CN112817316A - Multi-robot path planning method and device - Google Patents
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Abstract
The application discloses a multi-robot path planning method and a device, which belong to the technical field of robot path planning, and the method comprises the following steps: constructing a grid environment according to a robot channel environment, and performing cross point diagram environment reconstruction by reserving channel cross points; planning an optimal path for a single robot in the cross point diagram environment by a heuristic A-algorithm; planning all channels in the cross point diagram environment into a one-way channel according to the primary optimal path; aiming at the single-row road, planning a secondary optimal path of a single robot in the cross point diagram environment through the heuristic A-x algorithm; and reconstructing a grid environment aiming at the secondary optimal path to obtain the optimal path in the grid environment. The method solves the defect that the traditional heuristic algorithm causes deadlock due to robot conflict in a channel.
Description
Technical Field
The application relates to the technical field of robot path planning, in particular to a multi-robot path planning method and device.
Background
The multi-robot path planning problem is a problem that has been studied for many years and results in high complexity for solving the problem due to collisions between robots. The traditional algorithm is a tedious and tedious process by modeling an integer programming model for an environment and optimally solving by taking conflicts among robots as constraint conditions, and when the scale of the environment rises, the complexity of solving the integer programming model is an exponential rise process and occupies a large amount of computer resources, so that the calculation speed is further reduced. In recent years, heuristic algorithms have been proposed to reduce the complexity of solving the multi-robot path planning model. The core of the heuristic algorithms is to divide the original environment into small-scale environments and establish corresponding solving models on the small-scale environments, so that the heuristic algorithms can greatly reduce the solving complexity.
Meanwhile, in order to improve the logistics efficiency and the bearing capacity of the warehouse, a channel in the warehouse can be narrowed so as to increase the volume of the goods shelf. In a high-density large-scale warehouse, the width of a channel in the warehouse may be only the width of 1 robot, in such a case, since a heuristic algorithm removes most potential feasible nodes in the process of segmenting the environment, a collision-free path cannot be generated in a small environment obtained by segmentation, so that a deadlock problem occurs in a narrow channel to cause algorithm stagnation, and the high complexity of a traditional algorithm hinders practical application.
Disclosure of Invention
The embodiment of the application aims to provide a multi-robot path planning method and a multi-robot path planning device, so as to solve the problem that deadlock occurs when a plurality of robots operate in a channel in the related art.
According to a first aspect of an embodiment of the present application, there is provided a multi-robot path planning method, including:
constructing a grid environment according to a robot channel environment, and performing cross point diagram environment reconstruction by reserving channel cross points, wherein all channel nodes except the channel cross points and barrier nodes are ignored, and a single robot starting point and a single robot end point are mapped to the channel cross points closest to the single robot end point;
planning an optimal path for a single robot in the cross point diagram environment by a heuristic A-algorithm, wherein the conflict among the robots is not considered in the planning of the optimal path for the first time;
planning all channels in the cross point diagram environment into a one-way channel according to the primary optimal path;
aiming at the single-row road, planning a secondary optimal path of a single robot in the cross point diagram environment through the heuristic A-x algorithm;
and reconstructing a grid environment aiming at the secondary optimal path to obtain the optimal path in the grid environment.
According to a second aspect of the embodiments of the present application, there is provided a multi-robot path planning apparatus, including:
the system comprises a first reconstruction module, a second reconstruction module and a third reconstruction module, wherein the first reconstruction module is used for constructing a grid environment according to a robot channel environment and reconstructing a cross point image environment by reserving channel cross points, wherein all channel nodes except the channel cross points and barrier nodes are ignored, and a starting point and an end point of a single robot are mapped to the closest channel cross points;
the first path planning module is used for planning an optimal path for a single robot in the cross point diagram environment through a heuristic A-x algorithm, and conflicts among the robots are not considered in the planning of the optimal path for the first time;
the planning module is used for planning all channels in the cross point diagram environment into a one-way channel according to the primary optimal path;
the second path planning module is used for planning a secondary optimal path of the single robot in the cross point diagram environment through the heuristic A-x algorithm aiming at the single road;
and the second reconstruction module is used for reconstructing a grid environment aiming at the secondary optimal path to obtain the optimal path under the grid environment.
