CN117555341B - Deep sea mining vehicle path planning method and system based on improved ant colony algorithm - Google Patents
Deep sea mining vehicle path planning method and system based on improved ant colony algorithm Download PDFInfo
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Abstract
The invention relates to the technical field of ocean engineering, and particularly provides a deep sea mining vehicle path planning method and system based on an improved ant colony algorithm. The planning method comprises the following steps: the method comprises the steps of obtaining a three-dimensional environment model of a deep sea submarine mining area topography, wherein the three-dimensional environment model comprises a plurality of three-dimensional topography coordinates, calculating a gradient value of each three-dimensional topography coordinate based on the three-dimensional topography model, building a two-dimensional grid model of the deep sea mining area based on the gradient value of each three-dimensional topography coordinate, and introducing a corner heuristic function into an ant colony algorithm according to the two-dimensional grid model to obtain an optimal path of the deep sea mining vehicle. Under the condition of adopting the technical scheme, the method for planning the deep sea mining vehicle path based on the improved ant colony algorithm can avoid more turning times, reduce the energy consumption of the mining vehicle, improve the quality of the optimal solution and further obtain the optimal path of the deep sea mining vehicle.
Description
Technical Field
The invention relates to the technical field of ocean engineering, and particularly provides a deep sea mining vehicle path planning method and system based on an improved ant colony algorithm.
Background
The deep sea is rich in mineral resources such as polymetallic nodules, cobalt-rich crusts, polymetallic sulfides and the like, wherein the polymetallic nodules are rich in rare noble metal elements such as manganese, copper, cobalt, nickel and the like, and the deep sea has great commercial exploitation value. Unlike cobalt-rich crust and polymetallic sulfide ore deposit, deep sea polymetallic nodule is deposited on the surface of submarine sediment with water depth of 4000-6000 m, and is usually in semi-buried state, and the surface of the ore area is soft sediment with flat overall topography accompanied by small protrusion obstacle and gully.
The path planning is an important basic technology for guaranteeing that the deep sea mining vehicle can safely finish mining operation, and has important practical significance for the deep sea mining vehicle to efficiently finish mining operation, reduce energy consumption and safely run. The reasonable path planning scheme can ensure that the mining vehicle can safely run in a given submarine mining area according to a preset path, effectively reduce the distance and time cost of the mining vehicle in the process of mining site transfer and ore conveying, and reduce the running risk.
In the prior art, an optimal path of deep sea mining is generally found through an ant colony algorithm, and the basic thought of the ant colony algorithm is as follows: the walking path of the ants is used for representing the feasible solution of the problem to be optimized, all paths of the whole ant group form a solution space of the problem to be optimized, the amount of pheromones released by ants with shorter paths is more, the concentration of the pheromones accumulated on the shorter paths is gradually increased along with the advancement of time, and the number of the ants selecting the paths is increased. Finally, the whole ant is concentrated on the optimal path under the action of positive feedback, and the optimal solution of the problem to be optimized is correspondingly obtained. However, in the actual working environment of the mining vehicle, if the number of turns of the optimal path found by the ant colony algorithm is too large, the path track is not smooth, the energy consumption in the traveling process of the mining vehicle is increased, and the traveling efficiency is reduced.
Accordingly, there is a need in the art for a deep sea mining vehicle path planning method and system based on an improved ant colony algorithm to solve the above-mentioned technical problems.
Disclosure of Invention
The invention aims to solve the technical problems that the existing ant colony algorithm has excessive path turning times, unsmooth path track, larger energy consumption in the walking process, lower walking efficiency and the like in the path planning of deep sea mining.
The invention provides a deep sea mining vehicle path planning method based on an improved ant colony algorithm, which comprises the following steps:
S1, acquiring a three-dimensional environment model of the topography of a deep sea submarine mining area, wherein the three-dimensional environment model comprises a plurality of three-dimensional topography coordinates,
S2, calculating the gradient value of each three-dimensional terrain coordinate based on the three-dimensional terrain model,
S3, establishing a two-dimensional grid model of the deep sea mining area based on the gradient value of each three-dimensional topographic coordinate,
And S4, introducing a corner heuristic function into the ant colony algorithm according to the two-dimensional grid model to obtain an optimal path of the deep sea mining vehicle.
In a specific embodiment of the above path planning method, the step of introducing a corner heuristic function into the ant colony algorithm according to the two-dimensional grid model to obtain an optimal path of the deep sea mining vehicle further includes:
Acquiring the number of ants, determining the initial point positions and the target point positions of the ants in the two-dimensional grid model,
Based on the ant colony algorithm introducing the corner heuristic function, the path information from the starting point position to the target point position of the current ant is obtained,
Based on the number of ants, path information of all ants from the starting point position to the target point position is acquired,
And selecting optimal path information from the initial point position to the target point position of all ants.
