A tower crane low-altitude path planning method, device and equipment based on configuration space

Abstract

A tower crane low-altitude path planning method, device and equipment based on configuration space provided by the present invention relate to the technical field of path planning. The present invention generates and expands an obstacle bounding box and maps it to the configuration space by constructing a configuration space composed of the amplitude variation degree of freedom, slewing degree of freedom and self-rotation degree of freedom of the suspended object of the tower crane, identifies dangerous nodes through quadtree partitioning and collision detection, connects adjacent planar nodes and uses the A* algorithm to plan to obtain an optimal planned path, and operates the tower crane according to the optimal planned path. This application can solve the problems of complex paths, low operability and failure to consider the self-rotation of the suspended object in traditional path planning, improve the calculation efficiency and path accuracy, reduce the operation cost, and is applicable to hoisting tasks with rotation requirements for the lifting point and the target point.

Description

Technical Field

[0001] The present invention relates to the technical field of collision detection and path planning, and in particular, to a tower crane low-altitude path planning method, device, and equipment based on configuration space. Background Art

[0002] In the field of construction, the path planning of tower cranes is an important link to ensure the safe and efficient lifting operation. Traditionally, tower crane operators rely on experience and visual judgment for lifting tasks, which is not only cumbersome but also error-prone.

[0003] With the development of technology, researchers have proposed various path planning methods to optimize the lifting process, but these methods still have significant deficiencies. For example, the Rapidly-Exploring Random Tree (RRT) and its variants search non-convex high-dimensional spaces by randomly constructing space-filling trees. Although they can effectively cover complex spaces, they lack constraints in the search direction, resulting in complex and difficult-to-execute paths. Evolutionary algorithms such as genetic algorithms, particle swarm optimization, and simulated annealing reflect the preferences of researchers by establishing optimization objectives and weights. However, these methods require high computational performance and are difficult to be widely applied in the case of limited high-performance equipment at the construction site. In addition, due to the sensitivity of these algorithms to the initial state, small changes may lead to significantly different results, making their interpretability and repeatability low, and it is difficult to meet the requirements of stability and accuracy in lifting practice. Dijkstra and A* algorithms achieve optimization by recursively searching for the minimum-cost path, but the paths they plan are mostly in the form of broken lines with many nodes, resulting in frequent swinging of the hook, increasing the risk of collision and energy consumption. At the same time, the paths are not compatible with the rotation and luffing motion characteristics of the tower crane, making it difficult for operators to operate according to the planned paths. More importantly, existing methods mainly focus on the degrees of freedom of the tower crane itself, such as slewing, hoisting, and luffing, while ignoring the possibility of self-rotation of the lifted object at low altitude, which not only increases the operation cost but also cannot meet the task requirements where specific rotation angles of the lifted object are required at the starting and target positions.

[0004] In view of this, the applicant has specifically proposed this application after studying the existing technologies. Summary of the Invention

[0005] The present invention aims to provide a tower crane low-altitude path planning method, device, and equipment based on configuration space to solve the problems existing in the existing tower crane low-altitude path planning technology, such as complex paths, low operability, high calculation cost, and ignoring the self-rotation degree of freedom of the lifted object. By comprehensively considering the motion characteristics of the tower crane, the possibility of self-rotation of the lifted object, and the calculation efficiency, efficient, accurate, and highly operable path planning is achieved.

[0006] To solve the above technical problems, the present invention is realized through the following technical solutions:

[0007] A low-altitude path planning method for tower cranes based on configuration space, comprising:

[0008] S1. Obtain relevant data of the tower crane, the lifted object, and the obstacles;

[0009] S2. Construct a three-dimensional configuration space for the low-altitude hoisting motion of the tower crane according to the luffing degree of freedom, slewing degree of freedom of the tower crane, and the self-rotation degree of freedom of the lifted object;

[0010] S3. Generate an OBB bounding box of the lifted object and the obstacles in the Cartesian space, and expand the bounding box of the obstacles according to the self-rotation angle and size of the lifted object to obtain the expanded obstacles;

[0011] S4. Map the lifted object, the expanded obstacles, and the pseudo-obstacles to the three-dimensional configuration space; wherein, the pseudo-obstacles are the ranges of the luffing degree of freedom and slewing degree of freedom of the tower crane;

[0012] S5. Perform quadtree partitioning on each plane of the mapped three-dimensional configuration space, and perform recursive collision detection between the quadtree and the expanded obstacles to identify the collision sub-volumes until the partitioning termination condition is reached;

[0013] S6. Connect the vertices of the same-level sub-volumes of adjacent planes that are not in the collision sub-volumes with straight lines to generate a three-dimensional grid;

[0014] S7. According to the three-dimensional grid, use the A* algorithm to perform path planning to obtain the optimal planned path, and operate the tower crane according to the optimal planned path.

[0015] Preferably, the S3 is specifically:

[0016] Let the length of the lifted object be W, the width be H, and the vertex coordinates of the OBB bounding box of the lifted object be:

[0017] ;

[0018] ;

[0019] wherein, is the vertex coordinate of the OBB bounding box of the lifted object ;

[0020] is the rotation matrix of the lifted object, is the current self-rotation angle of the lifted object; is the centroid coordinate of the lifted object;

[0021] The vertex coordinates of each obstacle OBB bounding box are:

[0022] ;

[0023] ;

[0024] Among them, are the vertex coordinates of the obstacle OBB bounding box; is the rotation matrix of the obstacle; is the orientation angle of the current obstacle; , are the centroid coordinates of the obstacle; 、 are the length and width of the obstacle respectively;

[0025] After generating the OBB bounding boxes of the object to be lifted and the obstacle, according to the self-rotation angle of the object to be lifted and the bounding box vertices, expand the outer boundary of the obstacle's OBB bounding box. The expansion expression is:

[0026] ;

[0027] Among them, is the vertex coordinate of the expanded obstacle in the Cartesian coordinate system when the self-rotation angle of the object to be lifted is ; is the Minkowski sum operation; 、 represent the point coordinates of the obstacle and the object to be lifted respectively; 、 are the x value and y value of the vertex of the expanded obstacle in the Cartesian coordinate system respectively.

