Crane path planning method, device and equipment based on polar coordinate recursive division

Abstract

The application provides a tower crane path planning method, device and equipment based on polar coordinate recursive division, and relates to the field of path planning.The application obtains the related information of a hoisting point, a target point, a tower crane and a hoisted object and obstacles, takes the position of the tower crane as a center of a circle, and establishes a polar coordinate system;then, the hoisting range of the tower crane is obtained in the form of a torus cylinder;then, the hoisting range of the tower crane is divided into several sub-bodies in different ways in a dynamic and recursive manner until a division termination condition is reached;then, under the condition that the hoisting point and the target point are not on the outer boundary frame of the sub-body and under the premise of meeting the division termination condition, the hoisting point and the target point are segmented to obtain a new sub-body with the hoisting point and the target point as vertices;an A* algorithm is adopted in combination with a cost function to calculate an optimal path and to optimize the optimal path;collision detection between obstacles and the hoisted object on the optimized path is performed until the detection result is safe, a planned path is obtained, and the tower crane is controlled to run.The planned path has high operability and safety.

Description

Technical Field

[0001] This invention relates to the field of path planning technology, and more specifically, to a tower crane path planning method, apparatus, and equipment based on polar coordinate recursive partitioning. Background Technology

[0002] Path planning is a critical task when operating tower cranes. Traditional path planning methods rely primarily on the operator's observation and experience, which is tedious and prone to errors. Existing research on tower crane path planning mainly focuses on optimizing path length or planning time beforehand, often employing stochastic algorithms such as Rapid Random Tree Exploration (RRT), probabilistic route graphs, annealing, and Particle Swarm Optimization (PSO). However, it rarely considers the feasibility of the planned route, resulting in the following drawbacks:

[0003] (1) The lack of constraints in the search direction leads to path complexity. Some studies have attempted to use sampling algorithms with directional constraints to address this limitation, but this approach consumes a lot of computational memory and has low algorithm efficiency.

[0004] (2) The limited availability and high cost of high-performance computing devices restrict the application of algorithms in real-world environments;

[0005] (3) Small changes in the state may lead to significant changes in the algorithm results, thus some algorithms have low interpretability and reusability;

[0006] (4) The planned routes are mostly broken lines with many nodes, which leads to frequent swinging of the hook, increasing the risk of collision and energy consumption; and the routes are incompatible with the rotation-amplitude motion characteristics of the tower crane, making it difficult for the operator to operate according to the planned path.

[0007] In view of this, the applicant hereby submits this application after studying the existing technology. Summary of the Invention

[0008] The present invention aims to provide a tower crane path planning method, apparatus and equipment based on polar coordinate recursive partitioning to solve at least one of the above-mentioned problems.

[0009] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0010] A tower crane path planning method based on polar coordinate recursive partitioning includes:

[0011] S1: Obtain relevant information on the lifting point, target point, tower crane, object being lifted, and obstacles; and establish a polar coordinate system with the tower crane position as the center.

[0012] S2, Based on the center of the circle, the maximum radius of the tower crane, and the minimum radius of the tower crane, the lifting range of the tower crane represented by the annular cylinder is obtained;

[0013] S3, Based on the relevant information of the object being lifted and the obstacles, the lifting range of the tower crane is dynamically and recursively divided in different ways until the division termination condition is reached, and the division is generated into several sub-body;

[0014] S4. Determine whether the lifting point and the target point are on the outer bounding box of the sub-body. If not, then under the condition of satisfying the division termination, divide the sub-body by using the lifting point and the target point as vertices to generate a sector-shaped column and obtain a new sub-body.

[0015] S5. Based on the lifting point and the target point, the A* algorithm is used in combination with the cost function to calculate the priority of each node in the path, select the node with the lowest replacement value as the optimal path and optimize it to obtain the optimized path.

[0016] S6. Based on the optimized path, perform collision detection between obstacles and the suspended object within the sub-body area where the optimized path passes through the outer boundary box of the sub-body. If the detection result does not meet the requirements, select the next suboptimal path for optimization and perform collision detection again until the result meets the requirements. Then, use the optimized path as the planned path of the tower crane and control the operation of the tower crane.

[0017] Preferably, when establishing a polar coordinate system, the position of the tower crane is taken as the center of the circle, and the line connecting the center of the circle perpendicular to the ground and upward, and the plane projection position of the center of the circle and the suspended object is taken as the polar axis direction.

[0018] Preferably, the sub-body is a sub-annular cylinder or a sector-shaped cylinder after the division of an annular cylinder, and the different ways of division include angular division, radius division and height division;

[0019] The radius division method is as follows: taking the center of the current column to be divided as the center, the arc length of the current column to be divided is divided according to a preset arc length ratio; wherein, the starting point and ending point of the divided sub-body are respectively the positions of the original radius of the column to be divided at the preset arc length ratio.

[0020] The angle division method is as follows: the angle of the current column to be divided is divided according to a preset angle ratio; wherein, the length of the divided sub-body is the original radius of the column to be divided, and the starting point and ending point of the sub-body are the positions of the inner arc and outer arc of the column to be divided, respectively, corresponding to the angle ratio.

[0021] The height division method is as follows: the height of the column to be divided is divided according to a preset height ratio.