In a specific embodiment of the above path planning method, the step of "obtaining path information of the current ant from the start point position to the target point position based on the ant colony algorithm introducing the corner heuristic function" further includes:
Referring to a pseudo-random proportion rule, in the path of the current ant from the starting point position to the target point position, the trafficability of eight nodes around the current node and the transition probability of each trafficable node are calculated,
Ant k at node (i, j) selects the next passable point according to the following formula,In the/>Is a pseudo-random proportion rule, k is iteration times, m is ants in the current iteration process,/>, andThe method is characterized in that the method is the state transition probability, i, j is a current path node of ants, alpha is a pheromone importance degree parameter, beta is a heuristic factor importance degree parameter, gamma is a heuristic function importance degree parameter representing a turning angle, q is a transition probability coefficient, q 0 is a transition probability coefficient threshold value, and T (k) is a turning angle heuristic function so as to avoid more turning times; wherein the transition probability formula represents the transition probability of ant k after a period of time t has elapsed, selecting the next node,In the/>The next step of the ant m can select the node to go to, and T is a corner heuristic function so as to avoid more times of turning; the calculation formula of the corner heuristic function is as follows: /(I)Where θ is the rotation angle between three adjacent nodes in the path, and smaller θ represents smoother path,In the/>The spatial coordinates of three adjacent nodes in the path are respectively.
In a specific embodiment of the above path planning method, the calculation formula of the transition probability coefficient threshold is as follows: wherein K is the maximum iteration number of the algorithm.
In the specific implementation mode of the path planning method, a normal distribution function is introduced into the ant colony algorithm, heuristic information is dynamically adjusted, and the calculation formula of the adjusted heuristic information is as follows: Wherein c is a constant, d ij is the distance between two nodes,/> Is a normal distribution function through deformation.
In a specific embodiment of the above path planning method, the step of selecting optimal path information from the start point position to the target point position of all ants further includes:
introducing a pheromone volatilization mechanism to continuously update the pheromone in each iteration process, simultaneously introducing a sigmoid function to improve the pheromone volatilization mechanism to control the variation range and the variation rate of the pheromone volatilization factors in the whole iteration process,
The pheromone updating strategy formula comprises: In/> Is a dynamic pheromone volatilization factor; /(I)The information element from the node i to the node j in the kth iteration; m is the total number of ants; /(I)The increment of pheromone from node i to node j for the mth ant in the kth iteration; q is the intensity of the pheromone, and K is the maximum iteration number of the algorithm.
Wherein, the calculation formula of the improved pheromone volatilization factor is as follows: Wherein A and B are parameters of a sigmoid function respectively.
In a specific embodiment of the foregoing path planning method, the step of selecting optimal path information from the start point position to the target point position of all ants further includes:
introducing a reward and punishment mechanism to update the pheromones on the path, namely taking rewarding measures on the pheromones on the optimal path, and adding more pheromones; and taking punishment measures on the pheromone on the worst path, and properly reducing the pheromone on the path, so that the algorithm is quickly converged, and the pheromone updating strategy formula comprises: in the/> A pheromone increment representing the m-th ant from node i to node j in the kth iteration; l m represents the total length of the path that the mth ant passes through in this cycle; l y and L c are the lengths of the optimal path and the worst path in the current iteration, L mean is the average path length in the current iteration, μ,/>The weight coefficients of the optimal and worst paths respectively.
In another aspect, the present invention also provides a deep sea mining vehicle path planning system based on an improved ant colony algorithm, which implements the deep sea mining vehicle path planning method based on the improved ant colony algorithm according to the above embodiment, the system includes:
A three-dimensional model acquisition module configured to be able to acquire a three-dimensional environmental model of a deep sea submarine mining site topography, wherein the three-dimensional environmental model comprises a plurality of three-dimensional topography coordinates,
A slope value calculation module configured to calculate a slope value for each three-dimensional terrain coordinate based on the three-dimensional terrain model,
A two-dimensional model building module configured to be able to build a deep sea mining area two-dimensional grid model based on the gradient values of each three-dimensional terrain coordinate,
And the optimal path obtaining module is configured to be capable of introducing a corner heuristic function into the ant colony algorithm according to the two-dimensional grid model to obtain the optimal path of the deep sea mining vehicle.
By combining all the technical schemes, the method for planning the deep sea mining vehicle path based on the improved ant colony algorithm is characterized in that the three-dimensional environment model of the deep sea submarine mining area topography is obtained, the gradient value of each three-dimensional topography coordinate is calculated, a two-dimensional grid model of the deep sea mining area is built to provide data support for the ant colony algorithm, and a corner heuristic function is introduced into the ant colony algorithm, so that more turning times can be avoided, the mining vehicle energy consumption is reduced, and the quality of an optimal solution is improved.