[0028] Preferably, the specific step S4 is:

[0029] After expanding the obstacle bounding box, map the object to be lifted, the expanded obstacle and the pseudo-obstacle to the three-dimensional configuration space;

[0030] The object to be lifted is mapped to a point P in the three-dimensional configuration space, expressed as:

[0031] ;

[0032] Among them, D is the luffing degree of freedom of the tower crane; is the slewing degree of freedom of the tower crane; is the self-rotation degree of freedom of the object to be lifted;

[0033] Map each discrete face of the expanded obstacle in the three-dimensional configuration space to a convex polygon, and the expression is:

[0034] ;

[0035] Among them, To expand the vertex coordinates of each plane in the configuration space of the obstacle map, connect the vertices to form an expanded obstacle in the configuration space; Indicates existence;

[0036] Are the vertex coordinates of the expanded obstacle in the Cartesian coordinate system;

[0037] 、 Are respectively the x-value and y-value of the vertex of the expanded obstacle in the Cartesian coordinate system;

[0038] Next, map the pseudo-obstacle to the configuration space and represent it as a rectangle or a square; the position of the pseudo-obstacle in each plane of the configuration space Is the same, and its vertex coordinates in the configuration space are represented as K, and K is determined by the luffing range and the slewing range.

[0039] Preferably, the S5 is specifically:

[0040] The self-rotation angle of each lifted object Corresponds to a two-dimensional plane Each plane The center point of is initialized as the root node of a quadtree, and the coverage range of the quadtree is the luffing degree of freedom D of the tower crane and the slewing degree of freedom Value range;

[0041] Perform quadtree partitioning on each plane To create new sub-volumes until the partitioning termination condition is reached;

[0042] The shape of each sub-volume is a square or a rectangle, and the shapes of the same-level sub-volumes are the same;

[0043] In each plane Perform collision detection between the quadtree and the expanded obstacle, identify the collided sub-volumes with the obstacle, and store the vertices of the identified collided sub-volumes in the collision danger node set N to avoid the planned route passing through N.

[0044] Preferably, the partitioning termination condition is that the side length of the partitioned sub-volume or the recursive level reaches a set threshold.

[0045] Preferably, in the configuration space, when connecting the vertices of the same-level sub-volumes of adjacent planes that are not within the collided sub-volumes with straight lines to generate a three-dimensional grid, that is, when establishing a connection relationship between the nodes of adjacent planes And In the three-dimensional configuration space, the following conditions need to be met when connecting:

[0046] The two end vertices of the connection are not within the collision danger node set N, and the difference in self-rotation angles is wherein is a preset discrete rotation step;

[0047] Let the lifting point or target point of the tower crane be on the three-dimensional grid or connected to the vertex of the three-dimensional grid;

[0048] The edge of each three-dimensional grid is a possible planned path, and it is ensured that the planned path can reach the lifting point or the target point.

[0049] Preferably, it further includes that when the lifting point or target point of the tower crane is not on the three-dimensional grid, connecting the lifting point or target point of the tower crane to the vertex of the three-dimensional grid with an optimal connection path; the optimal connection path is:

[0050] Identifying the vertices of the smallest sub-body where the lifting point or target point is located and the extended obstacles within the sub-body or the vertices K of the pseudo-obstacles;

[0051] Taking the four vertices of the identified smallest sub-body as the path start point, the lifting point or target point of the tower crane as the path end point, and the vertices of the extended obstacles and the vertices K of the pseudo-obstacles as path nodes, and using the Dijkstra algorithm to plan an optimal connection path from the vertex of the smallest sub-body to the lifting point or target point.

[0052] Preferably, in the process of obtaining the optimal planned path;

[0053] The total cost function of the A* algorithm is expressed as:

[0054] ;

[0055] wherein is the path cost function; is the heuristic function;

[0056] The path cost function has the following expression:

[0057] ;

[0058] ;

[0059] ;

[0060] ;

[0061] wherein is the distance difference between the current node and the previous node in the luffing direction; , are the luffing amplitudes of the previous node and the current node respectively;

[0062] is the angular difference between the current node and the previous node in the slewing direction; 、 are the slewing angle values of the previous node and the current node respectively;

[0063] is the angular difference between the current node and the previous node in the self-rotation of the lifted object; 、 are the self-rotation angle values of the lifted object of the previous node and the current node respectively;

[0064] 、 、 are the luffing weight, slewing weight, and self-rotation weight of the lifted object respectively;

[0065] Heuristic function The expression of

[0066] ;

[0067] Among them, is the luffing value of the lifting point or the target point; is the luffing value of the current node n; is the angular difference between the lifting point or the target point and the current node in the slewing direction; is the self-rotation angle value of the lifted object of the lifting point or the target point; is the self-rotation angle value of the lifted object of the current node n;

[0068] When the total cost function is the smallest, it is the optimal planning path.

