Path planning method and device for bridge crane

Abstract

The present invention discloses a path planning method and device for a bridge crane, and provides a bee colony algorithm for a bridge crane in a three-dimensional environment, which is suitable for a three-dimensional environment with complex obstacles, has the characteristics of fast search speed, few parameters, and strong robustness, and conforms to the operating characteristics of the bridge crane. First, a three-dimensional obstacle space map model is established, and the starting point and the target point are defined. Then, the algorithm parameters are initialized to generate multiple initial paths. The employed bees first optimize the initial path, and then the watch bees use the roulette and anti-roulette parallel selection mechanism to select the employed bees to follow, and optimize the optimized path selected by the employed bees again. The scout bees choose whether to abandon the original path according to the number of times the path value remains unchanged, thereby re-searching and generating a path in the global environment to avoid the algorithm from falling into a local optimal solution. Then find the best path and record it, and iterate continuously to obtain a once-optimized path.

Description

Technical Field

[0001] The invention relates to the technical field of crane control, in particular to a path planning method for a bridge crane. Background Art

[0002] Currently, the common bridge crane path planning methods are as follows: ant colony algorithm, genetic algorithm, particle swarm algorithm and RRT algorithm.

[0003] The ant colony algorithm is an intelligent algorithm that simulates the foraging process of ants. Some inventions apply it to the field of path planning. First, based on the prior information of obstacle distribution, a global obstacle distribution map is obtained, and the environment is modeled using the grid method. Then, the algorithm is used for path planning. The characteristics are: the feasible solution is represented by the walking path of the ants, and information is transmitted by releasing pheromones during the walking process to form a positive feedback mechanism, so that the entire ant colony can reach the food source along the shortest path. However, the algorithm takes a long time to play the role of positive feedback. Therefore, the initial convergence speed of the algorithm is slow.

[0004] The idea of ​​genetic algorithm is to simulate the natural evolution process to search for the optimal solution. When applied to the field of crane path planning, it is also necessary to establish a grid map. The algorithm converts the process of solving the shortest path into the crossover, mutation, and selection of chromosome genes. The characteristics are: the algorithm cannot directly process the parameters of the problem, and must solve the problem through encoding conversion. In addition, the algorithm generally does not require other external information during the evolutionary search process, and only relies on the evaluation function. This algorithm has the problem of coding inaccuracy and low efficiency.

[0005] The particle swarm algorithm is a group collaborative random search algorithm developed based on the foraging behavior of birds. It is applied in the field of path planning and is characterized by fast search speed. Unlike genetic algorithms, it does not have crossover and mutation operations and has fewer parameters to adjust. However, the algorithm still cannot effectively solve the combinatorial optimization problem of path nodes, is prone to fall into local optimality, and has low convergence accuracy.

[0006] The RRT algorithm is a search method based on probability sampling. It is constructed in a special incremental way and can quickly shorten the expected distance between a random state point and the tree. It is characterized by being able to quickly and effectively search high-dimensional space, search blank areas through random sampling points in the state space, and thus find a planning path from the starting point to the target point. However, it is difficult for the algorithm to obtain the optimal solution, and the convergence rate is unknown, and there is a "long tail" effect.

[0007] The above four algorithms are commonly used in the field of crane path planning. Generally, they are improved based on the shortcomings of the algorithms, and then a two-dimensional grid map or a three-dimensional map is established based on the obstacle data. By fixing the operating height of the crane, a collision-free transportation route is planned. However, the planned path has many turning points, or the obtained path is not the shortest path, and the path is not smooth enough. The present invention fully considers the operating characteristics of the bridge crane, comprehensively considers factors such as route smoothness, operating time, obstacle danger level, energy consumption, etc., and proposes an improved artificial bee colony algorithm in a three-dimensional environment space that can intelligently rise or fall according to the height of the obstacle, improves the obstacle avoidance accuracy of the crane path planning, speeds up the planning speed, and the planned path is smooth enough, reducing the frequent braking of the crane and the large and small vehicles, which is more in line with the hoisting path of the crane in the actual environment, improves production efficiency, and ensures safe production. Summary of the invention

[0008] The present invention provides a path planning method for a bridge crane, which is used to solve the problem that the existing algorithm for crane path planning has many turning points, or the obtained path is not the shortest path, and the path is not smooth enough.

