in the passing grid set at the corresponding time, it is judged that the feasible path can be applied, the feasible path is retained, otherwise, the feasible path is cleared; The actual length of the feasible path is calculated, and the absolute value of the difference between the actual length of the shortest path is obtained. If the length deviation is less than the preset length deviation threshold, the feasible path is marked as an initial path and is included in the initial path set, otherwise the feasible path is cleared. After traversing all the obtained feasible paths, the shortest path is marked as an initial path and is included in the initial path set. Each initial path only contains a start node, a change node and an end node. The start node, the change node and the end node can be classified as path nodes. The path nodes of each initial path are integrated in sequence to obtain the path node sequence of each initial path. It should be noted that the purpose of this step is to search and select the initial path set that meets the safety and efficiency requirements based on the time grid state matrix and the dynamic environment constraints. The improved A* algorithm simplifies the path structure by identifying the change node. The construction of the time sequence passing grid set sequence integrates the time dimension into the path search, which upgrades the path planning from static space obstacle avoidance to space-time dynamic obstacle avoidance, ensuring that the path is in a safe grid at each time. The improved A* algorithm and the RRT algorithm are combined to consider the path efficiency and robustness, overcoming the limitations of a single algorithm. S3: For the initial path, the path tortuosity of the initial path is quantitatively calculated, and the path tortuosity is used for screening to mark the preferred path and arrange the preferred path set. Specifically, for each initial path, the path node sequence of the initial path is obtained, and a vector calculation method is used. For three consecutive path nodes 、 、 of the initial path, the vectors between each two adjacent nodes are calculated. The vector direction is from the previous node to the next node. The cosine value of the included angle between the two vectors is calculated by vector dot product . Based on the obtained cosine value of the included angle, the included angle between the two vectors is calculated , and the turning angle of the change node is marked. For each initial path, the number of change nodes num contained in the initial path is counted, and the turning angle of each change node is calculated . The normalized turning angle of each change node after normalization processing is obtained by normalizing all turning angles of each initial path in the initial path set , , wherein represents the normalized turning angle of the poi-th change node in the corresponding initial path. The actual length of each initial path is calculated, and the normalized actual length of each initial path after normalization processing is obtained by normalizing the actual length of each initial path in the initial path set , wherein rou represents the number of the initial path. calculating the path tortuosity of the initial path , the formula is: ; It should be noted that the role of path tortuosity is to standardize the tortuosity of paths of different lengths, so that the tortuosity between initial paths is horizontally comparable; The average of the path tortuosity of each initial path in the initial path set is calculated as the tortuosity threshold, and the initial path set is filtered based on the tortuosity threshold. For each initial path, if the path tortuosity of the initial path is less than the tortuosity threshold, the initial path is marked as a preferred path and is included in the preferred path set; It should be noted that the role of this step is to filter out paths with lower tortuosity from the initial path set, reduce the steering energy consumption and load swing risk in the operation of the crane, and through the quantification of the steering angle and length of the path, the path tortuosity index is constructed, the horizontal comparison and scientific screening of different paths are realized, the steering angle of the continuous path node is calculated by using the vector calculation method, the local tortuosity characteristics of the path are accurately quantified, and through the normalization processing and the path tortuosity formula, the standardized comparison of paths of different lengths and different steering characteristics is realized, breaking through the one-sidedness of traditional length or steering times screening; S4: For the preferred path, analyze the load swing at the change node, calculate the swing envelope range of the load, mark the risk change node, and select the optimal path; Based on the Lagrange equation, a load pendulum model is established, and the motion equation of the load pendulum model is: ; Among them, represents the load swing angle, represents the second derivative of the load swing angle, represents the swing inertia force, LG represents the length of the steel wire rope dropped by the bridge crane, represents the load mass, c represents the air damping coefficient, and respectively represent the acceleration of the bridge crane in the horizontal x direction and the y direction to control the load; Based on any preferred path, each change node in the preferred path is obtained, based on the expected average speed and the steering angle of the change node, the acceleration of the bridge crane in the horizontal x direction and the y direction to control the load is calculated respectively and , the load swing angle is solved by numerical integration, and the swing envelope range of the load is obtained in combination with the length of the steel wire rope LG dropped by the bridge crane; The time when the instant position of the bridge crane reaches the change node is calculated, which is marked as a change time, a change window is taken with the change time as a middle point, a passing grid set at each time in the change window is obtained based on the time sequence passing grid set sequence, the overlapping parts of the passing grid sets in the change window are sorted into an overlapping grid set, whether the swing envelope range of the load on the change node exceeds the overlapping grid set is judged, if it exceeds, it is judged that there is a collision risk, and the change node is a risk change node; For each preferred path, the number of risk change nodes in the preferred path is obtained, and the preferred path is sorted from small to large according to the number of risk change nodes to obtain a preferred path sequence, and the first preferred path of the preferred path sequence is the optimal path found in the path planning, and the movement path of the bridge crane is planned; It should be noted that the role of this step is to analyze the collision risk of the load and the obstacle in the preferred path in combination with the dynamic swing characteristics of the load, and finally select the path with the lowest risk, calculate the swing range by establishing a load simple pendulum model, and verify the safety in combination with the overlapping grid set of the change window to ensure that the path has no collision in actual operation, and the change window and the overlapping grid set are introduced to match the load swing range and the obstacle grid at different times in space-time, realize the double collision risk analysis of the dynamic load swing and the dynamic obstacle, and upgrade the path safety to the overall system safety containing the load; The technical scheme of the embodiment of the application is as follows: the movement environment of the bridge crane is uniformly divided into grid units, dynamic obstacles are identified and a state value is assigned to each grid unit, the trajectory of the dynamic obstacle is predicted, a time grid state matrix including the state value of each grid unit at the corresponding time is generated, a time sequence passing grid set sequence including passing grid sets is constructed based on the time grid state matrix, the improved A* algorithm is used to explore the shortest path, the RRT algorithm is used to explore multiple feasible paths, the change nodes are marked and the multiple feasible paths are screened based on the shortest path, the initial path is marked and the initial path set and the path node sequence are sorted, for the initial path, the path tortuosity of the initial path is quantitatively calculated, and the screening is performed based on the path tortuosity, the preferred path is marked and sorted into a preferred path set, and for the preferred path, the load swing of the load at the change node is analyzed, the swing envelope range of the load is calculated, the risk change node is marked, and the optimal path is screened.