[0069] The present invention also provides a tower crane low-altitude path planning device based on the configuration space, including:

[0070] An acquisition unit for acquiring relevant data of the tower crane, the lifted object, and the obstacle;

[0071] A three-dimensional configuration space construction unit for constructing a three-dimensional configuration space for the low-altitude hoisting motion of the tower crane according to the luffing degree of freedom, slewing degree of freedom, and self-rotation degree of freedom of the lifted object of the tower crane;

[0072] An extended obstacle unit for generating an OBB bounding box of the lifted object and the obstacle in the Cartesian space, and extending the bounding box of the obstacle according to the self-rotation angle and size of the lifted object to obtain an extended obstacle;

[0073] A mapping unit for mapping the object to be lifted, extended obstacles, and pseudo-obstacles into the three-dimensional configuration space; wherein, the pseudo-obstacles are the ranges of the luffing degree of freedom and slewing degree of freedom of the tower crane.

[0074] A quadtree partitioning unit for performing quadtree partitioning on each plane of the mapped three-dimensional configuration space, and performing recursive collision detection between the quadtree and the extended obstacles to identify collision sub-bodies until the partitioning termination condition is reached.

[0075] A three-dimensional grid unit for connecting the vertices of the same-level sub-bodies of adjacent planes that are not in the collision sub-bodies with straight lines to generate a three-dimensional grid.

[0076] An optimal path planning unit for performing path planning using the A* algorithm based on the three-dimensional grid to obtain an optimal path planning, and operating the tower crane to run according to the optimal path planning.

[0077] The present invention also provides a tower crane low-altitude path planning device based on the configuration space, including a processor and a memory, where the memory stores a computer program, and the computer program can be executed by the processor to implement a tower crane low-altitude path planning method based on the configuration space as described above.

[0078] The present invention also provides a computer-readable storage medium, on which computer-readable instructions are stored, and when the computer-readable instructions are executed by the processor of the device where the computer-readable storage medium is located, a tower crane low-altitude path planning method based on the configuration space as described above is implemented.

[0079] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0080] By constructing a three-dimensional configuration space and introducing the self-rotation degree of freedom of the object to be lifted, the path planned by the present invention conforms to the kinematic characteristics of the tower crane, is convenient for the operator to execute, and has stronger operability. At the same time, the method of the present invention fully considers the possibility of self-rotation of the object to be lifted, is applicable to tasks with rotation requirements for the object to be lifted and the target point, and reduces the lifting cost.

[0081] Through collision detection and path optimization, the present invention effectively reduces the collision risk and energy consumption during the lifting process, improves safety, solves the problems of complex paths, low operability, high calculation cost, and failure to consider the self-rotation degree of freedom of the object to be lifted in the existing tower crane low-altitude path planning technology, and provides an efficient, accurate, and safe tower crane low-altitude path planning solution. Description of the Drawings

[0082] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0083] Figure 1 Schematic diagram of a tower crane low-altitude path planning method based on configuration space provided for Embodiment 1.

[0084] Figure 2 Example diagram of the result after expanding the obstacle bounding box provided for Embodiment 1, where Figure 2 (a) is the result diagram expanded into an octagon, Figure 2 (b) is the result diagram expanded into a quadrilateral.

[0085] Figure 3 Schematic diagram of the construction of the configuration space and three-dimensional grid based on quadtree partitioning provided for Embodiment 1.

[0086] Figure 4 Schematic diagram of a tower crane low-altitude path planning device based on configuration space provided for Embodiment 2.

[0087] The following further details the present invention in conjunction with the drawings and specific embodiments. Specific Embodiments

[0088] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0089] Embodiment 1

[0090] Embodiment 1 of the present invention provides a tower crane low-altitude path planning method based on configuration space, which can be implemented by a tower crane low-altitude path planning device based on configuration space (hereinafter referred to as the path planning device), and particularly, is executed by one or more processors in the path planning device.

[0091] In this embodiment, the path planning device may be an electronic device equipped with a processor, and the processor has a computer program of this tower crane low-altitude path planning method based on the configuration space and the computer program can be executed, such as a computer, a smart phone, a smart tablet, a workstation, etc., which is not limited here.

[0092] In this embodiment, the configuration space is an abstract space concept, and each point in it represents a specific configuration of a robot (such as a tower crane). Configuration refers to a set of independent variables required to completely describe the position and posture of a robot in space. For example, for a robot with n degrees of freedom, the configuration space is an n-dimensional space, and each point in the space is determined by n coordinate values, and these n coordinate values respectively correspond to the positions or angles of the respective joints of the robot.

[0093] Quad-tree: Recursively divide the space into four quadrants or regions, and each node is a leaf node or has four leaf nodes. Each node corresponds to a specific two-dimensional region. When the data in this region meets specific conditions (such as the number of data exceeds the threshold, the data distribution in the region is uneven, etc.), the region will be divided into four sub-regions, and each sub-region corresponds to a sub-node.

[0094] A recursive algorithm is a method of calling itself in a function or algorithm. In programming, recursive algorithms are mainly used to solve tasks that can be decomposed into smaller-scale same problems.

[0095] The A* algorithm is an efficient path search and graph traversal algorithm, which is widely used in fields such as game design, robot path planning, and network routing. It finds the shortest path from the starting point to the ending point by combining heuristic evaluation and actual cost.

[0096] Path planning: The sequence of points or curves connecting the starting point position and the ending point position is called a path, and the strategy for constructing the path is called path planning.

[0097] As Figure 1 shown, a tower crane low-altitude path planning method based on the configuration space includes steps S1 to S7.

[0098] S1. Obtain the relevant data of the tower crane, the lifted object, and the obstacles.