[0009] According to one aspect of the present invention, a path planning method for a bridge crane is provided, using a bee colony algorithm in a known three-dimensional environment, the method comprising:

[0010] Step S10, based on the obstacle distribution information in the space, a space map model is established using a three-dimensional equally divided grid method as a basis for path planning;

[0011] Step S20, the algorithm parameters are initialized. The algorithm parameters include the number of nectar sources N, the maximum number of algorithm iterations maxcycle, and the limit number of times the bee colony searches at the nectar source with unchanged fitness value limit. The number of nectar sources is equal to the number of bee colonies. The employed bees and the watch bees each account for half. One nectar source represents one feasible path. The nectar source is calculated according to formula (1): Generate, where j takes the value {1,2,…,D}, j is a component of the D-dimensional solution vector, and are the upper and lower limits of the j-th dimension, and rand(0,1) is a random number between [0,1];

[0012] Step S30: The employed bees start searching. Each employed bee selects a path for optimization according to formula (2): After all hired bees have finished searching, the fitness value of each hired bee's optimized nectar source is calculated. If it is better than the current nectar source, it will be replaced. Otherwise, the number of times the nectar source remains unchanged is increased by 1. When the number is equal to the limit, the nectar source is abandoned and the fitness value is calculated according to formula (3)X i (n) = X min +rand(0,1)(X max -Xmin ) trial≥limit global search for new nectar sources;

[0013] Step S40, the watch bee follows the employed bee according to the selection mechanism. After the employed bee finishes searching, the watch bee needs to select the employed bee to follow according to the roulette and anti-roulette parallel mechanism, and continue to search for the nectar source optimized by the employed bee;

[0014] The roulette and anti-roulette parallel mechanism includes:

[0015] Step S41, first calculate the probability of each nectar source fitness value accounting for the total nectar source fitness value, put them together into a roulette wheel, and randomly generate a random number in the range of (0,1). The watch bee will select the corresponding hired bee in which interval this number falls;

[0016] Step S42, calculate the probability that the reciprocal of each nectar source fitness value accounts for the sum of the reciprocal of the total nectar source fitness value, and after forming a roulette wheel, the watch bee selects a hired bee to follow according to the roulette wheel mechanism;

[0017] Step S43, searching the nectar sources searched by the two employed bees again according to formula (2), then comparing the original nectar source with the two newly generated nectar sources, and retaining the nectar source with a smaller fitness according to the greedy principle;

[0018] Step S50, scout bee search. The scout bee is responsible for searching for new nectar sources globally. When the fitness value of the nectar source searched by the employed bees and the watch bees remains unchanged for the number of times reaching the upper limit, the scout bee chooses to abandon the nectar source and searches for a new nectar source according to formula (3).

[0019] Step S60, after the artificial bees have finished searching, they record the best nectar source, and iterate in a loop according to steps 30, 40, and 50, each time recording the current best nectar source, and comparing it with the best nectar source, and retaining the better one;

[0020] Step S70, determining the number of iterations, if the number of iterations is less than the maxcycle upper limit, returning to step 30, if the number of iterations is equal to the maxcycle upper limit, executing step 80;

[0021] Step S80, displaying the path represented by the best nectar source on the three-dimensional map, which is an optimized path.

[0022] Furthermore, it also includes:

[0023] Step S90, using the node screening rule to remove redundant nodes in the primary path;

[0024] The node screening rules are:

[0025] Step S91, first define the distance s, starting from the starting point, calculate the distance from the current point to the next point and the distance from the next point to the end point, these points are all nodes in the current optimal path, the current point is (x a ,y a ,z a ), the next point is (x b ,y b ,z b ), the end point is (x c ,y c ,z c );

[0026] Step S92, when formula (4) is satisfied When, using formula (5)

[0027] Calculate D n , ensure that the next point is in front of the current point and close to the end point. In order to make the line connecting the current point and the next point not pass through obstacles, the next point must be within the range of s from the current point. All optimized points within the distance s of the current point are considered as feasible solutions for the next point. At this time, take D n The point with the smaller value is taken as the next point, D n The smaller the value, the more optimal the weighting from this point to the current point and the end point.

[0028] Furthermore, it also includes:

[0029] Step S100, using the Bezier curve formula p(t) to smooth the secondary optimization path, the Bezier curve formula p(t) is

[0030]

[0031] Furthermore, it also includes:

[0032] Step S21, before step S30, determines the height of the obstacle. When the height of the obstacle is within a certain threshold, the crane can intelligently choose to bypass or cross the obstacle.

[0033] Furthermore, it also includes:

[0034] Step S22, before step S30, determines whether there is a concave obstacle, and if it is a concave obstacle, virtually fills it.