The application discloses a multi-robot path planning method and device, which are particularly suitable for narrow-channel environments and achieve multi-robot conflict-free path planning. The technical scheme provided by the embodiment of the application can have the following beneficial effects:
and the cross point diagram environment reconstruction is carried out by reserving the channel cross points, and all channel nodes except the channel cross points and the barrier nodes are ignored, so that the complexity of the method is reduced. And planning an optimal path for a single robot in the cross point diagram environment by a heuristic A-x algorithm, so that the aggregation of the robots and redundant turning are avoided. All channels in the cross point diagram environment are planned to be one-way channels, so that the robot is prevented from colliding in the channels. And planning a secondary optimal path for the single robot in the cross point diagram environment and reconstructing a grid environment by the heuristic A-x algorithm, so that the aim of planning a collision-free optimal path for each single robot in the channel direction is fulfilled. Therefore, the robot can reach any terminal from any starting point at a flexible speed without collision, algorithm failure caused by change of the running speed of the robot due to system disturbance or load change is avoided, and the condition that a multi-robot collision-free path is generated efficiently in practical application is met. The problem that deadlock can occur when a plurality of robots operate in a channel is solved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is a flow chart illustrating a method of multi-robot path planning in accordance with an exemplary embodiment.
FIG. 2 is a schematic diagram illustrating a cross-point map reconstruction, according to an example embodiment.
Fig. 3 is a schematic diagram illustrating a heuristic a-algorithm single-robot optimal path planning according to an exemplary embodiment.
FIG. 4 is a schematic diagram illustrating a 0-example rotational law prescribed channel direction according to an exemplary embodiment.
Fig. 5 is a schematic diagram illustrating heuristic a-algorithm single-robot optimal path planning and path reconstruction in an original narrow channel environment according to an exemplary embodiment.
FIG. 6 is a schematic diagram illustrating a 0-instance rotation method for performing path planning according to a single-robot optimal path, according to an exemplary embodiment.
Fig. 7 is a graph showing the run time results for the number of robots going up from 100 to 1000 in a 96 x 58 narrow lane environment with 2 x 4 shelves for the algorithm of the present invention according to an exemplary embodiment.
Fig. 8 is a graph illustrating the ratio of the longest path length in an algorithmically planned path to the longest path length in a single robot optimal path without regard to collisions with the same set of start and end points, in a narrow channel environment of different scale and different aspect ratio, with all free cells occupied by the robot, according to an example embodiment.
Fig. 9 is a diagram illustrating the ratio of the total path length of an algorithmically planned path to the longest path length in a single robot optimal path without regard to collisions with the same set of start and end points, in a narrow channel environment of different scale and different aspect ratio, with all free cells occupied by the robot, according to an exemplary embodiment.
FIG. 10 is a diagram illustrating statistics of the number of turns that occur to a robot in a narrow channel environment of different scales and different aspect ratios with all free grids occupied by the robot, according to an exemplary embodiment.
FIG. 11 is a simulation result of the algorithm of the present invention shown in accordance with an exemplary embodiment.
FIG. 12 is a block diagram illustrating a multi-robot path planner according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
FIG. 1 is a flow diagram illustrating a multi-robot path planning method in accordance with an exemplary embodiment; as shown in fig. 1, an embodiment of the present invention provides a multi-robot path planning method, which may include the following steps:
step S101, constructing a grid environment according to a robot channel environment, and performing cross point diagram environment reconstruction by reserving channel cross points, wherein all channel nodes except the channel cross points and barrier nodes are ignored, and a single robot starting point and a single robot end point are mapped to the channel cross points closest to the single robot end point;
step S102, planning an optimal path for a single robot in the cross point diagram environment through a heuristic A-x algorithm, wherein conflicts among the robots are not considered in the planning of the optimal path for the first time;
step S103, planning all channels in the cross point diagram environment into one-way channels according to the primary optimal path;
step S104, aiming at the one-way road, planning a secondary optimal path for the single robot in the cross point diagram environment through the heuristic A-x algorithm;
and step S105, reconstructing a grid environment aiming at the secondary optimal path to obtain the optimal path in the grid environment.