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Preferred embodiments of the present invention are described below with reference to the accompanying drawings, in which:
figure 1 is a flow chart of a first embodiment of the method for planning a path of a deep sea mining vehicle based on an improved ant colony algorithm according to the present invention;
FIG. 2 is a schematic view of a three-dimensional environmental model provided by the present invention;
FIG. 3 is a schematic diagram of a two-dimensional grid model provided by the present invention;
Figure 4 is a flow chart of a second embodiment of the method for planning a path of a deep sea mining vehicle based on an improved ant colony algorithm according to the present invention;
FIG. 5 is a block diagram of the deep sea mining vehicle path planning system based on the improved ant colony algorithm of the present invention;
fig. 6 is a simulation experiment diagram of a deep sea mining vehicle path planning method based on an improved ant colony algorithm and a conventional ant colony algorithm for path planning according to an embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention. Those skilled in the art can adapt it as desired to suit a particular application. For example, although the present embodiment is described in connection with a mining vehicle path planning method applied in deep sea situations, the mining vehicle path planning method of the present invention may obviously also be used in other environments. Such changes in the application scenario do not deviate from the basic principle of the present invention and fall within the scope of the present invention.
The deep sea is rich in mineral resources such as polymetallic nodules, cobalt-rich crusts, polymetallic sulfides and the like, wherein the polymetallic nodules are rich in rare noble metal elements such as manganese, copper, cobalt, nickel and the like, and the deep sea has great commercial exploitation value. Unlike cobalt-rich crust and polymetallic sulfide ore deposit, deep sea polymetallic nodule is deposited on the surface of submarine sediment with water depth of 4000-6000 m, and is usually in semi-buried state, and the surface of the ore area is soft sediment with flat overall topography accompanied by small protrusion obstacle and gully.
The path planning is an important basic technology for guaranteeing that the deep sea mining vehicle can safely finish mining operation, and has important practical significance for the deep sea mining vehicle to efficiently finish mining operation, reduce energy consumption and safely run. The reasonable path planning scheme can ensure that the mining vehicle can safely run in a given submarine mining area according to a preset path, effectively reduce the distance and time cost of the mining vehicle in the process of mining site transfer and ore conveying, and reduce the running risk.
In the prior art, an optimal path of deep sea mining is generally found through an ant colony algorithm, and the basic thought of the ant colony algorithm is as follows: the walking path of the ants is used for representing the feasible solution of the problem to be optimized, all paths of the whole ant group form a solution space of the problem to be optimized, the amount of pheromones released by ants with shorter paths is more, the concentration of the pheromones accumulated on the shorter paths is gradually increased along with the advancement of time, and the number of the ants selecting the paths is increased. Finally, the whole ant is concentrated on the optimal path under the action of positive feedback, and the optimal solution of the problem to be optimized is correspondingly obtained. However, in the actual working environment of the mining vehicle, if the number of turns of the optimal path found by the ant colony algorithm is too large, the path track is not smooth, the energy consumption in the traveling process of the mining vehicle is increased, and the traveling efficiency is reduced.
The invention aims to solve the problems of excessive path turning times, unsmooth path track, larger energy consumption in the walking process, lower walking efficiency and the like of the conventional ant colony algorithm in the path planning of deep sea mining, and solve the technical problem of planning the deep sea mining vehicle path in the conventional method. Therefore, the technical scheme can be said to overcome the technical prejudice, provide a more accurate and comprehensive planning method and provide more reliable support for engineering practice in the related field.
A first embodiment of the method for planning a path of a deep sea mining vehicle according to the present invention based on an improved ant colony algorithm will now be described with reference to figure 1. Fig. 1 is a flowchart of a first embodiment of a method for planning a path of a deep sea mining vehicle based on an improved ant colony algorithm according to the present invention, in which the method for planning a path of a deep sea mining vehicle based on an improved ant colony algorithm comprises:
S1, acquiring a three-dimensional environment model of the topography of a deep sea submarine mining area, wherein the three-dimensional environment model comprises a plurality of three-dimensional topography coordinates,
S2, calculating the gradient value of each three-dimensional terrain coordinate based on the three-dimensional terrain model,
S3, establishing a two-dimensional grid model of the deep sea mining area based on the gradient value of each three-dimensional topographic coordinate,
And S4, introducing a corner heuristic function into the ant colony algorithm according to the two-dimensional grid model to obtain an optimal path of the deep sea mining vehicle.