[0035] A path planning device for a bridge crane, characterized in that a bee colony algorithm is used in a known three-dimensional environment, and the device comprises:

[0036] A three-dimensional map construction module, which uses a three-dimensional equally divided grid method to establish a spatial map model based on obstacle distribution information in the space as a basis for path planning;

[0037] Parameter initialization module, which is used to initialize algorithm parameters. The algorithm parameters include the number of nectar sources N, the maximum number of algorithm iterations maxcycle, and the limit number of times the bee colony searches at the nectar source with unchanged fitness value limit. The number of nectar sources is equal to the number of bee colonies, and the employed bees and the watch bees each account for half. One nectar source represents a feasible path. The nectar source is calculated according to formula (1): Generate, where j takes the value {1,2,…,D}, j is a component of the D-dimensional solution vector, and are the upper and lower limits of the j-th dimension, and rand(0,1) is a random number between [0,1];

[0038] The first search module is used for hired bee search. Each hired bee selects a path for optimization according to formula (2): After all hired bees have finished searching, the fitness value of each hired bee's optimized nectar source is calculated. If it is better than the current nectar source, it will be replaced. Otherwise, the number of times the nectar source remains unchanged is increased by 1. When the number is equal to the limit, the nectar source is abandoned and the fitness value is calculated according to formula (3)X i (n) = X min +rand(0,1)(X max -X min )trial≥limit global search for new nectar sources;

[0039] A second search module, wherein the second search module is used for watch bee search. The watch bee follows the hired bee according to the selection mechanism. After the hired bee has finished searching, the watch bee needs to select the hired bee to follow according to the roulette and anti-roulette parallel mechanism, and continue to search for the nectar source optimized by the hired bee;

[0040] The second search module includes a first calculation module, a second calculation module and a first comparison module;

[0041] The first calculation module is used to calculate the probability of each nectar source fitness value accounting for the total nectar source fitness value, put them together into a roulette wheel, and randomly generate a random number in the range of (0,1). The watch bee selects the corresponding hired bee in which interval the number falls;

[0042] The second calculation module is used to calculate the probability that the reciprocal of each nectar source fitness value accounts for the sum of the reciprocal of the total nectar source fitness values, and after forming a roulette wheel, the watch bee selects a hired bee to follow according to the roulette wheel mechanism;

[0043] The first comparison module is used to search the nectar sources searched by the two employed bees again according to formula (2) at the same time, and then compare the original nectar source and the two newly generated nectar sources, and retain the nectar source with smaller fitness according to the greedy principle;

[0044] The third search module is used for scout bee search. The scout bee is responsible for searching for new nectar sources globally. When the fitness value of the nectar source searched by the employed bees and the watch bees remains unchanged for the number of times reaching the upper limit limit, the scout bee chooses to abandon the nectar source and searches for a new nectar source according to formula (3).

[0045] A first recording module, wherein the first recording module is used to record the best nectar source after the artificial bees have finished searching, and iterates in sequence according to step 30, step 40, and step 50, each time recording the current best nectar source, and comparing it with the best nectar source, and retaining the better one;

[0046] A first judgment module, the first judgment module is used to judge the number of iterations, if the number of iterations is less than the maxcycle upper limit, return to step 30, if the number of iterations is equal to the maxcycle upper limit, execute step 80;

[0047] The identification module is used to indicate the path of the best nectar source representative on a three-dimensional map, which is an optimized path.

[0048] Furthermore, it also includes:

[0049] A first optimization module, wherein the first optimization module uses a node screening rule to remove redundant nodes in a primary path;

[0050] The first optimization module includes:

[0051] The third calculation module first defines the distance s. Starting from the starting point, the third calculation module calculates the distance from the current point to the next point and the distance from the next point to the end point. These points are all nodes in the current optimal path. The current point is (x a ,y a ,z a ), the next point is (x b ,y b ,z b ), the end point is (x c ,y c ,z c );

[0052] The fourth calculation module, when formula (4) is satisfied When , the fourth calculation module uses formula (5) Calculate Dn to ensure that the next point is in front of the current point and close to the end point. In order to ensure that the line connecting the current point and the next point does not pass through obstacles, the next point must be within the range of s from the current point. All optimized points within the distance s of the current point are considered feasible solutions for the next point. At this time, the point with a small Dn value is taken as the next point. The smaller the Dn value, the better the weighted distance from the point to the current point and the end point. This method is used to select nodes and optimize the path length. By adjusting the distance s, the number of nodes in the remaining path can be controlled.

[0053] Furthermore, it also includes:

[0054] The second optimization module uses the Bezier curve formula p(t) to smooth the secondary optimization path. The Bezier curve formula p(t) is:

[0055]

[0056] Furthermore, it also includes:

[0057] The second judgment module is used to judge the height of the obstacle. When the height of the obstacle is within a certain threshold, the crane can intelligently choose to bypass or cross it.

[0058] Furthermore, it also includes:

[0059] The third judgment module is used to judge a concave obstacle, and if it is a concave obstacle, it is virtually filled.