[0022] The lifting range of the tower crane is dynamically and recursively divided in different ways, specifically as follows:

[0023] The division method for each step is determined by dynamic weighting factors. The decision-making process involves dynamically determining the partitioning method and dynamic weighting factors based on the density of obstacles. The calculation formula is:

[0024] ;

[0025] in, This is the slope adjustment factor; Density threshold;

[0026] The obstacle density of the current sub-body. ;

[0027] The number of obstacles within the sector; V is the sector volume, calculated using the following formula:

[0028] ;

[0029] when When the value exceeds the first threshold, it indicates a high obstacle density. The current state is then divided using the radius division method to accurately avoid obstacles, improve spatial resolution, and reduce path length costs. ;

[0030] when When the value is greater than the second threshold but not greater than the first threshold, it indicates that the obstacle distribution in the current state is relatively dispersed and of medium density. The angle-based segmentation method is then selected for segmentation to quickly scan a large area of ​​space, reduce blind spots in path search, and lower time costs. ;

[0031] when When the height is not greater than the second threshold, it indicates that there are fewer obstacles in the current state, which is considered low density. The height division method is selected to reduce unnecessary lifting operations, improve hoisting efficiency, and reduce energy consumption. ;

[0032] Each sub-entity after partitioning is defined as a six-tuple. , represented as:

[0033] ;

[0034] in, This represents the i-th child body. , These are the minimum polar angle and the maximum polar angle of the daughter body, respectively. , These are the maximum radius and minimum radius of the sub-body, respectively. , These are the highest and lowest heights of the sub-body, respectively.

[0035] Preferably, the division termination condition is:

[0036] ;

[0037] ;

[0038] in, , These are the maximum radius and minimum radius of the sub-body, respectively. , These are the highest and lowest heights of the sub-body, respectively. The equivalent collision radius of the suspended object; δ represents the height of the suspended object; δ represents the safety margin.

[0039] Preferably, the equivalent collision radius of the suspended object The calculation method is as follows:

[0040] ;

[0041] Where L, W, and H are the length, width, and height of the object being lifted, respectively.

[0042] Preferably, the cost function combines actual cost and heuristic cost calculation, and S6 specifically includes:

[0043] The priority of each node in the path is sorted according to the calculated cost value;

[0044] The cost function The expression is:

[0045] ;

[0046] ;

[0047] ;

[0048] in, For the actual cost; The cost of heuristics; This is the path length weighting coefficient; For smoothing weighting coefficients; Energy consumption weighting coefficient; This is the time weighting coefficient;

[0049] For path length cost, = + + The penalty path is too long; This represents the total length of the path's amplitude variation. The total length of the rotational motion, To increase the total length of the movement;

[0050] As a smoothness cost, multi-node motion in the path is penalized to suppress abrupt changes in angle and radius, expressed as:

[0051] ;

[0052] in, The angle difference between the current node and the target node; This represents the radius difference between the current node and the target node. The height difference between the current node and the target node; j represents the current node;

[0053] As a penalty for frequent vertical adjustments in energy consumption, the expression is:

[0054] ;

[0055] in, This represents the vertical height difference.

[0056] As a time cost, penalties are imposed for excessive time, expressed as:

[0057] ;

[0058] in, For the velocity of the variable amplitude motion, For rotational speed, To increase speed;

[0059] Indicates the current average radius. ;

[0060] It is the arc length; The current radius; The target radius;

[0061] Based on the lifting point and the target point, when traversing each node in the path using the A* algorithm, two sets are used to represent the node to be traversed and the node that has been traversed, respectively.

[0062] After calculating the cost of each node, a priority queue of nodes is obtained.

[0063] The cost function is selected from the priority queue each time. The node with the smallest value, i.e. the node with the highest priority, is selected as the next node to be traversed, thus obtaining the optimal path composed of the nodes with the highest priority.

[0064] Preferably, the step of optimizing the optimal path is as follows:

[0065] When the optimal path contains two discontinuous points, and these two points are located at the vertices of different sub-bodies with the same radius r and the same height z, then concentric arcs are used to connect these two points to generate a new, better route.

[0066] If the optimal path contains two discontinuous points, and these two points are located at the vertices of different sub-bodies with the same angle θ and the same height z, then the two points are connected by a radius to generate a new, better route.

[0067] If the optimal path contains two discontinuous points, and these two points are located at the vertices of different sub-bodies with the same radius r and the same angle θ, then a height line is used to connect the two points to generate a new, better route.

[0068] The better route generated is called the optimized path.

[0069] Preferably, the specific steps for collision detection between the suspended object and the obstacle are as follows:

[0070] Based on the relevant information about the obstacles, obtain the minimum spherical bounding box of the obstacles, and the radius of the spherical bounding box. Equivalent collision radius with the suspended object The calculation method is the same, with the center of the sphere being the center of mass of the object;

[0071] For nodes in the optimized path With nodes The formed straight path , the straight path Construct it as a cylinder, and the parameters of the constructed cylinder are:

[0072] cylinder radius = Where δ is the safety margin;

[0073] The axis of the cylinder is a line segment ;

[0074] The height of the cylinder is equal to the length of the cylinder. ,Right now ;

[0075] Calculate the bounding box of the obstacle sphere on the axis of the cylinder. Projection region interval in the direction [ , ],like , ]∩[0, ]≠ , If the field is empty, then calculate the axis from the center of the obstacle sphere to the cylinder. The shortest distance d; otherwise, no collision; where, , These are the lower and upper limits of the projection area, respectively;

[0076] If d < + ,in, If the radius of the sphere bounding box of the obstacle is given, a collision will occur.