In the preferred embodiment, the method for planning the deep sea mining vehicle path based on the improved ant colony algorithm is characterized in that the three-dimensional environment model of the deep sea submarine mining area topography is obtained, the gradient value of each three-dimensional topography coordinate is calculated, a two-dimensional grid model of the deep sea mining area is built to provide data support for the ant colony algorithm, and a corner heuristic function is introduced into the ant colony algorithm, so that more turning times can be avoided, the mining vehicle energy consumption is reduced, and the quality of an optimal solution is improved.
In this embodiment, the step S1 further includes: acquiring and integrating a plurality of submarine environment coordinates to form a three-dimensional environment model of the topography of the deep-sea submarine mining area; performing interpolation modeling analysis on the three-dimensional environment model; and generating a plurality of three-dimensional topographic coordinates after interpolation.
In some embodiments, due to the complex topography of the mine site on the deep sea floor, not only bare bedrock is covered, but also underwater boulder clay mixtures, topography variations, the mining vehicle is difficult to pass due to overlarge protrusion or depression of the seabed ground, so that the establishment of the three-dimensional environment model of the underwater mining area is a precondition for the path planning of the mining vehicle.
Specifically, the three-dimensional environment model of the deep sea submarine mining area is a process of discretizing real continuous underwater topography values, however, when the deep sea mining vehicle performs mining collection work, it is very difficult to detect surrounding environment information on line and process the environment information in real time, so that the environment information of the submarine mining area is wanted, in general, workers can use unmanned underwater robots to conduct topography and geological survey on a target mining area in advance, so that a plurality of corresponding submarine environment coordinates are obtained, and finally, environment modeling is performed on the target mining area according to the submarine environment coordinates. Further, the invention interpolates the data of the three-dimensional environment model in the three-dimensional space to generate a plurality of interpolated three-dimensional topographic coordinates (X, Y, Z). Alternatively, as shown in fig. 2, the mining area range is 20×20 (km), and 100 grids are interpolated in each direction to obtain a three-dimensional terrain model of the deep sea mining area.
In this embodiment, based on the three-dimensional terrain model, the gradient value of each three-dimensional terrain coordinate may be further calculated, and based on the gradient value of each three-dimensional terrain coordinate, a two-dimensional grid model of the deep sea mining area is established, and finally the passable area of the two-dimensional grid model is divided according to the gradient.
Specifically, the gradient value calculation formula of the three-dimensional terrain coordinates is: wherein: v is the slope inclination of the terrain; arctan is an arctangent function; /(I) Is the height difference between adjacent grids; /(I)Is the horizontal distance between adjacent grids.
Optionally, the two-dimensional grid model after dividing the traffic area is shown in fig. 3, where the black grid represents the non-traffic area, the light grid represents the gentle slope area with a gradient of 5-10 °, the white grid represents the flat area with a gradient of less than 5 °, and the flat area does not have an additional influence on the running of the mining vehicle, but the green grid in the gentle slope area consumes a longer time due to a larger gradient, so that the time can be converted into a path for measurement, that is, the green grid is equivalent to the white grid with a larger side length, and the path optimization can be performed after the conversion is completed. Alternatively, as shown in fig. 3, the method can simplify 100 grids interpolated in each direction into 50 grids when a two-dimensional grid model is built, so that the calculation efficiency can be improved on the basis of accurately reflecting the characteristics of the terrain.
A second embodiment of the method for planning a path of a deep sea mining vehicle according to the present invention based on an improved ant colony algorithm will now be described with reference to figure 4. Fig. 4 is a flowchart of a second embodiment of the method for planning a path of a deep sea mining vehicle based on an improved ant colony algorithm according to the present invention, in which the method for planning a path of a deep sea mining vehicle based on an improved ant colony algorithm comprises:
S201, acquiring a three-dimensional environment model of the topography of the deep sea submarine mining area, wherein the three-dimensional environment model comprises a plurality of three-dimensional topography coordinates,
S202, calculating the gradient value of each three-dimensional terrain coordinate based on the three-dimensional terrain model,
S203, establishing a two-dimensional grid model of the deep sea mining area based on the gradient value of each three-dimensional topographic coordinate,
S204, acquiring the number of ants, determining the initial point position and the target point position of the ants in the two-dimensional grid model,
S205, based on the ant colony algorithm introducing the corner heuristic function, obtaining the path information from the starting point position to the target point position of the current ant,
S206, based on the number of ants, obtaining the path information from the initial point position to the target point position of all ants,
S207, selecting optimal path information from the starting point position to the target point position of all ants.
In this embodiment, since ants in the basic ant colony algorithm need to continuously judge the trafficability of eight nodes around the current node and the transition probability of each passable node when searching a path leading to the end point from the start point, find one point as the next point of transition in the path, and the transition rule adopts a pseudo-random proportion rule, and ant k at node (i, j) selects the next passable point according to the following formula.