[0060] Beneficial effects of the present invention

[0061] A bee colony algorithm for bridge cranes in a three-dimensional environment is provided. This method is suitable for three-dimensional environments with complex obstacles and has the characteristics of fast search speed, few parameters and strong robustness. The searched path is short and smooth, which conforms to the operating characteristics of the bridge crane. First, a three-dimensional obstacle space map model is established, the starting point and the target point are defined, and then the algorithm parameters are initialized to generate multiple initial paths. The employed bees first optimize the initial path, and then the watch bees use the roulette and anti-roulette parallel selection mechanism to select the employed bees to follow, and optimize the optimized path selected by the employed bees again. After the watch bees have completed the optimization, the scout bees choose whether to abandon the original path according to the number of times the path value remains unchanged, so as to re-search and generate a path in the global environment to avoid the algorithm from falling into the local optimal solution. Then find the best path and record it, and iterate continuously to update the shortest path to obtain a once-optimized path. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0063] Figure 1 A flowchart of the path planning method for a bridge crane according to a specific embodiment of the present invention;

[0064] Figure 2 A three-dimensional view of a motion trajectory of a path planning method for a bridge crane after the first optimization in a specific embodiment of the present invention;

[0065] Figure 3 A three-dimensional view of a motion trajectory of a path planning method for a bridge crane after the second optimization in a specific embodiment of the present invention;

[0066] Figure 4 A three-dimensional view of a motion trajectory of a path planning method for a bridge crane according to a specific embodiment of the present invention after the third optimization;

[0067] Figure 5 A flowchart of the path planning method for a bridge crane according to another specific embodiment of the present invention;

[0068] Figure 6 It is a structural framework diagram of a path planning device for a bridge crane according to a specific embodiment of the present invention;

[0069] Figure 7 It is a structural framework diagram of a path planning device for a bridge crane in another specific embodiment of the present invention;

[0070] In the figure: 10-three-dimensional map construction module; 20-parameter initialization module; 21-second judgment module; 22-third judgment module; 30-first search module; 40-second search module; 50-third search module; 60-first recording module; 70-first judgment module; 80-identification module; 90-first optimization module; 100-second optimization module. DETAILED DESCRIPTION

[0071] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0072] It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meanings as commonly understood by ordinary technicians in the technical field to which this application belongs.

[0073] In the present invention, unless otherwise specified, the directional words used, such as "up, down, top, bottom", usually refer to the directions shown in the drawings, or to the components themselves in the vertical, perpendicular or gravity directions; similarly, for ease of understanding and description, "inside and outside" refer to the inside and outside relative to the outline of each component itself, but the above-mentioned directional words are not used to limit the present invention.

[0074] See also Figure 1 As shown, a specific implementation of a path planning method for a bridge crane of the present invention.

[0075] A path planning method for a bridge crane adopts a bee colony algorithm in a known three-dimensional environment, and the method comprises:

[0076] Step S10, based on the obstacle distribution information in the space, a space map model is established using a three-dimensional equally divided grid method as a basis for path planning;

[0077] Step S20, the algorithm parameters are initialized. The algorithm parameters include the number of nectar sources N, the maximum number of algorithm iterations maxcycle, and the limit number of times the bee colony searches at the nectar source with unchanged fitness value limit. The number of nectar sources is equal to the number of bee colonies. The employed bees and the watch bees each account for half. One nectar source represents one feasible path. The nectar source is calculated according to formula (1): Generate, where j takes the value {1,2,…,D}, j is a component of the D-dimensional solution vector, and are the upper and lower limits of the j-th dimension, and rand(0,1) is a random number between [0,1];

[0078] Step S30: The employed bees start searching. Each employed bee selects a path for optimization according to formula (2): After all hired bees have finished searching, the fitness value of each hired bee's optimized nectar source is calculated. If it is better than the current nectar source, it will be replaced. Otherwise, the number of times the nectar source remains unchanged is increased by 1. When the number is equal to the limit, the nectar source is abandoned and the fitness value is calculated according to formula (3)X i (n) = X min +rand(0,1)(X max -X min )trial≥limit global search for new nectar sources;

[0079] Step S40, the watch bee follows the employed bee according to the selection mechanism. After the employed bee finishes searching, the watch bee needs to select the employed bee to follow according to the roulette and anti-roulette parallel mechanism, and continue to search for the nectar source optimized by the employed bee;

[0080] The roulette and anti-roulette parallel mechanism includes:

[0081] Step S41, first calculate the probability of each nectar source fitness value accounting for the total nectar source fitness value, put them together into a roulette wheel, and randomly generate a random number in the range of (0,1). The watch bee will select the corresponding hired bee in which interval this number falls;

[0082] Step S42, calculate the probability that the reciprocal of each nectar source fitness value accounts for the sum of the reciprocal of the total nectar source fitness value, and after forming a roulette wheel, the watch bee selects a hired bee to follow according to the roulette wheel mechanism;

[0083] Step S43, searching the nectar sources searched by the two employed bees again according to formula (2), then comparing the original nectar source with the two newly generated nectar sources, and retaining the nectar source with a smaller fitness according to the greedy principle;

[0084] Step S50, scout bee search. The scout bee is responsible for searching for new nectar sources globally. When the fitness value of the nectar source searched by the employed bees and the watch bees remains unchanged for the number of times reaching the upper limit, the scout bee chooses to abandon the nectar source and searches for a new nectar source according to formula (3).