And the cross point diagram environment reconstruction is carried out by reserving the channel cross points, and all channel nodes except the channel cross points and the barrier nodes are ignored, so that the algorithm complexity is reduced. And planning an optimal path for a single robot in the cross point diagram environment by a heuristic A-x algorithm, so that the aggregation of the robots and redundant turning are avoided. All channels in the cross point diagram environment are planned to be one-way channels, so that the robot is prevented from colliding in the channels. And planning a secondary optimal path for the single robot in the cross point diagram environment and reconstructing a grid environment by the heuristic A-x algorithm, so that the aim of planning a collision-free optimal path for each single robot in the channel direction is fulfilled. Therefore, the robot can reach any terminal from any starting point at a flexible speed without collision, algorithm failure caused by change of the running speed of the robot due to system disturbance or load change is avoided, and the condition that a multi-robot collision-free path is generated efficiently in practical application is met. The problem that deadlock can occur when a plurality of robots operate in a channel is solved.
Each of the above steps is described in detail below.
In step S101, a grid environment is constructed according to a robot channel environment, and a cross point diagram environment is reconstructed by reserving channel cross points, including the following sub-steps:
step S1011, constructing a grid environment according to a robot channel environment, setting top views of the obstacles to be rectangles with the same size and the same shape, uniformly arranging the rectangles, setting a passable part between two adjacent obstacles as a robot channel, and setting the minimum width of each channel to be 1 single robot width; specifically, a grid environment M is constructedh×w，Mh×wThe occupancy of each node in the grid environment is represented, h and w represent the length and width of the environment, M (i, j) ═ 1 represents that the grid is occupied by obstacles, M (i, j) ═ 0 represents that the grid is free, and (i, j) represents the node cartesian coordinates. Setting the size of the obstacle as hs×ws，hsAnd wsRespectively indicating barriersThe length and width of the obstacles, s, are the obstacles which are evenly arranged, the passable part between two adjacent obstacles is set as a robot passage, and the minimum width of each passage is set as 1 single robot width.
In step S1012, the intersection of the two channels is set as a cross point, the direction of the channel is indicated by the cross points at the two ends of the channel, and a cross point map environment is reconstructed. Specifically, a set of settings
And a set of values v that are,
representing the horizontal coordinate set corresponding to the channel, v representing the vertical coordinate set corresponding to the channel, and setting the set of the intersection points of the roadways as
Preserving a set of intersection points
And reconstructing the middle node into a cross point diagram environment. The following steps are based on a cross point diagram environment, so that the environment scale is reduced, and the problem that the running time of the traditional algorithm is too long is solved.
As shown in fig. 2, the blank grid in the left drawing represents a free grid, the black grid represents an obstacle grid, and the map is labeled with cartesian rectangular coordinates, each grid corresponding to a pair of abscissa and ordinate. The free grid between the obstacles has a channel width of 1. In FIG. 2
v: 1, 4, 7 denotes the abscissa and ordinate of the channel,
all channel intersections are indicated. The right panel shows the results after reconstruction of the cross-point map, with only the cross-points
The remaining nodes are omitted.
In step S101, mapping a single robot start and end point to its nearest channel intersection, comprises:
step S1013, creating a channel intersection point copy X ' of the actual starting point and end point X of a single robot according to the cross point diagram environment, mapping X ' to the closest intersection point to X, and using the copy X ' corresponding to X as the input starting point and end point of the heuristic a-X algorithm. Specifically, a set of intersections is set
For the
Arbitrary
There is | v-X | > | X' -X |, v representing the intersection coordinates. At this time, X' is used as a copy of X, and is mapped to X and used as an input starting point and an input end point of the heuristic A-X algorithm.