[0099] In this embodiment, the application scenario is the low-altitude hoisting movement of the tower crane, so this method does not consider the lifting degree of freedom and height attribute of the tower crane. The luffing degree of freedom D, slewing degree of freedom , the position of the lifting point, the position of the target point, the size of the lifted object, the self-rotation degree of freedom , data such as centroid coordinates, dimensions of obstacles, positions, centroid coordinates, orientation angles, rotation angles, etc., for subsequent path planning.

[0100] S2. Construct a three-dimensional configuration space for the low-altitude hoisting motion of the tower crane according to the luffing degree of freedom, slewing degree of freedom of the tower crane, and self-rotation degree of freedom of the lifted object.

[0101] In this embodiment, first, a three-dimensional configuration space is constructed, and its three axes are respectively the luffing degree of freedom D of the tower crane, the slewing degree of freedom and the self-rotation degree of freedom of the lifted object .

[0102] Specifically, the value range of the luffing degree of freedom D is , , where represents the minimum luffing value, represents the maximum luffing value;

[0103] The slewing degree of freedom has a value range of ;

[0104] The self-rotation degree of freedom of the lifted object is represented as a discrete angle set:

[0105] ;

[0106] where k is an arbitrary integer; represents an integer; is a preset discrete rotation step, set by the user according to actual needs.

[0107] The luffing degree of freedom and slewing degree of freedom of the tower crane change continuously, forming a continuous plane. Since the self-rotation of the lifted object is generally rotated manually using a pulling rope, not machine rotation, it is not suitable for rotation with a small angle, such as 1 degree, and it is also not practical. Therefore, the self-rotation axis of the lifted object is discrete, and the discrete step is determined by the user. An overly small discrete step will result in a large amount of calculation time, and an overly large discrete step will result in an inaccurate planned path. Therefore, the configuration space is composed of multiple parallel planes stacked, and each plane corresponds to a specific self-rotation angle of the lifted object . These planes form the basic structure for subsequent path planning.

[0108] S3. Generate the OBB bounding boxes of the lifted object and obstacles in the Cartesian space, and expand the bounding boxes of the obstacles according to the self-rotation angle and size of the lifted object to obtain the expanded obstacles.

[0109] Next, generate OBB bounding boxes for the suspended object and the obstacles in Cartesian space, and expand the bounding boxes of the obstacles.

[0110] Take Figure 2 as an example. Let the length of the suspended object be W and the width be H. The vertex coordinates of the OBB bounding box of the suspended object are:

[0111] ;

[0112] ;

[0113] where is the vertex coordinate of the OBB bounding box of the suspended object ;

[0114] is the rotation matrix of the suspended object, is the self-rotation angle of the current suspended object; is the centroid coordinate of the suspended object.

[0115] For the vertex coordinates of each obstacle OBB bounding box are:

[0116] ;

[0117] ;

[0118] where is the vertex coordinate of the obstacle OBB bounding box; is the rotation matrix of the obstacle; is the orientation angle of the current obstacle; , is the centroid coordinate of the obstacle; , are the length and width of the obstacle respectively.

[0119] After generating the OBB bounding boxes of the suspended object and the obstacles, expand the outer boundary of the OBB bounding box of the obstacle. The expansion method is related to the self-rotation angle of the suspended object and the bounding box vertices, and the expansion is carried out through Minkowski sum operation. The specific method is:

[0120] ;

[0121] where is the vertex coordinate of the expanded obstacle in the Cartesian coordinate system when the self-rotation angle of the suspended object is ; is the Minkowski sum operation; , respectively represent the point coordinates of the obstacle and the object to be lifted; , are respectively the x-value and y-value of the vertex of the extended obstacle in the Cartesian coordinate system.

[0122] The Minkowski Sum is an operation defined in Euclidean space and is used to describe the combination of two point sets. In this embodiment set, each point in the point set is added to each point in the point set , and the finally formed new point set is their Minkowski Sum.

[0123] The bounding box of the extended obstacle is an octagonal convex polygon with eight vertices, as shown in Appendix Figure 2 (a). In special cases, when the OBB sides of the obstacle and the object to be lifted are parallel, the bounding box of the extended obstacle is a four-vertex four-sided convex polygon, as shown in Appendix Figure 2 (b). The bounding box of the extended obstacle can accurately describe the geometric relationship between the obstacle and the object to be lifted, thereby improving the accuracy of path planning.

[0124] S4. Map the object to be lifted, the extended obstacle, and the pseudo-obstacle to the three-dimensional configuration space; wherein, the pseudo-obstacle is the range of the luffing degree of freedom and the slewing degree of freedom of the tower crane.

[0125] After expanding the bounding box of the obstacle, map the object to be lifted, the extended obstacle, and the pseudo-obstacle to the configuration space. The object to be lifted is mapped as a point P in the configuration space, expressed as:

[0126] ;

[0127] The extended obstacle is mapped as a convex polygon in each discrete plane , as shown by the black polygon in Appendix Figure 3 , and the mapping method is:

[0128] ;

[0129] wherein, are the vertex coordinates of the extended obstacle mapped in each plane of the configuration space, and connecting the vertices forms the extended obstacle in the configuration space; indicates existence; are the vertex coordinates of the extended obstacle in the Cartesian coordinate system;

[0130] , are respectively the x-value and y-value of the vertex of the extended obstacle in the Cartesian coordinate system.