[0017] Embodiment 2:

[0018] As shown in Figure 2 A bridge crane movement path planning system according to an embodiment of the application includes the following modules: The environment modeling module: divides the moving environment of the bridge crane into grid units uniformly, identifies dynamic obstacles, and assigns a state value to each grid unit, performs trajectory prediction on the dynamic obstacles, and generates a time grid state matrix including the state value of each grid unit at the corresponding time; The basic planning module: constructs a time sequence of passing grid set sequences including passing grid sets based on the time grid state matrix, explores the shortest path using the improved A* algorithm, explores multiple feasible paths using the RRT algorithm, marks the change nodes and screens the multiple feasible paths based on the shortest path, marks the initial path and arranges the initial path set and path node sequence; The preliminary screening module: for the initial path, quantitatively calculates the path tortuosity of the initial path, and screens based on the path tortuosity, marks the preferred path and arranges it into a preferred path set; The load selection module: for the preferred path, analyzes the load swing of the load at the change node, calculates the swing envelope range of the load, marks the risk change node, and screens to obtain the optimal path.

[0019] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for planning a motion path for a bridge crane, characterized by: include: The bridge crane motion environment is evenly divided into grid cells, dynamic obstacles are identified and a state value is assigned to each grid cell, the trajectory of the dynamic obstacles is predicted, and a time grid state matrix including the state value of each grid cell at the corresponding moment is generated; Based on the time grid state matrix, a time-series pass grid set sequence including a pass grid set is constructed, and the improved A* algorithm is used to explore the shortest path. The RRT algorithm is used to explore multiple feasible paths. The changed nodes are marked and multiple feasible paths are screened based on the shortest path. The initial path is marked and the initial path set and path node sequence are sorted. For the initial path, the path tortuosity of the initial path is quantitatively calculated, and based on the path tortuosity, the path is screened, the preferred path is marked and organized into a preferred path set; For the optimal path, the load swing at the change node is analyzed, the load swing envelope range is calculated to mark the risk change node, and the optimal path is screened.

2. A method for planning a motion path of a bridge crane according to claim 1, characterized in that: The state value is obtained as follows: A three-dimensional coordinate system is established for the bridge crane's motion environment, which is evenly divided into grid cells. Real-time motion environment data is collected and obstacles are identified. An obstacle analysis period is set with the current moment as the end point. During this period, the distance between the coordinates of the grid cells covered by the obstacle at adjacent moments is calculated. If the distance between each two adjacent moments meets the preset conditions, the obstacle is judged to be a static obstacle; otherwise, it is judged to be a dynamic obstacle. A status value is assigned to each grid cell. The status value of a grid cell not covered by any obstacle is 0, the status value of a grid cell covered by a static obstacle is 1, and the status value of a grid cell covered by a dynamic obstacle is 2.

3. A method for planning a motion path of a bridge crane according to claim 2, characterized in that: The time grid state matrix is ​​obtained as follows: Set a future time period starting from the current moment, obtain the coordinates of the grid cells covered by the dynamic obstacle at each moment in the obstacle analysis period, use the Kalman filter algorithm to predict the motion trajectory of the dynamic obstacle in the future time period, obtain the coordinates of the grid cells covered by the dynamic obstacle at each moment in the future time period, organize and transform the state values ​​of each grid cell at each moment in the future time period, and generate a time grid state matrix.