In addition, in the actual working environment of the mining vehicle, if the number of turns of the found optimal path is too large, the path track is not smooth, the energy consumption in the traveling process of the mining vehicle is increased, and the traveling efficiency is reduced. Therefore, in order to avoid more turning times, reduce energy consumption of the mining vehicle, improve the quality of the optimal solution, a turning angle heuristic function is introduced into the state transition probability of the ant colony algorithm in the text so as to acquire optimal path information.
Thus, the step S205 further includes: referring to a pseudo-random proportion rule, in the path of the current ant from the starting point position to the target point position, the trafficability of eight nodes around the current node and the transition probability of each trafficable node are calculated,
Ant k at node (i, j) selects the next passable point according to the following formula,In the/>Is a pseudo-random proportion rule, k is iteration times, m is ants in the current iteration process,/>, andThe method is characterized in that the method is the state transition probability, i, j is a current path node of ants, alpha is a pheromone importance degree parameter, beta is a heuristic factor importance degree parameter, gamma is a heuristic function importance degree parameter representing a turning angle, q is a transition probability coefficient, q 0 is a transition probability coefficient threshold value, and T (k) is a turning angle heuristic function so as to avoid more turning times;
Wherein the transition probability formula represents the transition probability of ant k after a period of time t has elapsed, selecting the next node, In the/>The node to which the ant m goes can be selected for the next step;
The calculation formula of the corner heuristic function is as follows: where θ is the rotation angle between three adjacent nodes in the path, and smaller θ represents smoother path,/> In the method, in the process of the invention,The spatial coordinates of three adjacent nodes in the path are respectively.
In some embodiments, when the ant transitions to the next node, the node with the highest probability of state transition is selected by q 0, and meanwhile, the other nodes are selected by the probability that the ant has (1-q 0), so that the ant selects the optimal path with high probability and the ant searches other paths. In summary, the value of the parameter q 0 affects the searching degree of ants on other paths outside the optimal path, and when the value of q 0 is larger, the ant colony algorithm tends to search the area near the current optimal path with the largest transition probability.
Therefore, in the preferred embodiment, the method for planning the deep sea mining vehicle path based on the improved ant colony algorithm provided by the invention is provided with the q 0 threshold value which dynamically changes, and in the initial stage of algorithm iteration, the larger q 0 can enable the algorithm to effectively search the optimal path according to the global path information, so that the convergence speed and convergence stability of the algorithm are improved, the q 0 value is gradually reduced along with the continuous increase of the iteration times, the searching capacity of the algorithm for other paths is improved, the situation of sinking into a local optimal solution is avoided, and the algorithm has good global searching capacity.
Therefore, the calculation formula of the transition probability coefficient threshold q 0 is as follows: wherein K is the maximum iteration number of the algorithm.
In some embodiments, the heuristic information of the ant colony algorithm is calculated by the inverse of the euclidean distance between the two nodes, but this approach tends to result in inefficient convergence of the ant colony algorithm in searching for the optimal path. Therefore, the method for planning the deep sea mining vehicle path based on the improved ant colony algorithm changes the distance between two nodes into the square of the distance in the traditional heuristic information calculation formula, so that ants have higher probability to select a relatively near grid when judging the node to be walked next, the efficiency of the ant colony algorithm is improved, and the time required for algorithm convergence is shortened.
In the preferred embodiment, a normal distribution function is introduced into the ant colony algorithm, heuristic information is dynamically adjusted, the influence of the heuristic information is strengthened when the iteration times are small, the searching capability of the algorithm to the shortest path in the early stage is enhanced, the searching efficiency of the algorithm is improved, the influence of the heuristic information is reduced in the later stage of the algorithm, the effect of strengthening the global searching performance is achieved, and the adjusted heuristic information has the following calculation formula: Wherein c is a constant, d ij is the distance between two nodes,/> Is a normal distribution function through deformation.
In some embodiments, the ant colony is guided by the pheromone in the process of searching the path, and the updating mode of the pheromone in the path influences the path selection probability of the ant colony, and the fixed pheromone volatilization factor used by the traditional ant colony algorithm cannot optimize the algorithm performance.
Therefore, the step S207 further includes: and a pheromone volatilization mechanism is introduced to continuously update the pheromone in each iteration process, and a sigmoid function is introduced to improve the pheromone volatilization mechanism so as to control the variation range and the variation rate of the pheromone volatilization factors in the whole iteration process.