[0077] For nodes in the optimized path With nodes The resulting arc-shaped path is then divided into n equal parts to generate n arc segments, where the value of n is determined by the maximum radius of the tower crane. The chord length of each arc segment is used to replace the corresponding arc to construct a corresponding cylinder, and collision detection is performed on each arc segment. The method of constructing the cylinder and the collision detection method of each arc segment are the same as those for the straight path.

[0078] If the current optimized path does not meet the safety detection requirements, the next suboptimal path is selected as the optimal path for optimization, and then the safety collision detection is performed until the safety requirements are met.

[0079] The present invention also provides a tower crane path planning device based on polar coordinate recursive partitioning, comprising:

[0080] The acquisition unit is used to acquire relevant information about the lifting point, target point, tower crane, the object being lifted, and obstacles; and to establish a polar coordinate system with the tower crane position as the center.

[0081] The tower crane lifting range unit is used to obtain the tower crane lifting range represented by a circular cylinder based on the center of the circle, the maximum radius of the tower crane, and the minimum radius of the tower crane.

[0082] The sub-body division unit is used to dynamically and recursively divide the lifting range of the tower crane in different ways according to the relevant information of the object being lifted and obstacles, until the division termination condition is reached, and to divide and generate several sub-bodies.

[0083] The new sub-body generation unit is used to determine whether the lifting point and the target point are on the outer bounding box of the sub-body. If not, under the condition of satisfying the division termination, the fan-shaped column is generated with the lifting point and the target point as vertices to obtain a new sub-body.

[0084] The path optimization unit is used to calculate the priority of each node in the path based on the lifting point and the target point, using the A* algorithm and a cost function, and selects the node with the lowest replacement value as the optimal path and optimizes it to obtain the optimized path.

[0085] The path planning unit is used to perform collision detection between obstacles and the suspended object within the sub-body area when the optimized path passes through the outer boundary box of the sub-body, according to the optimized path; if the detection result does not meet the requirements, the next suboptimal path is selected for optimization, and collision detection is performed again until the result meets the requirements. The optimized path is then used as the planned path for the tower crane to control the operation of the tower crane.

[0086] The present invention also provides a tower crane path planning device based on polar coordinate recursive partitioning, including a processor and a memory. The memory stores a computer program that can be executed by the processor to implement the tower crane path planning method based on polar coordinate recursive partitioning as described above.

[0087] The present invention also provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor of the device on which the computer-readable storage medium resides, implement the tower crane path planning method based on polar coordinate recursive partitioning as described above.

[0088] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0089] This invention recursively divides the three-dimensional space of the tower crane's lifting range into sub-circular cylinders or sector cylinders in a polar coordinate system, and dynamically adapts different division methods (radius division / angle division / height division) according to the density of obstacles, so as to conform to the kinematic characteristics of the tower crane and facilitate the actual operation of the operator.

[0090] This invention employs the A* algorithm combined with a cost function to select the optimal path nodes, thereby optimizing the target and ensuring that the planned path balances efficiency and operability. Furthermore, the cost function of this invention introduces a path length cost term to penalize excessively long and redundant hoisting paths; a time cost term to suppress time-consuming movements; an energy consumption cost term to suppress high-energy-consuming movements; and a path smoothness cost term to suppress abrupt changes in angle and radius, reducing the number of inflection points. By considering multiple factors in the tower crane path planning process, this invention improves the efficiency of the planned path.

[0091] When performing collision detection between obstacles and suspended objects, this invention performs deep segmentation and collision detection only in necessary areas, avoiding global detection and reducing the amount of computation and computer memory usage. Attached Figure Description

[0092] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0093] Figure 1 This is a schematic diagram of a tower crane path planning method based on polar coordinate recursive partitioning, provided in Example 1.

[0094] Figure 2 This is a schematic diagram illustrating different ways of dividing the hoisting range, as provided in Example 1.

[0095] Figure 3 This is a schematic diagram illustrating path optimization for different scenarios provided in Example 1.

[0096] Figure 4 This is a schematic diagram of a tower crane path planning device based on polar coordinate recursive partitioning, provided in Embodiment 2.

[0097] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Detailed Implementation

[0098] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0099] Example 1

[0100] Embodiment 1 of the present invention provides a tower crane path planning method based on polar coordinate recursive partitioning, which can be implemented by a tower crane path planning device based on polar coordinate recursive partitioning (hereinafter referred to as path planning device), specifically, executed by one or more processors within the path planning device.

[0101] In this embodiment, the path planning device may be an electronic device equipped with a processor, which carries a computer program for the tower crane path planning method based on polar coordinate recursive partitioning and the computer program can be executed, such as a computer, smartphone, smart tablet, workstation, etc., which are not limited here.

[0102] In this embodiment, a recursive algorithm is a method that calls itself within a function or algorithm. In programming, recursive algorithms are primarily used to solve tasks that can be broken down into smaller, identical problems.

[0103] The A* algorithm is an efficient path search and graph traversal algorithm widely used in game design, robot path planning, and network routing. It finds the shortest path from the starting point to the destination by combining heuristic evaluation and actual cost.

[0104] like Figure 1 As shown, a tower crane path planning method based on polar coordinate recursive partitioning includes steps S1 to S6.

[0105] S1: Obtain relevant information on the lifting point, target point, tower crane, object being lifted, and obstacles; and establish a polar coordinate system with the tower crane position as the center.