In step S102, a heuristic a-x algorithm is used to plan an optimal path for a single robot in the cross point diagram environment, including:
obtaining the optimal path of a single robot through an A-x algorithm according to the cross point diagram environment, introducing two new heuristic values T and N, planning the optimal path for one time, and adding a heuristic value combination CT*T+CN*N，CTAnd CNAnd constants respectively representing the control turning cost weight and the control node occupation cost weight, wherein T represents the cost of turning operation of a single robot, and N represents the number of the single robots passing through each channel. Specifically, assume that a successive node of the planning robot r is v, P ' represents a planned path, end represents a last node of the planned path P ', and the turning cost t (v) is 0, if v-P ' (end) is P ' (end) -P ' (end-1), otherwise, a certain turning cost 0 < t (v) < 1 is given. After planning the path P of the robot r, allv belongs to P, a node occupation value N represents the number of robots passing through a certain node, N is an all-zero matrix in an initial state, and N (v) ═ N (v) + 1;
according to the improved a-algorithm, the final heuristic value is: h (v) ═ Dist (v, X)G)+Ct*T(v)+CN*N(v)。Dist(v，Xr G) Indicating successive nodes v and Xr GManhattan distance, Xr GIndicating the end point of robot r, r the robot number and G the end point. The optimal path of the single robot is obtained by improving the heuristic value in the A-star algorithm, and the number of the robots passing through a certain node is introduced into the heuristic value, so that the number of the robots passing through the same node is reduced, the possibility of collision is reduced, the turning cost of the robots is added into the heuristic value, the turning times of the robots are reduced, and the loss caused by turning is reduced.
As shown in fig. 3, the optimal path of the single robot is planned without considering the collision between the robots.
In step S103, according to the primary optimal path, planning all channels in the cross point diagram environment into a one-way channel, including the following substeps:
step S1031, by stipulating the channel direction in the cross point diagram environment, the single robots in the channel all run according to the same direction to avoid the conflict in the channel, namely all the channels become one-way channels; specifically, all robots are enabled to pass through the channel in a specified direction by fixing the direction of the channel, so that the robots are prevented from colliding in the channel, and the condition that the algorithm cannot solve the collision of the robots in the channel is avoided;
and S1032, sequencing the single robots from long to short according to the primary optimal path length of the single robot, and sequentially prescribing the channel direction according to the optimal path of the single robot according to a 0-instance rotation method, so that the single robot can run along the corresponding primary optimal path in the prescribed one-way channel direction. Specifically, according to the optimal path length of the single robot at one time, the direction of a channel passed by the single robot is specified sequentially from long to short according to a 0-instance rotation method. According to the 0-instance rotation method, any single robot can reach an end point according to the prescribed lane direction.
Further, according to the 0-instance rotation method, the channel direction is sequentially specified according to the optimal path of the single robot, and the method comprises the following sub-steps:
step S10321, forming four adjacent intersections into a ring I ═ I1，i2，i3，i4](ii) two adjacent intersections form a channel, to form (i)1，i2)，(i2，i3)，(i3，i4)，(i4，i1) The directions of the four channels form a clockwise or counterclockwise directed loop I or I', I ═ I, around the geometric center of the four intersections4，i3，i2，i1]I represents 0-instance, I1，i2，i3，i4Subscripts of (d) indicate node numbers, setting clockwise and counterclockwise to 0-instance directions; specifically, the size is hs×wsFour channels around the obstacle of (a) may make up one such 0-instance. As shown in fig. 6, the 0-example may be formed around 4 obstacles;
step S10322, setting a primary optimal path of the single robot with the highest priority to pass through a channel belonging to a certain 0-instance, and if the direction of the channel is opposite to the direction of the primary optimal path, rotating the 0-instance direction including the channel, that is: if 0-instance I is clockwise, then the rotation is counterclockwise I', and the four end-to-end lane directions in each 0-instance are coincident with the 0-instance direction, and vice versa; specifically, as in FIG. 6, the robot r passes through { p }r(tr)，pr(tr+1)，pr(tr+2)}，prRepresents the primary optimal path, t, of the robot rrRepresents the operating time point of the robot r, since (p)r(tr+2)，pr(tr+1))∈I，(pr(tr+1)，pr(tr+ 2)). epsilon.I' is opposite to the optimal path direction, and the 0-instance rotation method is applied on I, when (p)r(tr+1)，pr(tr+2)) ∈ I, corresponding to the optimal path direction, as shown in fig. 4, the channel direction under the 0-instance rotation law. (ii) a
Step S10323, the rotated 0-instance containing the channel is locked, the four channel directions of each 0-instance are fixed to the one-way direction and cannot be changed again, and the 0-instance direction and the one-way direction passed by other single robots with lower priorities are sequentially specified according to the priority ranking and the 0-instance rotation method. Here, assuming that the 0-instance I is rotated, I is locked and cannot be rotated again, thereby fixing the orientation.