[0085] Step S60, after the artificial bees have finished searching, they record the best nectar source, and iterate in a loop according to steps 30, 40, and 50, each time recording the current best nectar source, and comparing it with the best nectar source, and retaining the better one;

[0086] Step S70, determining the number of iterations, if the number of iterations is less than the maxcycle upper limit, returning to step 30, if the number of iterations is equal to the maxcycle upper limit, executing step 80;

[0087] Step S80, see Figure 2 As shown, the path representing the best nectar source is shown on a three-dimensional map, which is an optimized path.

[0088] A bee colony algorithm for bridge cranes in a three-dimensional environment is provided. This method is suitable for three-dimensional environments with complex obstacles and has the characteristics of fast search speed, few parameters and strong robustness. The searched path is short and smooth, which conforms to the operating characteristics of the bridge crane. First, a three-dimensional obstacle space map model is established, the starting point and the target point are defined, and then the algorithm parameters are initialized to generate multiple initial paths. The employed bees first optimize the initial path, and then the watch bees use the roulette and anti-roulette parallel selection mechanism to select the employed bees to follow, and optimize the optimized path selected by the employed bees again. After the watch bees have completed the optimization, the scout bees choose whether to abandon the original path according to the number of times the path value remains unchanged, so as to re-search and generate a path in the global environment to avoid the algorithm from falling into the local optimal solution. Then find the best path and record it, and iterate continuously to update the shortest path to obtain a once-optimized path.

[0089] Furthermore, the path planning method of the bridge crane also includes:

[0090] Step S90, see Figure 3 As shown, the node screening rule is used to remove redundant nodes in a path;

[0091] The node screening rules are:

[0092] Step S91, first define the distance s, starting from the starting point, calculate the distance from the current point to the next point and the distance from the next point to the end point, these points are all nodes in the current optimal path, the current point is (x a ,y a ,z a ), the next point is (x b ,y b,z b ), the end point is (x c ,y c ,z c );

[0093] Step S92, when formula (4) is satisfied When, using formula (5)

[0094] Calculate D n , ensure that the next point is in front of the current point and close to the end point. In order to make the line connecting the current point and the next point not pass through obstacles, the next point must be within the range of s from the current point. All optimized points within the distance s of the current point are considered as feasible solutions for the next point. At this time, take D n The point with the smaller value is taken as the next point, D n The smaller the value, the more optimal the weighting of the point to the current point and the end point. This method is used to select nodes and optimize the path length. By adjusting the distance s, the number of nodes in the remaining path can be controlled.

[0095] Use the node screening rule to remove redundant nodes in this path, complete the secondary optimization, reduce the number of path nodes, and shorten the length again.

[0096] Furthermore, the path planning method of the bridge crane also includes:

[0097] Step S100, see Figure 4 As shown in the figure, the quadratic optimization path is smoothly optimized using the Bezier curve formula. After the redundant nodes are eliminated, the path contains fewer nodes and is smoothed using multiple segments of cubic Bezier curves. The Bezier curve uses the path nodes as control points and follows formula (6):

[0098]

[0099] Smoothing the secondary optimization path.

[0100] Combined with Bezier curves, the path nodes are used as the control points of the cubic Bezier curves, and multiple segments of cubic Bezier curves are used for optimization. The final path is shorter and smoother, which conforms to the motion characteristics of bridge cranes, improves the obstacle avoidance accuracy of crane path planning, speeds up planning, improves production efficiency, and ensures safe production.

[0101] Furthermore, the path planning method of the bridge crane also includes:

[0102] Step S21, see Figure 5 As shown, before step S30, the height of the obstacle is determined. When the height of the obstacle is within a certain threshold, the crane can intelligently choose to bypass or cross.

[0103] Furthermore, the path planning method of the bridge crane also includes:

[0104] Step S22, see Figure 5 As shown, before step S30, a concave obstacle is determined, and if it is a concave obstacle, it is virtually filled.