[0106] In the information acquisition phase of this embodiment, information such as the lifting point, target point, tower crane position, maximum radius, and minimum radius of the tower crane are acquired; information such as the position and size of the object being lifted is acquired; and information such as the position and size of obstacles are acquired; in order to perform subsequent path planning and collision detection operations.

[0107] Then, with the tower crane position as the center and the direction perpendicular to the ground upward as the polar axis, a polar coordinate system is established by connecting the plane projection positions of the tower crane position and the suspended object position as the other polar axis direction.

[0108] S2, Based on the center of the circle, the maximum radius of the tower crane, and the minimum radius of the tower crane, the lifting range of the tower crane, represented by a circular cylinder, is obtained.

[0109] S3. Based on the relevant information of the object being lifted and the obstacles, the lifting range of the tower crane is dynamically and recursively divided in different ways until the division termination condition is reached, and several sub-body is generated.

[0110] In this step, the toroidal cylinder is recursively divided to generate sub-cylinders, which are sub-toroidal cylinders or sector-shaped cylinders after the toroidal cylinder is divided. Different division methods in each step include angle division, radius division, and height division.

[0111] The radius division method is as follows: based on the current column to be divided (e.g., Figure 2(a) Using the center of the circular or sector-shaped cylinder (within the black solid line area shown in a diagram) as the center, the arc length of the cylinder to be divided is determined according to a preset arc length ratio (e.g., half the original arc length, but other ratios can be used depending on the actual situation, which are not limited here). The starting and ending points of the divided sub-body are respectively the positions of the original radius of the cylinder to be divided at the preset arc length ratio (e.g., ...). Figure 2 (a) is shown by the red dividing line.

[0112] The angle division method is as follows: according to a preset angle ratio (e.g., ... Figure 2 (b) As shown, the angle of the column to be divided is set to half of the original angle. The length of the sub-body after division is the original radius of the column to be divided, and the starting point and ending point of the sub-body are the positions of the inner arc and outer arc of the column to be divided, respectively, according to the angle ratio.

[0113] The height division method is as follows: according to a preset height ratio (e.g., ... Figure 2 (c) As shown, the height is set to half of the original height, and the height of the column to be divided is divided.

[0114] The division method for each step is determined by dynamic weighting factors. The decision-making process involves dynamically determining the partitioning method and dynamic weighting factors based on the density of obstacles. The calculation formula is:

[0115] ;

[0116] in, This is the slope adjustment factor; Density threshold;

[0117] The obstacle density of the current sub-body. ;

[0118] The number of obstacles within the sector; V is the sector volume, calculated using the following formula:

[0119] ;

[0120] In tower crane operations, the area near the tower (such as the main structure of a building) or within a specific radius (such as an equipment storage area) is often densely packed with obstacles, easily forming a high-density area. Therefore, when Greater than the first threshold (e.g.) When the current state is characterized by high obstacle density, the radius division method is selected to precisely avoid obstacles and improve spatial resolution. Further subdivision of the radius direction can also reduce path length costs. .

[0121] when Greater than the second threshold and not greater than the first threshold (e.g.) When the current obstacle distribution is relatively dispersed and of medium density, the angle division method is selected to quickly scan a large area of ​​space and reduce blind spots in path search. The speed of the tower crane's luffing motion is usually faster than its slewing speed, so the angle division can reduce time costs. .

[0122] when Not greater than the second threshold (e.g.) When the current obstacle density is low (few obstacles are present), the height division method is selected to reduce unnecessary lifting operations and improve hoisting efficiency. Tower crane lifting operations consume a lot of energy; finely dividing the height can reduce unnecessary lifting operations and lower energy costs. .

[0123] Each sub-entity after partitioning is defined as a six-tuple. , represented as:

[0124] ;

[0125] in, This represents the i-th child body. , These are the minimum polar angle and the maximum polar angle of the daughter body, respectively. , These are the maximum radius and minimum radius of the sub-body, respectively. , These are the highest and lowest heights of the sub-body, respectively.

[0126] The division termination condition is:

[0127] ;

[0128] ;

[0129] in, , These are the maximum radius and minimum radius of the sub-body, respectively. , These are the highest and lowest heights of the sub-body, respectively. The equivalent collision radius of the suspended object; δ represents the height of the object being lifted; δ is the safety margin, such as 1 meter.

[0130] Among them, the equivalent collision radius of the suspended object The calculation method is as follows:

[0131] ;

[0132] Where L, W, and H are the length, width, and height of the object being lifted, respectively.

[0133] Using this method, the original circular column hoisting range is recursively divided into sub-body segments. The outer boundary frame of each sub-body conforms to the kinematic characteristics of the tower crane, making it easy for the operator to carry out the operation, which represents the possible tower crane running route.

[0134] S4. Determine whether the lifting point and the target point are on the outer bounding box of the sub-body. If not, then under the condition of satisfying the division termination, divide the sub-body by using the lifting point and the target point as vertices to generate a sector-shaped column, thus obtaining a new sub-body.

[0135] Before planning the tower crane's path, the lifting point and target point are processed, and the lifting point is defined as follows: ), target point ), , These represent the angle values ​​of the lifting point and the target point, respectively. , These represent the radius values ​​of the lifting point and the target point, respectively. , These represent the height values ​​of the lifting point and the target point, respectively. If... and If the sector is not on the outer bounding box of the sub-body and there are no available sectors nearby, a temporary partition is performed. or Each of the eight vertices of the child sector column generates a sector column similar to the already generated child sector column, and the size must satisfy the termination condition of the partition.