In step S104, for the one-way road, performing a secondary optimal path planning on the single robot in the cross point diagram environment through the heuristic a-x algorithm, including:
and mapping the starting point and the end point of the single robot to corresponding intersection points according to the channel direction constructed by the primary optimal path and the 0-instance, repeating the heuristic A-x algorithm, and planning the secondary optimal path based on the intersection point environment. And according to the heuristic A-x algorithm, planning a secondary optimal path based on a cross point diagram environment in the channel direction.
Further, according to the channel direction constructed by the optimal path and the 0-instance, mapping the starting point and the ending point of the single robot to the corresponding intersection point, including:
and according to the cross point diagram environment, making a channel cross point copy Y ' of the actual starting point and end point Y of the single robot, mapping Y ' to the cross point which is closest to Y along the direction of the single-row channel, and taking the copy Y ' corresponding to Y as the input starting point and the end point of the heuristic A-x algorithm. Specifically, a set of intersections is set
For the
Arbitrary crossing point
There is | v-X | > | X '-X |, and the YY' direction satisfies the channel direction requirement. And taking Y ' as a copy of Y, mapping Y ' to Y and taking the Y ' as an input starting point and an input end point of the heuristic A-x algorithm.
In step S105, reconstructing a grid environment for the secondary optimal path to obtain an optimal path in the grid environment, including:
and filling the nodes in the channels into the secondary optimal paths according to the secondary optimal paths only including the cross points and the channel direction, expanding the secondary optimal paths into the secondary optimal paths in the grid environment, and completing the conflict-free multi-robot path planning in the grid environment. Specifically, as shown in fig. 5, a collision-free path in a cross point diagram environment is solved according to the heuristic a-x algorithm, and according to the channel direction, the nodes in the channel are filled into the secondary optimal path, and are expanded into the secondary optimal path in a grid environment, so as to complete the collision-free multi-robot path planning in the grid environment.
As shown in fig. 5, a heuristic a-x algorithm searches for a collision-free path in the cross-point diagram environment, and finally obtains an optimal path in the grid environment by reconstructing the grid environment.
As shown in fig. 7, in the present algorithm, the calculation time of the number of robots is from 100 to 1000 in the environment of 96 × 58, and it can be seen that the increase of the calculation time of the algorithm is linear with the increase of the number of robots, and the calculation time is short, and in the case that 1000 robots need to be solved, the calculation takes only about 1.2 seconds.
Experiments prove that under the condition that grid environments with different length-width ratios and all idle grids in the environments are occupied by robots, the optimal solution of the optimal path of the approximate single robot can be quickly found by using the algorithm. As shown in fig. 8 and fig. 9, the ratio of the longest path length and the total path length of the optimized path found by the algorithm to the optimal path without considering conflict by a single robot is close to 1, which proves that the proposed algorithm has realizability in practical operation, and the algorithm still shows higher performance under the environment of high robot density and special size.
As shown in fig. 10, in the grid environment with different aspect ratios, the average number of turns of the robot increases linearly as the density of the robot increases, and the average number of turns represents the ratio of the sum of the number of turns of all the robots to the number of robots.
As shown in fig. 11, it is demonstrated that the increase in the number of turns in the robot path found by the proposed algorithm grows linearly with the increase in the scale of the environment. The proposed algorithm therefore has an optimized performance for the number of turns.
As shown in fig. 12, an exemplary diagram is presented after completion of the proposed algorithm, the grey arrows indicate the direction of the passage, the black bars indicate the direction in which the robot is advancing, robot No. 9 has reached the end point, and the rest of the robots are in operation.