[0131] During the hoisting process, due to the slewing and luffing motions, even if the lifted object does not collide with obstacles, the boom and the lifting ropes of the tower crane may still collide with obstacles. That is, there are constraints on the luffing and slewing ranges of the tower crane. These are mapped into the configuration space as pseudo-obstacles, mapped as rectangles or squares, as shown by the red quadrilaterals in the appendix Figure 3 . The pseudo-obstacles are in the same position on each plane of the configuration space, and their vertex coordinates in the configuration space are represented as K (determined by the luffing range and the slewing range).

[0132] S5. Perform quadtree partitioning on each plane of the mapped three-dimensional configuration space, and perform recursive collision detection between the quadtree and the extended obstacles to identify the colliding sub-volumes until the partitioning termination condition is reached.

[0133] In the configuration space, the self-rotation angle of each lifted object corresponds to a two-dimensional plane . The center point of each plane is initialized as the root node of a quadtree. Initialize the root node of the quadtree, with the coverage range being the value range of the luffing degree of freedom D of the tower crane and the slewing degree of freedom of the tower crane, and recursively partition new sub-volumes until the partitioning termination condition is reached, such as setting a partitioning termination threshold (the side length of the sub-volume is less than 0.5 m or the recursive level exceeds 5 levels). The shape of each sub-volume is a square or a rectangle, and the shapes of the sub-volumes at the same level are the same. The outer border of the partitioned sub-volume is the possible hoisting route, and the vertices of the sub-volume are the path nodes. The introduction of the quadtree structure can significantly reduce the computational complexity of path planning while ensuring the accuracy of the path.

[0134] Perform recursive collision detection between the quadtree and the extended obstacles on each plane , identify the colliding sub-volumes that collide with the obstacles, and store the vertices of the identified colliding sub-volumes in the collision danger node set N to avoid the planned route passing through N.

[0135] S6. Connect the vertices of the same-level sub-volumes on adjacent planes that are not within the colliding sub-volumes with straight lines to generate a three-dimensional grid.

[0136] In the configuration space, when connecting the vertices of the same-level sub-volumes on adjacent planes that are not within the colliding sub-volumes with straight lines to generate a three-dimensional grid, as Figure 3 shown, establish connections between the nodes of adjacent self-rotation angle planes and in the three-dimensional configuration space (i.e., the adjacent planes corresponding to the self-rotation angles and of the lifted object) to form a three-dimensional grid.

[0137] When connecting, the following conditions need to be met: The two end vertices of the connection are not in the set N of collision - dangerous nodes, and the self - rotation angle difference is ; where is the preset discrete rotation step.

[0138] Let the lifting point or target point of the tower crane be on the three - dimensional grid or connected to the vertex of the three - dimensional grid. The side line of each three - dimensional grid is a possible planned path, and it is ensured that the planned path can reach the lifting point or the target point.

[0139] During the actual operation process, when the lifting point or target point of the tower crane is not on the three - dimensional grid, connect the lifting point or target point of the tower crane to the vertex of the three - dimensional grid with the optimal connection path. Specifically:

[0140] Identify the smallest sub - volume where the lifting point or target point of the tower crane is located and the vertices of the extended obstacles or pseudo - obstacles inside it, and use the Dijkstra algorithm to plan the optimal connection path from the vertex of the smallest sub - volume to the lifting point or target point. As shown by the green dashed line in the appendix Figure 3 shows the schematic diagram of the three - dimensional grid path. Through local path planning, the integrity and flexibility of the path are ensured, and at the same time, the computational complexity is reduced.

[0141] In this embodiment, the Dijkstra algorithm is a classic algorithm for calculating the shortest path from a single source. It is applicable to weighted directed graphs and requires that the weights of all edges are non - negative numbers. This algorithm gradually expands the shortest path from the starting point to other nodes and finally finds the shortest path from the starting point to all other nodes.

[0142] S7. According to the three - dimensional grid, use the A* algorithm to perform path planning to obtain the optimal planned path, and operate the tower crane according to the optimal planned path.

[0143] The path - planning problem of the tower crane is to find a collision - free and low - cost route in the Cartesian coordinate system and connect the starting point to the target point, which is transformed into finding a route connecting the starting point to the target point through the three - dimensional grid in the configuration space. The calculation amount of the present invention is small and not complex. The planned path conforms to the motion characteristics of the tower crane, which is convenient for the operator to operate. The planned path also takes into account the possibility of the self - rotation of the lifted object.

[0144] Specifically, in the path - planning stage, the A* algorithm is used. It is a heuristic search algorithm for graph search and path planning. It combines the advantages of the greedy algorithm and the Dijkstra algorithm and can significantly improve the search efficiency while ensuring the optimal solution. The A* algorithm selects the optimal planned path by evaluating the total cost function of each node. The total cost function includes the path cost function g(n) and the heuristic function h(n), which are defined as follows:

[0145] Total cost function Is expressed as:

[0146] ;

[0147] Wherein, Is the path cost function, i.e., the actual cost (path length) from the starting point to the current node; Is the heuristic function, i.e., the estimated cost from the current node to the end point (lifting point or target point).

[0148] Path cost function The expression of is:

[0149] ;

[0150] ;

[0151] ;

[0152] ;

[0153] Wherein, Is the distance difference between the current node and the previous node in the luffing direction; , Are respectively the luffing values of the previous node and the current node;

[0154] Is the angle difference between the current node and the previous node in the slewing direction; , Are respectively the slewing angle values of the previous node and the current node;

[0155] Is the angle difference between the current node and the previous node in the self-rotation of the lifted object; , Are respectively the self-rotation angle values of the lifted object of the previous node and the current node;

[0156] , , Are respectively the luffing weight, slewing weight, and self-rotation weight of the lifted object. For example, the luffing weight = 0.4, the slewing weight = 0.3, and the self-rotation weight = 0.3. It can balance the path cost through experimental verification.