[0105] See also Figure 6 As shown, a path planning device for a bridge crane adopts a bee colony algorithm in a known three-dimensional environment, and the device includes:

[0106] A three-dimensional map construction module 10, which uses a three-dimensional equally divided grid method to establish a spatial map model based on obstacle distribution information in the space as a basis for path planning;

[0107] Parameter initialization module 20, the parameter initialization module 20 is used to initialize algorithm parameters, the algorithm parameters include the number of nectar sources N, the maximum number of algorithm iterations maxcycle, and the limit number of times the bee colony searches at the nectar source with unchanged fitness value limit, where the number of nectar sources is equal to the number of bee colonies, the employed bees and the watch bees each account for half, one nectar source represents a feasible path, and the nectar source is calculated according to formula (1): Generate, where j takes the value {1,2,…,D}, j is a component of the D-dimensional solution vector, and are the upper and lower limits of the j-th dimension, and rand(0,1) is a random number between [0,1];

[0108] The first search module 30 is used for hired bee search. Each hired bee selects a path for optimization. According to formula (2): After all hired bees have finished searching, the fitness value of each hired bee's optimized nectar source is calculated. If it is better than the current nectar source, it will be replaced. Otherwise, the number of times the nectar source remains unchanged is increased by 1. When the number is equal to the limit, the nectar source is abandoned and the fitness value is calculated according to formula (3)X i (n) = X min +rand(0,1)(X max -X min )trial≥limit global search for new nectar sources;

[0109] The second search module 40 is used for the watch bee search. The watch bee follows the employed bee according to the selection mechanism. After the employed bee has finished searching, the watch bee needs to select the employed bee to follow according to the roulette and anti-roulette parallel mechanism, and continue to search for the nectar source optimized by the employed bee.

[0110] The second search module 40 includes a first calculation module, a second calculation module and a first comparison module;

[0111] The first calculation module is used to calculate the probability of each nectar source fitness value accounting for the total nectar source fitness value, put them together into a roulette wheel, and randomly generate a random number in the range of (0,1). The watch bee selects the corresponding hired bee in which interval the number falls;

[0112] The second calculation module is used to calculate the probability that the reciprocal of each nectar source fitness value accounts for the sum of the reciprocal of the total nectar source fitness values, and after forming a roulette wheel, the watch bee selects a hired bee to follow according to the roulette wheel mechanism;

[0113] The first comparison module is used to search the nectar sources searched by the two employed bees again according to formula (2) at the same time, and then compare the original nectar source and the two newly generated nectar sources, and retain the nectar source with smaller fitness according to the greedy principle;

[0114] The third search module 50 is used for scout bee search. The scout bee is responsible for searching for new nectar sources globally. When the fitness value of the nectar source searched by the employed bees and the watch bees remains unchanged for the number of times reaching the upper limit limit, the scout bee chooses to abandon the nectar source and searches for a new nectar source according to formula (3).

[0115] The first recording module 60 is used to record the best nectar source after the artificial bees have finished searching, and iterates according to step 30, step 40, and step 50 in sequence, each time recording the current best nectar source and comparing it with the best nectar source, and retaining the better one;

[0116] A first judgment module 70, the first judgment module 70 is used to judge the number of iterations, if the number of iterations is less than the maxcycle upper limit, return to step 30, if the number of iterations is equal to the maxcycle upper limit, execute step 80;

[0117] The identification module 80 is used to indicate the path of the best nectar source representative on a three-dimensional map, which is an optimized path.

[0118] Furthermore, the path planning device of the bridge crane also includes:

[0119] A first optimization module 90, wherein the first optimization module 90 uses a node screening rule to remove redundant nodes in a primary path;

[0120] The first optimization module 90 includes:

[0121] The third calculation module first defines the distance s. Starting from the starting point, the third calculation module calculates the distance from the current point to the next point and the distance from the next point to the end point. These points are all nodes in the current optimal path. The current point is (x a ,y a ,z a ), the next point is (x b,y b ,z b ), the end point is (x c ,y c ,z c );

[0122] The fourth calculation module, when formula (4) is satisfied When , the fourth calculation module uses formula (5) Calculate D n , ensure that the next point is in front of the current point and close to the end point. In order to make the line connecting the current point and the next point not pass through obstacles, the next point must be within the range of s from the current point. All optimized points within the distance s of the current point are considered as feasible solutions for the next point. At this time, take D n The point with the smaller value is taken as the next point, D n The smaller the value, the more optimal the weighting of the point to the current point and the end point. This method is used to select nodes and optimize the path length. By adjusting the distance s, the number of nodes in the remaining path can be controlled.

[0123] Furthermore, the path planning device of the bridge crane also includes:

[0124] The second optimization module 100 uses the Bezier curve to smoothly optimize the secondary optimization path. After the redundant nodes are eliminated, the path contains fewer nodes and is smoothed using multiple segments of cubic Bezier curves. The Bezier curve uses the path nodes as control points and is calculated according to formula (6):

[0125]

[0126] Smooth the path.

[0127] Furthermore, the path planning device of the bridge crane also includes:

[0128] The second judgment module 21, please refer to Figure 7 As shown, the second judgment module 21 is used to judge the height of the obstacle. When the height of the obstacle is within a certain threshold, the crane can intelligently choose to bypass or cross it.

[0129] Furthermore, the path planning device of the bridge crane also includes:

[0130] The third judgment module 22, please refer to Figure 7 As shown, the third judgment module 22 is used to judge a concave obstacle, and if it is a concave obstacle, it is virtually filled.