[0136] S5. Based on the lifting point and the target point, the A* algorithm combined with the cost function is used to calculate the priority of each node in the path, select the node with the lowest replacement value as the optimal path and optimize it to obtain the optimized path.

[0137] In this step, the A* algorithm is an algorithm that finds the path with the lowest cost among multiple nodes on a graph plane. It combines the characteristics of best-first search and Dijkstra's algorithm, efficiently finding the shortest path from the starting point to the destination. An improved A* algorithm is used to find the optimal path, through a cost function... The priority of each node in the path is calculated. The cost function combines the actual cost and the heuristic cost calculation, and the priority of each node in the path is sorted according to the calculated cost value.

[0138] The cost function The expression is:

[0139] ;

[0140] ;

[0141] ;

[0142] in, For the actual cost; The cost is the heuristic cost; n is the number of nodes. This is the path length weighting coefficient; For smoothing weighting coefficients; Energy consumption weighting coefficient; This is the time weighting coefficient;

[0143] For path length cost, = + + The penalty path is too long; This represents the total length of the path's amplitude variation. The total length of the rotational motion, To increase the total length of the movement;

[0144] As a smoothness cost, multi-node motion in the path is penalized to suppress abrupt changes in angle and radius, expressed as:

[0145] ;

[0146] in, The angle difference between the current node and the target node; This represents the radius difference between the current node and the target node. The height difference between the current node and the target node; j represents the current node;

[0147] As a penalty for frequent vertical adjustments in energy consumption, the expression is:

[0148] ;

[0149] in, This represents the vertical height difference.

[0150] As a time cost, penalties are imposed for excessive time, expressed as:

[0151] ;

[0152] in, For the velocity of the variable amplitude motion, For rotational speed, To increase speed;

[0153] Indicates the current average radius. ;

[0154] It is the arc length; The current radius; The target radius;

[0155] Based on the lifting point and the target point, when traversing each node in the path using the A* algorithm, two sets are used to represent the node to be traversed and the node that has been traversed, respectively.

[0156] After calculating the cost of each node, a priority queue of nodes is obtained.

[0157] The cost function is selected from the priority queue each time. The node with the smallest value, i.e. the node with the highest priority, is selected as the next node to be traversed, thus obtaining the optimal path composed of the nodes with the highest priority.

[0158] The optimal path is then optimized to obtain the optimized path.

[0159] The shortest path is the optimal path, and the nodes along the path need to be optimized. There are three scenarios for optimization:

[0160] Scenario 1: For example Figure 3 As shown in (a), when the optimal path contains two discontinuous points: node With nodes If the two points are located at the vertices of different sub-bodies and have the same radius r and height z, then a concentric arc is used to connect the two points to generate a new, better route (i.e., the green dividing line in the figure).

[0161] Scenario 2: For example Figure 3 As shown in (b), when the optimal path contains two discontinuous points: node With nodes If the two points are located at the vertices of different sub-bodies and have the same angle θ and height z, then the two points are connected by the radius to generate a new, better route.

[0162] Scenario 3: For example Figure 3 As shown in (c), when the optimal path contains two discontinuous points: node With nodes If the two points are located at the vertices of different sub-bodies and have the same radius r and the same angle θ, then a height line is used to connect the two points to generate a new, better route.

[0163] The optimal path is generated by traversing every two nodes of the optimal path and optimizing them.

[0164] S6. Based on the optimized path, perform collision detection between obstacles and the suspended object within the sub-body area where the optimized path passes through the outer boundary box of the sub-body. If the detection result does not meet the requirements, select the next suboptimal path for optimization and perform collision detection again until the result meets the requirements. Then, use the optimized path as the planned path of the tower crane and control the operation of the tower crane.

[0165] In this step, after path optimization, a safety check is required, specifically collision detection between the suspended object and obstacles. At this point, the collision detection only needs to check whether a collision occurs between an obstacle within the sub-body whose outer bounding box is selected as the path and the suspended object. In this embodiment, the outer bounding box of the sub-body represents the possible planned path.

[0166] First, based on the relevant information about the obstacles, obtain the minimum spherical bounding box of the obstacles, and the radius of the spherical bounding box. Equivalent collision radius with the suspended object The calculation method is the same, with the center of the sphere being the center of mass of the object.

[0167] For nodes in the optimized path With nodes The formed straight path , the straight path Construct it as a cylinder, and the parameters of the constructed cylinder are:

[0168] cylinder radius = δ; where δ is the safety margin;

[0169] The axis of the cylinder is a line segment ;

[0170] The height of the cylinder is equal to the length of the cylinder. ,Right now .

[0171] Calculate the bounding box of the obstacle sphere on the axis of the cylinder. Projection region interval in the direction [ , ],like , ]∩[0, ]≠ , If the area is empty, meaning the projected region intersects with the cylinder, then the axis from the center of the obstacle sphere to the cylinder is calculated. The shortest distance d; otherwise, no collision. , These are the lower and upper limits of the projection area interval, respectively.

[0172] If d < + ,in, If the radius of the sphere surrounding the obstacle is , a collision will occur.

[0173] For nodes in the optimized path With nodes The resulting arc-shaped path is then divided into n equal parts to generate n arc segments, where the value of n is determined by the maximum radius of the tower crane. The chord length of each arc segment is used to replace the corresponding arc to construct the corresponding cylinder, and collision detection is performed on each arc segment. The method of constructing the cylinder and the collision detection method of each arc segment are the same as those for the straight path.