[0157] Heuristic function The expression of is:

[0158] ;

[0159] Wherein, Is the luffing value of the lifting point or target point; is the variable amplitude value of the current node n; is the angular difference between the lifting point or target point and the current node in the slewing direction; is the self-rotation angle value of the lifted object at the lifting point or target point; is the self-rotation angle value of the lifted object at the current node n.

[0160] Finally, select the path with the smallest f(n) as the planning result.

[0161] Through the A* algorithm, the global optimality and operability of the path are achieved.

[0162] Through the above implementation steps, the technical effects of the method of the present invention are reflected. By combining the three-dimensional configuration space constructed by the present invention with the self-rotation degree of freedom of the lifted object, the planned path conforms to the kinematic characteristics of the tower crane, is convenient for the operator to execute, and has stronger operability. Compared with path planning in the Cartesian coordinate system, this method reduces the amount of calculation and memory resource occupation, and improves the calculation efficiency. In addition, this method fully considers the self-rotation possibility of the lifted object, is applicable to tasks with rotation requirements for the lifting point and target point of the lifted object, and at the same time reduces the lifting cost. Through collision detection and path optimization, the collision risk and energy consumption during the lifting process are effectively reduced, and the safety is improved.

[0163] In the construction scenario, for example, in a large construction site, a tower crane is needed to transport building materials from the lifting point to the target point, and the target point has clear requirements for the self-rotation angle of the materials. According to the traditional method, the operator needs to rely on experience for manual adjustment, which is prone to problems such as complex paths, difficult operations, and high safety risks. After adopting the method of the present invention, first, a three-dimensional configuration space is constructed according to the parameters of the tower crane, and the OBB bounding boxes of the lifted object and obstacles are generated, and the bounding boxes of the obstacles are extended to accurately describe the geometric relationship. Subsequently, through quadtree partitioning and collision detection, a three-dimensional grid path is generated to ensure the continuity and feasibility of the path. Finally, the A* algorithm is used to plan the optimal path, and the operator only needs to execute according to the planned path. During this process, the operation of the tower crane is more accurate, the path is simpler, the lifting efficiency is significantly improved, and at the same time the operation difficulty and safety risk are reduced.

[0164] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0165] The present invention constructs a three-dimensional configuration space with the boom amplitude degree of freedom, slewing degree of freedom and self-rotation degree of freedom of the lifted object of the tower crane as axes, and solves the problem that the traditional method does not consider the self-rotation degree of freedom of the lifted object.

[0166] The present invention introduces an OBB bounding box expansion mechanism. By expanding the bounding box of the obstacle, the geometric relationship between the obstacle and the lifted object is accurately described, improving the accuracy of path planning.

[0167] The present invention uses a quadtree structure to divide the configuration space and combines a recursive collision detection algorithm, significantly improving the efficiency and accuracy of path planning.

[0168] The present invention adopts the A* algorithm in the path planning stage. By defining the path cost function and the heuristic function, the optimality and operability of the path are ensured.

[0169] The method of the present invention avoids the problem of complex paths caused by excessive nodes in the traditional method by establishing a quadtree structure and performing collision detection in each discrete plane. At the same time, by connecting the nodes of adjacent planes to form a three-dimensional grid, the continuity and feasibility of the path between different self-rotation angles are ensured. In addition, when the lifting point or the target point is not on the three-dimensional grid, by identifying the smallest sub-body and using the Dijkstra algorithm to plan the local path, the integrity and flexibility of the path planning are guaranteed.

[0170] Embodiment 2

[0171] As Figure 4 shown, the second embodiment of the present invention also provides a tower crane low-altitude path planning device based on the configuration space, including:

[0172] An acquisition unit, configured to acquire relevant data of the tower crane, the lifted object, and the obstacle;

[0173] A three-dimensional configuration space construction unit, configured to construct a three-dimensional configuration space for the low-altitude hoisting movement of the tower crane according to the luffing degree of freedom, slewing degree of freedom of the tower crane, and the self-rotation degree of freedom of the lifted object;

[0174] An extended obstacle unit, configured to generate OBB bounding boxes of the lifted object and the obstacle in the Cartesian space, and expand the bounding box of the obstacle according to the self-rotation angle and size of the lifted object to obtain an extended obstacle;

[0175] A mapping unit, configured to map the lifted object, the extended obstacle, and the pseudo-obstacle to the three-dimensional configuration space; wherein, the pseudo-obstacle is the range of the luffing degree of freedom and slewing degree of freedom of the tower crane;

[0176] A quadtree division unit, configured to perform quadtree division on each plane of the mapped three-dimensional configuration space, and perform recursive collision detection between the quadtree and the extended obstacle to identify the collision sub-bodies until the division termination condition is reached;

[0177] A three-dimensional grid unit, configured to connect the vertices of the same-level sub-bodies of adjacent planes that are not in the collision sub-bodies with straight lines to generate a three-dimensional grid;

[0178] The optimal planning path unit is used to perform path planning according to the three-dimensional grid by using the A* algorithm to obtain the optimal planning path, and operate the tower crane according to the optimal planning path.

[0179] Embodiment III

[0180] The third embodiment of the present invention further provides a tower crane low-altitude path planning device based on the configuration space, which includes a memory and a processor. A computer program is stored in the memory, and the computer program can be executed by the processor to implement the tower crane low-altitude path planning method based on the configuration space as described above.