[0131] Obviously, the above-described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0132] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0133] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0134] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A path planning method for a bridge crane, characterized in that: Using a bee colony algorithm in a known three-dimensional environment, the method includes: Step S10, based on the obstacle distribution information in the space, a space map model is established using a three-dimensional equally divided grid method as a basis for path planning; Step S20, the algorithm parameters are initialized. The algorithm parameters include the number of nectar sources N, the maximum number of algorithm iterations maxcycle, and the limit number of times the bee colony searches at the nectar source with unchanged fitness value limit. The number of nectar sources is equal to the number of bee colonies. The employed bees and the watch bees each account for half. One nectar source represents one feasible path. The nectar source is calculated according to formula (1): Generate, where j takes the value {1, 2, ..., D}, j is a component of the D-dimensional solution vector, and are the upper and lower limits of the j-th dimension, and rand(0,1) is a random number between [0,1]; Step S30: The employed bees start searching. Each employed bee selects a path for optimization according to formula (2): After all hired bees have finished searching, the fitness value of each hired bee's optimized nectar source is calculated. If it is better than the current nectar source, it will be replaced. Otherwise, the number of times the nectar source remains unchanged is increased by 1. When the number is equal to the limit, the nectar source is abandoned and the fitness value is calculated according to formula (3)X i (n) = X min +rand(0,1)(X max -X min )trial≥limit global search for new honey sources; Step S40, the watch bee follows the employed bee according to the selection mechanism. After the employed bee finishes searching, the watch bee needs to select the employed bee to follow according to the roulette and anti-roulette parallel mechanism, and continue to search for the nectar source optimized by the employed bee; The roulette and anti-roulette parallel mechanism includes: Step S41, first calculate the probability of each nectar source fitness value accounting for the total nectar source fitness value, put them together into a roulette wheel, and randomly generate a random number in the range of (0, 1). The watch bee will select the corresponding hired bee in which interval this number falls; Step S42, calculate the probability that the reciprocal of each nectar source fitness value accounts for the sum of the reciprocal of the total nectar source fitness value, and after forming a roulette wheel, the watch bee selects a hired bee to follow according to the roulette wheel mechanism; Step S43, searching the nectar sources searched by the two employed bees again according to formula (2), then comparing the original nectar source with the two newly generated nectar sources, and retaining the nectar source with a smaller fitness according to the greedy principle; Step S50, scout bees search. Scout bees are responsible for searching for new nectar sources globally. When the fitness values ​​of nectar sources searched by employed bees and watch bees have not changed for the number of times reaching the upper limit, the nectar source is abandoned and the scout bees search for new nectar sources according to formula (3). Step S60, after the artificial bees have finished searching, they record the best nectar source, and iterate in a loop according to steps 30, 40, and 50, each time recording the current best nectar source, and comparing it with the best nectar source, and retaining the better one; Step S70, determining the number of iterations, if the number of iterations is less than the maxcycle upper limit, returning to step 30, if the number of iterations is equal to the maxcycle upper limit, executing step 80; Step S80, displaying the path represented by the best nectar source on the three-dimensional map, which is an optimized path.

2. The path planning method for a bridge crane according to claim 1, characterized in that: Also includes: Step S90, using the node screening rule to remove redundant nodes in the primary path; The node screening rules are: Step S91, first define the distance s, starting from the starting point, calculate the distance from the current point to the next point and the distance from the next point to the end point, these points are all nodes in the current optimal path, the current point is (x a ,y a , z a ), the next point is (x b ,y b , z b ), the end point is (x c ,y c , z c ); Step S92, when formula (4) is satisfied When, using formula (5) Calculate D n , ensure that the next point is in front of the current point and close to the end point. In order to make the line connecting the current point and the next point not pass through obstacles, the next point must be within the range of s from the current point. All optimized points within the distance s of the current point are considered as feasible solutions for the next point. At this time, take D n The point with the smaller value is taken as the next point, D n The smaller the value, the more optimal the weighting from this point to the current point and the end point.

3. The path planning method for a bridge crane according to claim 2, characterized in that: Also includes: Step S100, using the Bezier curve formula p(t) to smooth the secondary optimization path, the Bezier curve formula p(t) is 4. The path planning method for a bridge crane according to claim 3, characterized in that: Also includes: Step S21, before step S30, determines the height of the obstacle. When the height of the obstacle is within a certain threshold, the crane can intelligently choose to bypass or cross the obstacle.

5. The path planning method for a bridge crane according to claim 3, characterized in that: Also includes: Step S22, before step S30, determines whether there is a concave obstacle, and if it is a concave obstacle, virtually fills it.