In the preferred embodiment, the method for planning the deep sea mining vehicle path based on the improved ant colony algorithm controls the variation range and the speed of the pheromone volatilization factor in the whole iteration process by introducing the sigmoid function to improve the pheromone volatilization mechanism, so that the sigmoid function is reconstructed by using the current iteration times in the process of searching the optimal path by the ant colony algorithm, the pheromone volatilization speed of the algorithm in the initial stage of iteration is slower, the ant colony algorithm can be deviated to search the optimal path in the early stage, and the convergence of the solution in the early stage is stable and is not too discrete.
Along with the increase of the iteration times, the pheromone volatilization factors on the paths are gradually increased, so that the search space of the algorithm is increased, and the algorithm is ensured not to fall into a stagnation state to cause the solution to be too single. The improvement of the pheromone volatilization mechanism is used for properly adjusting and intervening the pheromone volatilization intensity of the early stage, the middle stage and the later stage of the ant colony algorithm, so that the phenomenon that the pheromone is accumulated continuously along with the iteration times in the process of searching different walking paths by the algorithm, the concentration difference of the pheromone of different paths is overlarge and falls into local optimum is avoided, and the random searching capacity and the global optimizing capacity of the algorithm are improved.
Further, the pheromone update policy formula includes: In/> Is a dynamic pheromone volatilization factor; /(I)The information element from the node i to the node j in the kth iteration; m is the total number of ants; /(I)The increment of pheromone from node i to node j for the mth ant in the kth iteration; q is the intensity of the pheromone, and K is the maximum iteration number of the algorithm.
Wherein, the calculation formula of the improved pheromone volatilization factor is as follows: Wherein A and B are parameters of a sigmoid function respectively.
In some embodiments, the pheromone updating strategy of the traditional ant colony algorithm is to judge the path that each ant walks after each iteration is completed, find out the ants that successfully reach the destination, and then add the pheromone to the path that they walk, but some ants have too long paths that successfully reach the destination, which is not in accordance with the optimizing requirement, and this ant is generally called inferior ant. Adding pheromones to paths found by inferior ants can affect the search of the ants for the optimal paths in subsequent iterations.
Therefore, the step S207 further includes: a punishment mechanism is introduced to update pheromones on the path.
In the preferred embodiment, the deep sea mining vehicle path planning method based on the improved ant colony algorithm updates the pheromones on the path by adopting a reward and punishment mechanism, namely, takes rewarding measures on the pheromones on the optimal path, and increases more pheromones; punishment measures are taken for the pheromone on the worst path, and the pheromone on the path is properly reduced, so that the algorithm is quickly converged.
Further, the pheromone update policy formula includes: In the method, in the process of the invention, A pheromone increment representing the m-th ant from node i to node j in the kth iteration; l m represents the total length of the path that the mth ant passes through in this cycle; l y and L c are the lengths of the optimal path and the worst path in the current iteration, L mean is the average path length in the current iteration, μ,/>The weight coefficients of the optimal and worst paths respectively.
It should be noted that the above-mentioned embodiments are merely for illustrating the principles of the present invention, and are not intended to limit the scope of the invention, and those skilled in the art can modify the above-mentioned structure to apply the present invention to more specific application scenarios without departing from the principles of the present invention.
Although the steps are described in the above-described sequential order in the above-described embodiments, it will be appreciated by those skilled in the art that in order to achieve the effects of the present embodiments, the steps need not be performed in such order, and may be performed simultaneously (in parallel) or in reverse order, and such simple variations are within the scope of the present invention.
In summary, the deep sea mining vehicle path planning method based on the improved ant colony algorithm has the advantages of improving efficiency and convergence speed, enhancing global searching capability and random searching capability, accelerating convergence, optimizing path quality and energy consumption and the like. The method has the advantages and feasibility in the field of deep sea mining vehicle path planning, and provides theoretical and technical support for vehicle path planning in deep sea mining areas. The beneficial technical effects include:
(1) The efficiency and the convergence rate of the algorithm are improved: by introducing a heuristic information mechanism, the guiding direction of path searching is set for the ant colony algorithm, so that the time required by algorithm convergence is shortened, and the searching capability of the algorithm in the earlier stage is enhanced. Meanwhile, by introducing the deformed normal distribution function, the influence of heuristic information on the later stage of the algorithm is reduced, so that the global searching performance is enhanced.
(2) Global search capability and random search capability are improved: by introducing dynamic pheromone volatilization factors, the searching behavior of the ant colony is controlled. In the initial stage of algorithm iteration, the convergence stability of the algorithm is improved; with the increase of iteration times, the positive feedback effect of the pheromone is reduced, the algorithm is prevented from falling into a stagnation state, the diversity of understanding is ensured, and the random searching capability of the algorithm is improved.
(3) Accelerating the convergence of the algorithm: through introducing a reward and punishment mechanism into a pheromone updating strategy of the ant colony algorithm, rewarding measures are adopted for the optimal path, and more pheromones are added; penalty measures are taken for the worst path, and pheromones on the path are properly reduced. Therefore, the algorithm can be converged rapidly, and finding out the optimal solution is accelerated.