[0181] Embodiment IV

[0182] The fourth embodiment of the present invention further provides a computer-readable storage medium. Computer-readable instructions are stored on the computer-readable storage medium. When the computer-readable instructions are executed by the processor of the device where the computer-readable storage medium is located, the tower crane low-altitude path planning method based on the configuration space as described above is implemented.

[0183] In several embodiments provided by the embodiments of the present invention, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device and method embodiments described above are merely illustrative. For example, the flowcharts in the drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present invention. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0184] In addition, the functional modules in each embodiment of the present invention can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0185] When the above-mentioned functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs. It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or device including the said element.

[0186] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a", "the" and "said" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise.

[0187] It should be understood that the term "and / or" used herein is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0188] Depending on the context, the word "if" as used herein can be interpreted as "when", "while", "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" can be interpreted as "when determined", "in response to determining", "when detecting (stated condition or event)", or "in response to detecting (stated condition or event)".

[0189] The "first / second" mentioned in the embodiments is only used to distinguish similar objects and does not represent a specific order for the objects. It can be understood that the "first / second" can be interchanged in a specific order or sequence when permitted. It should be understood that the objects distinguished by the "first / second" can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0190] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, various modifications and changes can be made to the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A tower crane low-altitude path planning method based on configuration space, characterized in that: include: S1, obtaining relevant data of the tower crane, the hoisted object and obstacles; S2, constructing the three-dimensional configuration space of the tower crane's low-altitude lifting motion according to the tower crane's amplitude degree of freedom, rotation degree of freedom and self-rotation degree of freedom of the hoisted object; S3, generating an OBB bounding box of the suspended object and the obstacle in Cartesian space, and expanding the bounding box of the obstacle according to the self-rotation angle and size of the suspended object to obtain an expanded obstacle; S4, mapping the suspended object, the extended obstacle and the pseudo-obstacle to the three-dimensional configuration space; wherein the pseudo-obstacle is the range of the tower crane's amplitude degree of freedom and rotation degree of freedom; specifically: After the obstacle bounding box is expanded, the suspended object, the expanded obstacle and the pseudo obstacle are mapped into the three-dimensional configuration space; The suspended object is mapped to a point P in the three-dimensional configuration space, expressed as: ； Where D is the luffing degree of freedom of the tower crane; is the rotational freedom of the tower crane; is the rotational freedom of the suspended object; Each discrete surface of the extended obstacle in the three-dimensional configuration space is mapped into a convex polygon, and the expression is: ； in, Mapping the vertex coordinates of each plane in the configuration space to the extended obstacle, connecting the vertices to form the extended obstacle in the configuration space; Indicates existence; is the vertex coordinates of the extended obstacle in the Cartesian coordinate system; , are the x-value and y-value of the vertex of the extended obstacle in the Cartesian coordinate system respectively; Next, the pseudo-obstacle is mapped to the configuration space and represented as a rectangle; the pseudo-obstacle is located on each plane of the configuration space. The position of is the same, and its vertex coordinate in the configuration space is expressed as K, which is determined by the amplitude range and the rotation range; S5, performing quadtree partitioning on each plane of the mapped three-dimensional configuration space, and performing recursive collision detection between the quadtree and the extended obstacle to identify collision sub-bodies, until a partition termination condition is reached; S6, connecting the sub-volumes of the same level of adjacent planes and not at the vertices of the collision sub-volume with straight lines to generate a three-dimensional grid; S7, using the A* algorithm to perform path planning according to the three-dimensional grid to obtain an optimal planned path, and operating the tower crane according to the optimal planned path.

2. A tower crane low-altitude path planning method based on configuration space according to claim 1, characterized in that , the S3 is specifically: Set the object to be hoisted The length is W, the width is H, and the vertex coordinates of the bounding box of the hanging object OBB are for: ； ； in, For the suspended object The vertex coordinates of the OBB bounding box; is the rotation matrix of the suspended object, is the current rotation angle of the suspended object; is the coordinate of the center of mass of the suspended object; The vertex coordinates of each obstacle OBB bounding box for: ； ； in, The vertex coordinates of the obstacle OBB bounding box; is the rotation matrix of the obstacle; is the direction angle of the current obstacle; , is the coordinate of the center of mass of the obstacle; , are the length and width of the obstacle respectively; After the OBB bounding box of the suspended object and the obstacle is generated, the outer boundary of the OBB bounding box of the obstacle is expanded according to the self-rotation angle of the suspended object and the vertices of the bounding box. The expansion expression is: ； in, When the self-rotation angle of the suspended object is When , the vertex coordinates of the expanded obstacle in the Cartesian coordinate system; is the Minkowski sum operation; , Respectively represent the point coordinates of the obstacle and the suspended object; , are the x and y values ​​of the vertices of the extended obstacle in the Cartesian coordinate system, respectively.

3. A tower crane low-altitude path planning method based on configuration space according to claim 2, characterized in that , the S5 is specifically: The rotation angle of each suspended object Corresponding to a two-dimensional plane , each plane The center point is initialized as the root node of a quadtree, and the coverage of the quadtree is the tower crane's variable degree of freedom D and the tower crane's rotational degree of freedom The value range of Each plane Perform quadtree partitioning to create new sub-bodies until the partition termination condition is reached; The shape of each sub-body is rectangular, and the shapes of sub-bodies at the same level are consistent; In each plane The quadtree is used to detect collisions with the extended obstacles, the collision sub-bodies that collide with the obstacles are identified, and the vertices of the identified collision sub-bodies are stored in the collision risk node set N to avoid the planned route passing through N.