[0174] If the current optimized path does not meet the safety requirements, the next suboptimal path is selected as the optimal path for optimization, and then safety collision detection is performed again until the safety requirements are met. The optimized path that meets the safety requirements is then used as the planned path for the tower crane to control its operation.

[0175] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0176] This invention recursively divides the three-dimensional space of the tower crane's lifting range into sub-circular cylinders or sector cylinders in a polar coordinate system, and dynamically adapts different division methods (radius division / angle division / height division) according to the density of obstacles, so as to conform to the kinematic characteristics of the tower crane and facilitate the actual operation of the operator.

[0177] This invention employs the A* algorithm combined with a cost function to select the optimal path nodes, thereby optimizing the target and ensuring that the planned path balances efficiency and operability. Furthermore, the cost function of this invention introduces a path length cost term to penalize excessively long and redundant hoisting paths; a time cost term to suppress time-consuming movements; an energy consumption cost term to suppress high-energy-consuming movements; and a path smoothness cost term to suppress abrupt changes in angle and radius, reducing the number of inflection points. By considering multiple factors in the tower crane path planning process, this invention improves the efficiency of the planned path.

[0178] When performing collision detection between obstacles and suspended objects, this invention performs deep segmentation and collision detection only in necessary areas, avoiding global detection and reducing the amount of computation and computer memory usage.

[0179] Example 2

[0180] like Figure 4 As shown, the second embodiment of the present invention also provides a tower crane path planning device based on polar coordinate recursive partitioning, comprising:

[0181] The acquisition unit is used to acquire relevant information about the lifting point, target point, tower crane, the object being lifted, and obstacles; and to establish a polar coordinate system with the tower crane position as the center.

[0182] The tower crane lifting range unit is used to obtain the tower crane lifting range represented by a circular cylinder based on the center of the circle, the maximum radius of the tower crane, and the minimum radius of the tower crane.

[0183] The sub-body division unit is used to dynamically and recursively divide the lifting range of the tower crane in different ways according to the relevant information of the object being lifted and obstacles, until the division termination condition is reached, and to divide and generate several sub-bodies.

[0184] The new sub-body generation unit is used to determine whether the lifting point and the target point are on the outer bounding box of the sub-body. If not, under the condition of satisfying the division termination, the fan-shaped column is generated with the lifting point and the target point as vertices to obtain a new sub-body.

[0185] The path optimization unit is used to calculate the priority of each node in the path based on the lifting point and the target point, using the A* algorithm and a cost function, and selects the node with the lowest replacement value as the optimal path and optimizes it to obtain the optimized path.

[0186] The path planning unit is used to perform collision detection between obstacles and the suspended object within the sub-body area when the optimized path passes through the outer boundary box of the sub-body, according to the optimized path; if the detection result does not meet the requirements, the next suboptimal path is selected for optimization, and collision detection is performed again until the result meets the requirements. The optimized path is then used as the planned path for the tower crane to control the operation of the tower crane.

[0187] Example 3

[0188] The third embodiment of the present invention also provides a tower crane path planning device based on polar coordinate recursive partitioning, which includes a memory and a processor. The memory stores a computer program, which can be executed by the processor to implement the tower crane path planning method based on polar coordinate recursive partitioning as described above.

[0189] Example 4

[0190] The fourth embodiment of the present invention also provides a computer-readable storage medium storing computer-readable instructions. When the computer-readable instructions are executed by the processor of the device where the computer-readable storage medium is located, they implement the tower crane path planning method based on polar coordinate recursive partitioning as described above.

[0191] In the several embodiments provided in this invention, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus and method embodiments described above are merely illustrative. For example, the flowcharts in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the invention. In this regard, each block in the flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0192] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0193] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, electronic device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further restrictions, an element defined by the phrase "comprising a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0194] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0195] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0196] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0197] The use of "first" and "second" in the embodiments is merely to distinguish similar objects and does not represent a specific ordering of objects. It is understood that "first" and "second" can be interchanged in a specific order or sequence where permitted. It should be understood that the objects distinguished by "first" and "second" can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than those illustrated or described herein.