(4) Path quality and energy consumption are optimized: the corner heuristic function is introduced into the state transition probability of the ant colony algorithm, so that excessive turning times of the mining vehicle are avoided, the quality of an optimal solution is improved, an optimal path is smoother, and energy consumption in the traveling process of the mining vehicle is reduced. Meanwhile, a parameter self-adaptive pseudo-random transition rule is introduced to dynamically adjust a transition probability threshold value, so that the searching efficiency and the global optimizing capability of the algorithm are further improved.
(5) The method has the advantages and feasibility in the field of deep sea mining vehicle path planning: compared with the traditional ant colony algorithm, the method can generate the optimal solution with high quality, and has remarkable advantages in the aspects of convergence speed and turning times. Compared with the prior art, the optimal path generated by the method is the smoother and has the least turning times. Furthermore, the average path length obtained by the present invention shows good stability.
On the other hand, the invention also provides a deep sea mining vehicle path planning system based on the improved ant colony algorithm, as shown in fig. 5, the system implements a deep sea mining vehicle path planning method based on the improved ant colony algorithm according to the above embodiment, and the system comprises:
A three-dimensional model acquisition module 501 configured to be capable of acquiring a three-dimensional environmental model of a deep sea submarine mine site topography, wherein the three-dimensional environmental model comprises a plurality of three-dimensional topography coordinates,
A slope value calculation module 502 configured to calculate a slope value for each three-dimensional terrain coordinate based on the three-dimensional terrain model,
A two-dimensional model building module 503 configured to be able to build a two-dimensional grid model of the deep sea mining area based on the gradient values of each three-dimensional terrain coordinate,
The optimal path obtaining module 504 is configured to introduce a corner heuristic function into the ant colony algorithm according to the two-dimensional grid model, so as to obtain an optimal path of the deep sea mining vehicle.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
The content of the information interaction and the execution process between the devices/units and the like is based on the same conception as the method embodiment of the present invention, and specific functions and technical effects brought by the content can be referred to in the method embodiment section, and will not be described herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present invention. For specific working processes of the units and modules in the system, reference may be made to corresponding processes in the foregoing method embodiments.
The embodiment of the invention also provides a computer device, which comprises: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, which when executed by the processor performs the steps of any of the various method embodiments described above.
Embodiments of the present invention also provide a computer readable storage medium storing a computer program which, when executed by a processor, performs the steps of the respective method embodiments described above.
The embodiment of the invention also provides an information data processing terminal, which is used for providing a user input interface to implement the steps in the method embodiments when being implemented on an electronic device, and the information data processing terminal is not limited to a mobile phone, a computer and a switch.
The embodiment of the invention also provides a server, which is used for realizing the steps in the method embodiments when being executed on the electronic device and providing a user input interface.
Embodiments of the present invention provide a computer program product which, when run on an electronic device, causes the electronic device to perform the steps of the method embodiments described above.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may implement all or part of the flow of the method of the above embodiments, and may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include at least: any entity or device capable of carrying computer program code to a photographing device/terminal apparatus, recording medium, computer Memory, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), electrical carrier signals, telecommunications signals, and software distribution media. Such as a U-disk, removable hard disk, magnetic or optical disk, etc.
To further illustrate the effects associated with the embodiments of the present invention, the following experiments were performed.
The method and the traditional ant colony algorithm are adopted to plan the mining vehicle path in the two-dimensional grid model of the deep sea mining area in fig. 3, the result is shown in fig. 6, the total path length of the method is 31.1105 km, the turning times are 9 times, the path length of the traditional ant colony algorithm is 36.7462 km, the turning times are 30 times, and compared with the traditional ant colony algorithm, the simulation experiment result proves that the optimal path length and the turning times obtained by the method are obviously reduced, wherein the path length can be shortened by 18.12%, the turning times are reduced by 70%, and the method has obvious advantages in the aspect of static path planning of the mining vehicle by improving four mechanisms including heuristic information, volatility factors, pheromone updating strategies and state transition probability.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will fall within the scope of the present invention.