4. A tower crane low-altitude path planning method based on configuration space according to claim 3, characterized in that ,The termination condition of the division is that the side length of the divided sub-body or the recursive level reaches the set threshold.

5. The tower crane low-altitude path planning method based on configuration space according to claim 3 is characterized in that In the configuration space, when the vertices of the same level of the adjacent planes that are not in the collision sub-body are connected by straight lines to generate a three-dimensional grid, the adjacent planes in the three-dimensional configuration space are and The connection relationship is established between nodes. The connection must meet the following requirements: The two vertices of the connection are not in the collision risk node set N, and the rotation angle difference is ;in is the preset discrete rotation step size; Make the lifting point or target point of the tower crane on the three-dimensional grid or connect with the vertex of the three-dimensional grid; The edge of each three-dimensional grid is a possible planning path, and it is guaranteed that the planned path can reach the lifting point or the target point.

6. A tower crane low-altitude path planning method based on configuration space according to claim 5, characterized in that , also includes, when the lifting point or target point of the tower crane is not on the three-dimensional grid, connecting the lifting point or target point of the tower crane with the vertices of the three-dimensional grid by an optimal connection path; the optimal connection path is: Identify the smallest sub-volume where the lifting point or target point is located and the vertex of the extended obstacle in the sub-volume Or the vertex K of the pseudo obstacle; The four vertices of the smallest identified sub-body are used as the starting point of the path, the lifting point of the tower crane or the target point is the end point of the path, and the vertices of the obstacle are expanded. The vertex K of the pseudo-obstacle is the path node, and the Dijkstra algorithm is used to plan an optimal connection path from the vertex of the smallest sub-body to the lifting point or the target point.

7. The method for tower crane low-altitude path planning based on configuration space according to claim 1 is characterized in that ,In the process of obtaining the optimal planning path: The total cost function of the A* algorithm It is expressed as: ； in, is the path cost function; is the heuristic function; Path cost function The expression is: ； ； ； ； in, is the distance difference between the current node and the previous node in the amplitude variation direction; , are the amplitude values ​​of the previous node and the current node respectively; is the angular difference between the current node and the previous node in the rotation direction; , They are the rotation angle values ​​of the previous node and the current node respectively; It is the angle difference between the current node and the previous node in the self-rotation of the suspended object; , They are the self-rotation angle values ​​of the suspended object at the previous node and the current node respectively; , , They are the amplitude variation weight, rotation weight, and self-rotation weight of the suspended object; Heuristic function The expression is: ； in, It is the amplitude variation value of the lifting point or target point; is the amplitude value of the current node n; It is the angle difference between the lifting point or target point and the current node in the rotation direction; The self-rotation angle value of the hoisting object at the lifting point or target point; is the self-rotation angle value of the suspended object at the current node n; When the total cost function When it is minimum, it is the optimal planning path.

8. A tower crane low-altitude path planning device based on configuration space, characterized in that: include: An acquisition unit, used to acquire relevant data of the tower crane, the hoisted object and the obstacles; A three-dimensional configuration space construction unit is used to construct a three-dimensional configuration space of the tower crane's low-altitude hoisting motion according to the tower crane's amplitude degree of freedom, rotation degree of freedom and the self-rotation degree of freedom of the hoisted object; The extended obstacle unit is used to generate the OBB bounding box of the suspended object and the obstacle in Cartesian space, and expand the bounding box of the obstacle according to the self-rotation angle and size of the suspended object to obtain the extended obstacle; A mapping unit is used to map the suspended object, the extended obstacle and the pseudo-obstacle to the three-dimensional configuration space; wherein the pseudo-obstacle is the range of the amplitude degree of freedom and the rotation degree of freedom of the tower crane; specifically: After the obstacle bounding box is expanded, the suspended object, the expanded obstacle and the pseudo obstacle are mapped into the three-dimensional configuration space; The suspended object is mapped to a point P in the three-dimensional configuration space, expressed as: ； Where D is the luffing degree of freedom of the tower crane; is the rotational freedom of the tower crane; is the rotational freedom of the suspended object; Each discrete surface of the extended obstacle in the three-dimensional configuration space is mapped into a convex polygon, and the expression is: ； in, Mapping the vertex coordinates of each plane in the configuration space to the extended obstacle, connecting the vertices to form the extended obstacle in the configuration space; Indicates existence; is the vertex coordinates of the extended obstacle in the Cartesian coordinate system; , are the x-value and y-value of the vertex of the extended obstacle in the Cartesian coordinate system respectively; Next, the pseudo-obstacle is mapped to the configuration space and represented as a rectangle; the pseudo-obstacle is located on each plane of the configuration space. The position of is the same, and its vertex coordinate in the configuration space is expressed as K, which is determined by the amplitude range and the rotation range; A quadtree partitioning unit is used to perform quadtree partitioning on each plane of the mapped three-dimensional configuration space, and to perform recursive collision detection between the quadtree and the extended obstacle to identify collision sub-bodies until a partition termination condition is reached; A three-dimensional grid unit, used to connect the vertices of the same level sub-volumes of adjacent planes and not at the colliding sub-volumes with straight lines to generate a three-dimensional grid; The optimal planning path unit is used to perform path planning based on the three-dimensional grid using the A* algorithm to obtain the optimal planning path, and operate the tower crane according to the optimal planning path.

9. A tower crane low-altitude path planning device based on configuration space, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program, and the computer program can be executed by the processor to implement a tower crane low-altitude path planning method based on a configuration space as described in any one of claims 1-7.

Images