The method is particularly suitable for large-scale high-density environments such as warehouses and the like with the channel width between obstacles being only 1 unit (only one robot passes through), and solves the defect that the traditional heuristic algorithm causes deadlock due to robot conflict in the channel, particularly narrow channels. The method has the core that the channel is subjected to unique direction regulation according to the optimal path of the single robot, and any robot can be ensured to reach respective terminal points along the regulated direction. The paths obtained by running the method can enable all robots to reach respective end points at flexible speed without conflict and optimize robot running cost, and new robots can be added into the environment to run without conflict without running an algorithm again. The method has low complexity, and can efficiently realize conflict-free multi-robot path planning by using less time and memory.
Corresponding to the embodiment of the multi-robot path planning method, the application also provides an embodiment of the multi-robot path planning device.
FIG. 12 is a block diagram illustrating a multi-robot path planner according to an exemplary embodiment. Referring to fig. 12, the apparatus includes:
the first reconstruction module 21 is used for constructing a grid environment according to the robot channel environment, and performing cross point diagram environment reconstruction by reserving channel cross points, wherein all channel nodes except the channel cross points and barrier nodes are ignored, and a starting point and an end point of a single robot are mapped to the closest channel cross points;
the first path planning module 22 is configured to plan an optimal path for a single robot in the cross point diagram environment through a heuristic a-x algorithm, where collisions between the robots are not considered in the planning of the optimal path for the first time;
the planning module 23 is configured to plan all channels in the cross point diagram environment into a one-way channel according to the primary optimal path;
the second path planning module 24 is configured to plan a secondary optimal path for the single robot in the cross point diagram environment through the heuristic a-x algorithm for the single road;
and a second reconstructing module 25, configured to reconstruct the raster environment for the secondary optimal path, so as to obtain an optimal path in the raster environment.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement it without inventive effort.
Correspondingly, the present application further provides an information display device, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured for performing steps S101-S105.
Accordingly, the present application also provides a terminal comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs configured to be executed by the one or more processors include operations for performing steps S101-S105.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (10)
1. A multi-robot path planning method is characterized by comprising the following steps:
constructing a grid environment according to a robot channel environment, and performing cross point diagram environment reconstruction by reserving channel cross points, wherein all channel nodes except the channel cross points and barrier nodes are ignored, and a single robot starting point and a single robot end point are mapped to the channel cross points closest to the single robot end point;
planning an optimal path for a single robot in the cross point diagram environment by a heuristic A-algorithm, wherein the conflict among the robots is not considered in the planning of the optimal path for the first time;
planning all channels in the cross point diagram environment into a one-way channel according to the primary optimal path;
aiming at the single-row road, planning a secondary optimal path of a single robot in the cross point diagram environment through the heuristic A-x algorithm;
and reconstructing a grid environment aiming at the secondary optimal path to obtain the optimal path in the grid environment.
2. The method for multi-robot path planning as claimed in claim 1, wherein the constructing of the grid environment from the robot path environment, the reconstructing of the cross point environment by reserving the path intersections, comprises:
constructing a grid environment according to a robot channel environment, setting top views of the obstacles to be rectangles with the same size and the same shape, uniformly arranging the rectangles, setting a passable part between two adjacent obstacles to be a robot channel, and setting the minimum width of each channel to be 1 single robot width;
the intersection point of the two channels is set as an intersection point, the direction of the channel is represented by the intersection point of the two ends of the channel, and the channel is reconstructed into an intersection point environment.
3. A method for multi-robot path planning as defined in claim 1, wherein mapping a single robot start and end point to a nearest path intersection point comprises:
and according to the cross point diagram environment, making a channel cross point copy X ' of the actual starting point and end point X of the single robot, mapping X ' to the nearest cross point away from X, and taking the copy X ' corresponding to X as the input starting point and end point of the heuristic A-X algorithm.
4. A method for multi-robot path planning as claimed in claim 1, wherein the step of planning an optimal path for a single robot in the cross-point diagram environment by a heuristic a-x algorithm comprises:
obtaining the optimal path of a single robot through an A-x algorithm according to the cross point diagram environment, introducing two new heuristic values T and N, planning the optimal path for one time, and adding a heuristic value combination CT*T+CN*N，CTAnd CNAnd constants respectively representing the control turning cost weight and the control node occupation cost weight, wherein T represents the cost of turning operation of a single robot, and N represents the number of the single robots passing through each channel.