[0198] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A tower crane path planning method based on polar coordinate recursive partitioning, characterized in that, The method comprises the following steps: S1, obtaining the information of the lifting point, the target point, the tower crane, the hoisted object and the obstacle; and establishing a polar coordinate system with the tower crane position as the center; S2, obtaining the tower crane lifting range represented by a torus according to the center, the maximum radius of the tower crane and the minimum radius of the tower crane; S3, dynamically and recursively dividing the tower crane lifting range in different ways according to the information of the hoisted object and the obstacle until the division termination condition is reached, and generating a plurality of sub-bodies; wherein the sub-body is a sub-torus or a sector body after the torus is divided, and the different ways of division include angle division, radius division and height division; wherein the radius division is performed by taking the center of the current to-be-divided body as the center and dividing the arc length of the current to-be-divided body according to a preset arc length ratio; wherein the start point and the end point of the divided sub-body are at the positions of the original radius of the to-be-divided body corresponding to the preset arc length ratio; the angle division is performed by dividing the angle of the current to-be-divided body according to a preset angle ratio; wherein the length of the divided sub-body is the original radius of the to-be-divided body, and the start point and the end point of the sub-body are at the positions of the inner arc and the outer arc of the to-be-divided body corresponding to the angle ratio; the height division is performed by dividing the height of the current to-be-divided body according to a preset height ratio; The dynamic and recursive division of the tower crane lifting range in different ways is as follows: The division manner of each step is determined by a dynamic weight factor , that is, the division manner is dynamically determined according to the density of the obstacles, and the dynamic weight factor The calculation formula of the dynamic weight factor is as follows: ； wherein, is a slope adjustment factor; is a density threshold value; density of obstacles for the current sub-volume, ; N is the number of obstacles within the sector; V is the volume of the sector, calculated by the following equation: ; When When the current state is greater than the first threshold value, indicating a high obstacle density, the radius is divided in a manner selected to accurately avoid obstacles, improve spatial resolution, and reduce path length cost ; When When the current state is greater than the second threshold value and not greater than the first threshold value, the obstacle distribution representing the current state is relatively dispersed, indicating a medium obstacle density, and the angle division manner is selected to divide to quickly scan a large range of space, reduce a blind area of path search, and reduce a time cost ; When When the current state is less than the second threshold, it indicates that there are fewer obstacles, i.e. low obstacle density, and the high-division mode is selected to reduce unnecessary lifting operations, improve lifting efficiency, and reduce energy consumption cost ; Each of the divided sub-bodies is defined as a six-tuple is represented as: ； wherein, represents the i-th sub-body, , are the minimum and maximum polar angles of the sub-body, respectively; , are the maximum and minimum radii of the sub-body, respectively; , are the maximum and minimum heights of the sub-body, respectively; the division termination condition is: ； ； wherein, , are the maximum radius of the sub-body, the minimum radius of the sub-body, respectively; , are the maximum height of the sub-body, the minimum height of the sub-body, respectively; is the equivalent collision radius of the hoisted object; is the height of the hoisted object; δ is the safety margin; S4, judging whether the lifting point and the target point are on the outer boundary frame of the sub-body, if not, generating a sector body with the lifting point and the target point as the vertices under the condition that the division termination condition is met, and obtaining a new sub-body; S5, calculating the priority of each node in the path by using the A* algorithm and combining the cost function according to the lifting point and the target point, selecting the node with the smallest value as the optimal path and optimizing it to obtain an optimized path; S6, performing collision detection between the obstacle and the hoisted object in the region of the sub-body through which the optimized path passes according to the optimized path; if the detection result does not meet the requirements, selecting the next sub-optimal path for optimization and then performing collision detection again until the result meets the requirements, taking the optimized path as the planning path of the tower crane and controlling the operation of the tower crane.

2. The method of claim 1, wherein When establishing the polar coordinate system, the tower crane position is taken as the center, and the vertical upward direction of the center and the line connecting the center and the planar projection position of the hoisted object are taken as the polar axis direction.

3. The method of claim 1, wherein the equivalent collision radius of the hoisted object is calculated as ； Wherein, L, W and H are the length, width and height of the hoisted object, respectively.

4. The method of claim 1, wherein The cost function is calculated by combining the actual cost and the heuristic cost, and S6 is specifically as follows: The priority of each node in the path is sorted according to the calculated cost value; The cost function The expression for the cost function is: ； ； ； wherein, is a practical cost; is a heuristic cost; is a path length weight coefficient; is a smoothing weight coefficient; is an energy consumption weight coefficient; is a time weight coefficient; for path length cost, for total length of path, for total length of turning motion, for total length of lifting motion;​​​​ To smooth the cost, the multi-node motion in the path is penalized to suppress the abrupt changes in angle and radius, expressed as: ； wherein, is the angle difference of the current node and the target node; is the radius difference of the current node and the target node; is the height difference of the current node and the target node; j represents the current node; To the energy consumption cost, the frequent vertical adjustment of energy consumption is punished, and the expression is: ； wherein is the vertical segment height difference; The time cost, the punishment time is too long, the expression is: ； wherein, is the velocity of the amplitude motion, is the rotational velocity, is the lifting velocity; represents the current average radius, ; is the arc length; is the current radius; is the target radius; When traversing each node in the path based on the A* algorithm according to the lifting point and the target point, two sets are used to represent the nodes to be traversed and the nodes that have been traversed, respectively; After calculating the cost value of each node, a priority queue of the nodes is obtained; selecting the cost function from the priority queue each time the node with the smallest value, i.e. the node with the highest priority, is selected as the next node to be traversed, thereby obtaining an optimal path consisting of the nodes with the highest priority.

5. The method of claim 1, wherein The step of optimizing the optimal path is as follows: When the optimal path contains two discontinuous points, and the two points are located in different sub-bodies, if the vertex coordinates of the two points satisfy the same radius r and the same height z, a concentric arc is used to connect the two points to generate a new more optimal route; When the optimal path contains two discontinuous points, and the two points are located in different sub-bodies, if the vertex coordinates of the two points satisfy the same angle θ and the same height z, a radius is used to connect the two points to generate a new more optimal route; When the optimal path contains two discontinuous points, and the two points are located in different sub-bodies, if the vertex coordinates of the two points satisfy the same radius r and the same angle θ, a height line is used to connect the two points to generate a new more optimal route; The generated more optimal route is the optimal path.