Claims (5)
1. A method for planning a path of a deep sea mining vehicle based on an improved ant colony algorithm, the method comprising:
S1, acquiring a three-dimensional environment model of the topography of a deep sea submarine mining area, wherein the three-dimensional environment model comprises a plurality of three-dimensional topography coordinates,
S2, calculating the gradient value of each three-dimensional terrain coordinate based on the three-dimensional environment model,
S3, establishing a two-dimensional grid model of the deep sea mining area based on the gradient value of each three-dimensional topographic coordinate,
S4, introducing a corner heuristic function into an ant colony algorithm according to the two-dimensional grid model to obtain an optimal path of the deep sea mining vehicle,
Wherein, step S4 further comprises:
Acquiring the number of ants, determining the initial point positions and the target point positions of the ants in the two-dimensional grid model,
Based on the ant colony algorithm introducing the corner heuristic function, the path information from the starting point position to the target point position of the current ant is obtained,
Based on the number of ants, path information of all ants from the starting point position to the target point position is acquired,
Selecting optimal path information from the initial point position to the target point position of all ants,
The step of selecting optimal path information from the initial point position to the target point position in the path information of all ants further comprises:
introducing a pheromone volatilization mechanism to continuously update the pheromone in each iteration process, simultaneously introducing a sigmoid function to improve the pheromone volatilization mechanism to control the variation range and the variation rate of the pheromone volatilization factors in the whole iteration process,
Introducing a reward and punishment mechanism to update the pheromones on the path, namely taking rewarding measures on the pheromones on the optimal path, and adding more pheromones; punishment measures are taken for the pheromone on the worst path, and the pheromone on the path is properly reduced, so that the algorithm is quickly converged,
The pheromone updating strategy formula comprises: In/> Is a dynamic pheromone volatilization factor; /(I)The information element from the node i to the node j in the kth iteration; m is the total number of ants; /(I)The information element from the node i to the node j is the mth ant in the kth iteration; q is the intensity of the pheromone, K is the maximum iteration number of the algorithm;
The calculation formula of the improved pheromone volatilization factor is as follows: wherein A and B are parameters of a sigmoid function,
The pheromone updating strategy formula comprises: in the/> A pheromone increment representing the m-th ant from node i to node j in the kth iteration; l m represents the total length of the path that the mth ant passes through in this cycle; l y and L c are the lengths of the optimal path and the worst path in the current iteration, L mean is the average path length in the current iteration, μ,/>The weight coefficients of the optimal and worst paths respectively.
2. The method of planning a path of a deep sea mining vehicle based on an improved ant colony algorithm according to claim 1, wherein the step of acquiring path information of a current ant from a start point position to a target point position based on an ant colony algorithm introducing a corner heuristic function further comprises:
Referring to a pseudo-random proportion rule, in the path of the current ant from the starting point position to the target point position, the trafficability of eight nodes around the current node and the transition probability of each trafficable node are calculated,
Ant k at node (i, j) selects the next passable point according to the following formula,In the/>Is a pseudo-random proportion rule, k is iteration times, m is ants in the current iteration process,/>, andThe method is characterized in that the method is a state transition probability, i, j is a current path node of ants, alpha is a pheromone importance degree parameter, beta is a heuristic factor importance degree parameter, gamma is a heuristic function importance degree parameter representing a corner, q is a transition probability coefficient, and q 0 is a transition probability coefficient threshold;
Wherein the transition probability formula represents the transition probability of ant k after a period of time t has elapsed, selecting the next node, In the/>The next step for the ant m can select the node to go to, T (k) is a corner heuristic function to avoid more turns;
The calculation formula of the corner heuristic function is as follows: where θ is the rotation angle between three adjacent nodes in the path, and smaller θ represents smoother path,/> In the/>The spatial coordinates of three adjacent nodes in the path are respectively.
3. A method for planning a path of a deep sea mining vehicle based on an improved ant colony algorithm according to claim 2,
The calculation formula of the transition probability coefficient threshold value is as follows: wherein K is the maximum iteration number of the algorithm.
4. The deep sea mining vehicle path planning method based on the improved ant colony algorithm according to claim 2, wherein a normal distribution function is introduced into the ant colony algorithm, heuristic information is dynamically adjusted, and the adjusted heuristic information has the following calculation formula: Wherein c is a constant, d ij is the distance between two nodes,/> Is a normal distribution function through deformation.
5. A deep sea mining vehicle path planning system based on an improved ant colony algorithm, characterized in that the system implements a deep sea mining vehicle path planning method based on an improved ant colony algorithm as claimed in any one of claims 1-4, the system comprising:
A three-dimensional model acquisition module configured to be able to acquire a three-dimensional environmental model of a deep sea submarine mining site topography, wherein the three-dimensional environmental model comprises a plurality of three-dimensional topography coordinates,
A gradient value calculation module configured to calculate a gradient value for each three-dimensional terrain coordinate based on the three-dimensional environment model,
A two-dimensional model building module configured to be able to build a deep sea mining area two-dimensional grid model based on the gradient values of each three-dimensional terrain coordinate,
And the optimal path obtaining module is configured to be capable of introducing a corner heuristic function into the ant colony algorithm according to the two-dimensional grid model to obtain the optimal path of the deep sea mining vehicle.
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