5. The method as claimed in claim 1, wherein the step of planning all channels in the cross point diagram environment as one-way channels according to the one-time optimal path comprises:
through the stipulation of the channel direction under the cross point diagram environment, the single robots in the channel all run according to the same direction, so as to avoid the conflict in the channel, namely all the channels become one-way channels;
and according to the length of the primary optimal path of the single robot, sequencing the single robot from long to short according to the length of the primary optimal path, and sequentially prescribing the channel direction according to the optimal path of the single robot according to a 0-instance rotation method, so that the single robot can run along the corresponding primary optimal path in the prescribed one-way channel direction.
6. The method for multi-robot path planning as claimed in claim 5, wherein defining the path directions according to the 0-instance rotation method sequentially according to the optimal path of the single robot comprises:
forming a ring I ═ I by four adjacent cross points1,i2,i3,i4]Two adjacent intersections are formed into channels, and four channels (i) connected end to end are formed1,i2),(i2,i3),(i3,i4),(i4,i1) The directions of the four channels form a clockwise or counterclockwise directed loop I or I', I ═ I, around the geometric center of the four intersections4,i3,i2,i1]Setting I to 0-instance, clockwise and counterclockwise to 0-instance directions;
setting a primary optimal path of a single robot with the highest priority to pass through a certain channel belonging to a certain 0-instance, and the direction of the channel is opposite to the direction of the primary optimal path, rotating the 0-instance direction containing the channel, namely: if 0-instance I is clockwise, then the rotation is counterclockwise I', and the four end-to-end lane directions in each 0-instance are coincident with the 0-instance direction, and vice versa;
the rotated 0-instance containing the channel is locked, the four channel directions of each 0-instance are fixed to be the single-line direction and cannot be changed again, and the 0-instance direction and the single-line direction of the single robot with low priority are sequentially specified according to the priority ranking and the 0-instance rotation method.
7. A method for multi-robot path planning as claimed in claim 1, wherein the step of performing quadratic optimal path planning on a single robot in the cross-point diagram environment by the heuristic a-x algorithm for the single-lane comprises:
and mapping the starting point and the end point of the single robot to corresponding intersection points according to the channel direction constructed by the primary optimal path and the 0-instance, repeating the heuristic A-x algorithm, and planning the secondary optimal path based on the intersection point environment.
8. The method as claimed in claim 7, wherein mapping the start point and the end point of a single robot to the corresponding intersection point according to the path direction constructed by the optimal path and 0-instance comprises:
and according to the cross point diagram environment, making a channel cross point copy Y ' of the actual starting point and end point Y of the single robot, mapping Y ' to the cross point which is closest to Y along the direction of the single-row channel, and taking the copy Y ' corresponding to Y as the input starting point and the end point of the heuristic A-x algorithm.
9. The method for multi-robot path planning as claimed in claim 1, wherein reconstructing a grid environment for the quadratic optimal path to obtain an optimal path in the grid environment comprises:
and filling the nodes in the channels into the secondary optimal paths according to the secondary optimal paths only including the cross points and the channel direction, expanding the secondary optimal paths into the secondary optimal paths in the grid environment, and completing the conflict-free multi-robot path planning in the grid environment.
10. A multi-robot path planning apparatus, comprising:
the system comprises a first reconstruction module, a second reconstruction module and a third reconstruction module, wherein the first reconstruction module is used for constructing a grid environment according to a robot channel environment and reconstructing a cross point image environment by reserving channel cross points, wherein all channel nodes except the channel cross points and barrier nodes are ignored, and a starting point and an end point of a single robot are mapped to the closest channel cross points;
the first path planning module is used for planning an optimal path for a single robot in the cross point diagram environment through a heuristic A-x algorithm, and conflicts among the robots are not considered in the planning of the optimal path for the first time;
the planning module is used for planning all channels in the cross point diagram environment into a one-way channel according to the primary optimal path;
the second path planning module is used for planning a secondary optimal path of the single robot in the cross point diagram environment through the heuristic A-x algorithm aiming at the single road;
and the second reconstruction module is used for reconstructing a grid environment aiming at the secondary optimal path to obtain the optimal path under the grid environment.
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