6. The method of claim 3, wherein The specific steps of the collision detection between the hoisted object and the obstacle are: According to the relevant information of the obstacle, a minimum sphere bounding box of the obstacle is obtained, the radius of the sphere bounding box The equivalent collision radius of the hoisted object The calculation method is the same as that of the equivalent collision radius of the hoisted object, and the sphere center is the object center For the node of the optimized path With the node The straight line path formed The straight line path is formed The constructed cylinder, the parameters of the constructed cylinder are: Cylinder radius = ; where δ is a safety margin; The cylinder axis is a line segment ; Cylinder height is the length of the cylinder i.e. ; Calculate the bounding sphere of the obstacle sphere in the projection region interval of the cylinder axis , , , if , ]∩[0, ]≠ , , then calculate the shortest distance d from the sphere center of the obstacle sphere to the cylinder axis ; otherwise, no collision; where , are the lower and upper limits of the projection region interval, respectively. if d < r + wherein, is the radius of the sphere bounding box of the obstacle, a collision occurs; For the node of the optimized path With the node The arc-shaped path is formed, the arc-shaped path is equally divided by n, n segments of arcs are generated, and the value of n is determined by the maximum radius of the tower crane; the chord length of each segment of arc is used to replace the corresponding arc to construct a corresponding cylinder, and collision detection of each segment of arc is performed; the cylinder construction method and the collision detection method of each segment of arc are the same as those of the straight line path. If the current optimal path does not meet the safety detection requirement, the next sub-optimal path is selected as the optimal path for optimization, and then the safety collision detection is performed until the safety is met.

7. A tower crane path planning device based on polar coordinate recursive partitioning, characterized by, It comprises: An acquisition unit is configured to acquire information related to the hoisting point, the target point, the tower crane, the hoisted object and the obstacle; An extreme coordinate system is established with the tower crane position as the center; A tower crane hoisting range unit is configured to obtain a tower crane hoisting range represented by a torus cylinder according to the center, the maximum radius of the tower crane and the minimum radius of the tower crane; A sub-body division unit is configured to divide the tower crane hoisting range in different ways dynamically and recursively according to the information related to the hoisted object and the obstacle until a division termination condition is reached, thereby generating a plurality of sub-bodies; A new sub-body generation unit is configured to determine whether the hoisting point and the target point are on the outer boundary frame of the sub-body. If not, a new sub-body is generated by dividing a sector cylinder with the hoisting point and the target point as the vertices under the condition that the division termination condition is met. The sub-body is a sub-torus cylinder or a sector cylinder after the division of the torus cylinder, and the different ways of division include angle division, radius division and height division. The radius division is performed by taking the center of the current to-be-divided cylinder as the center and dividing the arc length of the current to-be-divided cylinder according to a preset arc length ratio. The start point and the end point of the divided sub-body are located at the positions of the original radius of the to-be-divided cylinder corresponding to the preset arc length ratio. The angle division is performed by dividing the angle of the current to-be-divided cylinder according to a preset angle ratio. The length of the divided sub-body is the original radius of the to-be-divided cylinder, and the start point and the end point of the sub-body are located at the positions of the inner arc and the outer arc of the to-be-divided cylinder corresponding to the angle ratio. The height division is performed by dividing the height of the current to-be-divided cylinder according to a preset height ratio. The tower crane hoisting range is divided in different ways dynamically and recursively, and the specific steps are as follows: The division manner of each step is determined by a dynamic weight factor , that is, the division manner is dynamically determined according to the density of the obstacles, and the dynamic weight factor The calculation formula of the dynamic weight factor is: ； wherein, is a slope adjustment factor; is a density threshold value; density of obstacles for the current sub-volume, ; N is the number of obstacles within the sector; V is the volume of the sector, calculated by the following equation: ; When When the current state is greater than the first threshold value, it indicates that the current state is high obstacle density, and the division manner of the radius is selected to accurately avoid obstacles, improve spatial resolution, and reduce path length cost ; When When the current state is greater than the second threshold value and not greater than the first threshold value, the obstacle distribution representing the current state is relatively dispersed, indicating a medium obstacle density, and the angle division manner is selected to divide to quickly scan a large range of space, reduce a blind area of path search, and reduce a time cost ; When When the current state is less than the second threshold, it indicates that there are fewer obstacles, i.e. low obstacle density, and the high-division mode is selected for division, so as to reduce unnecessary lifting operations, improve lifting efficiency, and reduce energy consumption cost ; Each of the divided sub-bodies is defined as a six-tuple is represented as: ； wherein, represents the i-th sub-body, , are the minimum and maximum polar angles of the sub-body, respectively; , are the maximum and minimum radii of the sub-body, respectively; , are the maximum and minimum heights of the sub-body, respectively; The division termination condition is that: ； ； wherein, , respectively the maximum radius of the sub-body, the minimum radius of the sub-body; , respectively the maximum height of the sub-body, the minimum height of the sub-body; the equivalent collision radius of the hoisted object; the height of the hoisted object; δ is the safety margin; A path optimization unit is configured to calculate the priority of each node in the path by using the A* algorithm in combination with a cost function according to the hoisting point and the target point, select the node with the smallest value as the optimal path, and optimize the optimal path to obtain an optimal path. The path planning unit is configured to perform collision detection between the obstacles in the sub-body area where the optimized path passes through the outer bounding box of the sub-body and the hoisted object, according to the optimized path; if the detection result does not meet the requirements, the next sub-optimal path is selected for optimization, and then the collision detection is performed again until the result meets the requirements, the optimized path is taken as the planning path of the tower crane, and the tower crane is controlled to run.

8. A tower crane path planning device based on polar coordinate recursive partitioning, characterized by, The path planning method comprises a processor and a memory, and the memory stores a computer program which can be executed by the processor to implement the path planning method based on polar coordinate recursive division of the tower crane according to any one of claims 1-6.

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