4. The method for planning a motion path of a bridge crane according to claim 1, wherein: The method for obtaining the time-series pass grid set sequence is as follows: The path planning is set to include the starting grid cell, the ending grid cell, and the obstacle safety distance. At each moment in the future period, all grid cells with a state value of 0 are obtained and marked as empty grids. Grid cells with a state value other than 0 are marked as obstacle grids. The minimum Euclidean distance between the empty grid and the obstacle grid is calculated. If the minimum Euclidean distance is greater than the obstacle safety distance, the empty grid is marked as a passable grid and included in the passable grid set. An expected starting time is preset for the bridge crane, and the passing grid set before the expected starting time is cleared to obtain a time-series passing grid set sequence.

5. A method for planning a motion path of a bridge crane according to claim 4, characterized in that: The shortest path is obtained as follows: The center point of each grid unit is regarded as a node, where the starting grid unit and the ending grid unit are the starting node and the ending node respectively. The starting and ending grid sets of the time-sequential passing grid set sequence are obtained. The improved A* algorithm is used to search for paths in the starting and ending grid sets to obtain the basic shortest path. The line segments between the nodes in the shortest path and the non-adjacent subsequent nodes are analyzed to see whether they are in the starting and ending grid sets. Based on the analysis results, the changed nodes are marked to obtain the shortest path. The expected average speed is set for the bridge crane. The shortest path and the expected starting time are combined. The grid cell where the spreader center is located is marked as the instantaneous position. The instantaneous position of the bridge crane at each moment in the time-series pass grid set sequence is calculated. If there is an instantaneous position that is not in the pass grid set at the corresponding moment, the node corresponding to the instantaneous position is cleared from the starting pass grid set and the path search is repeated. Otherwise, the shortest path is obtained.

6. A method for planning a motion path of a bridge crane according to claim 5, characterized in that: The path node sequence is obtained as follows: Obtain the starting pass grid set of the time-series pass grid set sequence, use the RRT algorithm to run multiple independent runs within the starting pass grid set to search for paths, obtain multiple feasible paths, analyze whether the line segments between nodes in the feasible path and non-adjacent subsequent nodes are within the starting pass grid set, mark the changed nodes based on the analysis results, and simplify and update the feasible path; For any feasible path, combine the expected average speed and the expected start time to determine whether the current position is within the pass grid set at the corresponding time. If the current position is not within the pass grid set at the corresponding time, the feasible path is cleared. The actual lengths of the feasible path and the shortest path are calculated and processed to obtain the length deviation of the feasible path. If the length deviation meets the preset conditions, the feasible path is marked as the initial path. After traversing all the feasible paths obtained, the shortest path is marked as the initial path. The nodes of the initial path are integrated to obtain the path node sequence of each initial path.

7. The method for planning a motion path of a bridge crane according to claim 1, wherein: The path tortuosity is obtained as follows: For each initial path, the path node sequence of the initial path is obtained, and the vector calculation method is used to calculate the turning angle of the change node and the actual length of each initial path. The data is processed in combination with the number of change nodes in the initial path to calculate the path tortuosity of the initial path.

8. A method for planning a motion path of a bridge crane according to claim 7, characterized in that: The preferred path is obtained as follows: The mean path tortuosity of each initial path in the initial path set is calculated as the tortuosity threshold. The initial path set is screened based on the tortuosity threshold. For each initial path, if the path tortuosity of the initial path is less than the tortuosity threshold, the initial path is marked as the preferred path.

9. The method for planning a motion path of a bridge crane according to claim 5, wherein: The optimal path is obtained as follows: The motion equation of a simple pendulum load model is established based on the Lagrange equation. Each change node within the optimal path is obtained. Based on the expected average speed and the steering angle at the change node, the acceleration of the load controlled by the bridge crane in the x and y directions of the horizontal plane is calculated. The load swing angle is solved through numerical integration. The load swing envelope is obtained and combined with the length of the wire rope lowered by the bridge crane. Combining the preferred path, expected average speed and expected start time, the moment when the bridge crane's immediate position reaches the change node is calculated and marked as the change time. The change window is taken with the change time as the midpoint, and the overlapping parts of the passing grid sets within the change window are sorted into a coincident grid set. If the swing envelope range of the load on the change node exceeds the coincident grid set, the change node is regarded as a risk change node. The preferred path with the minimum number of risk change nodes is the optimal path.

10. A bridge crane motion path planning system, characterized by: Includes the following modules: Environmental modeling module: This module evenly divides the bridge crane's motion environment into grid cells, identifies dynamic obstacles, assigns a state value to each grid cell, predicts the trajectory of the dynamic obstacles, and generates a time grid state matrix that includes the state value of each grid cell at the corresponding moment. Basic Planning Module: Constructs a time-series pass grid set sequence including a pass grid set based on the time grid state matrix, uses the improved A* algorithm to explore the shortest path, uses the RRT algorithm to explore multiple feasible paths, marks the changed nodes and screens multiple feasible paths based on the shortest path, marks the initial path and organizes the initial path set and path node sequence; Preliminary screening module: For the initial path, the path tortuosity of the initial path is quantitatively calculated, and screening is performed based on the path tortuosity, and the preferred paths are marked and organized into a preferred path set; Load selection module: For the optimal path, analyze the load swing at the change node, calculate the load swing envelope range, mark the risk change node, and screen to obtain the optimal path.

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