6. A path planning device for a bridge crane, characterized in that: Using a bee colony algorithm in a known three-dimensional environment, the device includes: A three-dimensional map construction module, which uses a three-dimensional equally divided grid method to establish a spatial map model based on obstacle distribution information in the space as a basis for path planning; Parameter initialization module, which is used to initialize algorithm parameters. The algorithm parameters include the number of nectar sources N, the maximum number of algorithm iterations maxcycle, and the limit number of times the bee colony searches at the nectar source with unchanged fitness value limit. The number of nectar sources is equal to the number of bee colonies, and the employed bees and the watch bees each account for half. One nectar source represents a feasible path. The nectar source is calculated according to formula (1): Generate, where j takes the value {1, 2, ..., D}, j is a component of the D-dimensional solution vector, and are the upper and lower limits of the j-th dimension, and rand(0,1) is a random number between [0,1]; The first search module is used for hired bee search. Each hired bee selects a path for optimization according to formula (2): After all hired bees have finished searching, the fitness value of each hired bee's optimized nectar source is calculated. If it is better than the current nectar source, it will be replaced. Otherwise, the number of times the nectar source remains unchanged is increased by 1. When the number is equal to the limit, the nectar source is abandoned and the fitness value is calculated according to formula (3)X i (n) = X min +rand(0,1)(X max -X min )trial≥limit global search for new honey sources; A second search module, wherein the second search module is used for watch bee search. The watch bee follows the hired bee according to the selection mechanism. After the hired bee has finished searching, the watch bee needs to select the hired bee to follow according to the roulette and anti-roulette parallel mechanism, and continue to search for the nectar source optimized by the hired bee; The second search module includes a first calculation module, a second calculation module and a first comparison module; The first calculation module is used to calculate the probability of each nectar source fitness value accounting for the total nectar source fitness value, put them together into a roulette wheel, and randomly generate a random number in the range of (0, 1). The watch bee selects the corresponding hired bee in which interval the number falls; The second calculation module is used to calculate the probability that the reciprocal of each nectar source fitness value accounts for the sum of the reciprocal of the total nectar source fitness values, and after forming a roulette wheel, the watch bee selects a hired bee to follow according to the roulette wheel mechanism; The first comparison module is used to search the nectar sources searched by the two employed bees again according to formula (2) at the same time, and then compare the original nectar source and the two newly generated nectar sources, and retain the nectar source with smaller fitness according to the greedy principle; The third search module is used for scout bee search. The scout bee is responsible for searching for new nectar sources globally. When the fitness value of the nectar source searched by the employed bees and the watch bees remains unchanged for the number of times reaching the upper limit, the scout bee chooses to abandon the nectar source and searches for a new nectar source according to formula (3); A first recording module, wherein the first recording module is used to record the best nectar source after the artificial bees have finished searching, and iterates in sequence according to step 30, step 40, and step 50, each time recording the current best nectar source, and comparing it with the best nectar source, and retaining the better one; A first judgment module, the first judgment module is used to judge the number of iterations, if the number of iterations is less than the maxcycle upper limit, return to step 30, if the number of iterations is equal to the maxcycle upper limit, execute step 80; The identification module is used to indicate the path of the best nectar source representative on a three-dimensional map, which is an optimized path.

7. The path planning device for a bridge crane according to claim 6, characterized in that: Also includes: A first optimization module, wherein the first optimization module uses a node screening rule to remove redundant nodes in a primary path; The first optimization module includes: The third calculation module first defines the distance s. Starting from the starting point, the third calculation module calculates the distance from the current point to the next point and the distance from the next point to the end point. These points are all nodes in the current optimal path. The current point is (x a ,y a , z a ), the next point is (x b ,y b , z b ), the end point is (x c ,y c , z c ); The fourth calculation module, when formula (4) is satisfied When , the fourth calculation module uses formula (5) Calculate Dn to ensure that the next point is in front of the current point and close to the end point. In order to ensure that the line connecting the current point and the next point does not pass through obstacles, the next point must be within the range of s from the current point. All optimized points within the distance s of the current point are considered feasible solutions for the next point. At this time, the point with a small Dn value is taken as the next point. The smaller the Dn value, the better the weighted distance from the point to the current point and the end point. This method is used to select nodes and optimize the path length. By adjusting the distance s, the number of nodes in the remaining path can be controlled.

8. The path planning device for a bridge crane according to claim 7, characterized in that: Also includes: The second optimization module uses the Bezier curve formula p(t) to smooth the secondary optimization path. The Bezier curve formula p(t) is:

9. The path planning device for a bridge crane according to claim 8, characterized in that: Also includes: The second judgment module is used to judge the height of the obstacle. When the height of the obstacle is within a certain threshold, the crane can intelligently choose to bypass or cross it.

10. The path planning device for a bridge crane according to claim 9, characterized in that: Also includes: The third judgment module is used to judge a concave obstacle, and if it is a concave obstacle, it is virtually filled.

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