CN111813124A - Mobile robot hybrid scheduling method based on topological map - Google Patents

Mobile robot hybrid scheduling method based on topological map Download PDF

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CN111813124A
CN111813124A CN202010711872.4A CN202010711872A CN111813124A CN 111813124 A CN111813124 A CN 111813124A CN 202010711872 A CN202010711872 A CN 202010711872A CN 111813124 A CN111813124 A CN 111813124A
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topological map
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CN111813124B (en
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沈洋
胡志光
李卫君
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Zhejiang Mairui Robot Co Ltd
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    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
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Abstract

The invention provides a mobile robot hybrid scheduling method and a mobile robot hybrid scheduling system based on a topological map, wherein the hybrid scheduling method is used for pre-establishing the topological map and acquiring the size information and the position information of a target robot and an obstacle, and comprises the following steps: acquiring a moving and rotating swept area of the robot according to the size and the position of the robot; acquiring a robot planning path, and establishing a geometric intersection judgment function according to a swept area of the robot and the size of an obstacle; judging the geometric intersection condition of the robot according to the position and the moving mode of the robot; the robot hybrid scheduling method provided by the invention combines the geometric intersection condition and the map information to keep or re-plan the path, and executes the robot scheduling operation through establishing an accurate geometric intersection judgment function and deep optimization of an algorithm, thereby reducing the operation amount and improving the scheduling efficiency on the basis of realizing high space utilization rate.

Description

Mobile robot hybrid scheduling method based on topological map
Technical Field
The invention relates to the field of robots, in particular to a robot hybrid scheduling method based on a topological map.
Background
In the prior art, a grid map is generally adopted for robot scheduling, a forward search algorithm is generally adopted in the grid map of the robot, geometric judgment needs to be combined in a complex environment, and the construction of the geometric judgment of the robot is very complex due to different robot types and self parameters such as the height, the shape and the eccentricity of the robot. In a traditional grid map, the passing state of a node can be stored only by one binary number, and a robot can move and rotate freely in a passable node area, but the traditional forward search algorithm cannot calculate the moving edge of the robot in the grid map, and the robot can reach a specified node through the edge and in the posture which cannot be realized in the prior art.
Disclosure of Invention
One of the main objects of the present invention is to provide a mobile robot hybrid scheduling method based on a topological map, which can efficiently process a mobile solution of a robot on the basis of reducing the amount of calculation by calculating a simplified geometric judgment solution through the topological map.
The invention also mainly aims to provide a mobile robot hybrid scheduling method based on a topological map, which establishes a solution space including but not limited to a two-dimensional coordinate by dynamically planning the state dimension of the solution space, and also considers factors such as a robot angle and a shelf angle, thereby meeting the high utilization rate of the space in a complex environment.
The invention also aims to provide a mobile robot hybrid scheduling method based on a topological map, which can greatly improve the space utilization rate of the robot on the map by calculating the size, the shelf size and the path precision of the robot, calculating the sweeping range of the robot during moving or rotating, establishing a space inequality of a moving and rotating sweeping area according to the position of the robot, and establishing a geometric intersection function by combining the shelf position, the size and the path precision.
The invention also aims to provide a mobile robot hybrid scheduling method based on a topological map, and the hybrid scheduling method enables the robot to set the most appropriate posture to move in the path through edge comparison according to the width and height limits of the edges in the path planning precision and the geometric intersection function of the rotation or movement of the robot, so that the richness of the map path is improved.
The invention also aims to provide a mobile robot hybrid scheduling method based on a topological map, which realizes the control of path planning by increasing forward, reverse and transverse expansion sets of a target mobile solution space, setting the weight in the expansion sets and combining a set intersection function.
The invention also aims to provide a mobile robot hybrid scheduling method based on a topological map, which can increase the length of forward and reverse planning under the condition of high route richness, reduce the queuing depth, improve the replanning frequency, avoid replanning under the condition of low route richness, improve the queuing depth, reduce the waste of scheduling time and improve the scheduling efficiency by calculating the route richness under the topological map and setting the replanning frequency and the queuing depth of the robot according to the route richness.
The invention also aims to provide a mobile robot hybrid scheduling method based on a topological map, which provides an unlocking hybrid scheduling method on the basis of a geometric intersection function, sets priorities of different vehicles, carries out avoidance unlocking on vehicles with high priorities, calculates a planned path of each vehicle with high priority by an avoidance function, and carries out avoidance planning on a robot with low priority on the basis, thereby greatly improving unlocking operation efficiency under multi-vehicle deadlock.
The invention also aims to provide a mobile robot hybrid scheduling method based on a topological map, which can be simultaneously suitable for eccentric robots and non-eccentric robots, and can be simultaneously suitable for various types of robots through a geometric intersection function, a path planning strategy and a control unlocking strategy, so that the use barriers of different robots are broken on the basis of realizing high space utilization rate, and the adaptability of robot scheduling is improved.
In order to achieve at least one of the above objects, the present invention further provides a mobile robot hybrid scheduling method based on a topological map, which pre-establishes the topological map and acquires size information and position information of a target robot and an obstacle, the method comprising the steps of:
acquiring a moving and rotating swept area of the robot according to the size and the position of the robot;
acquiring a robot planning path, and establishing a geometric intersection function according to a swept area of the robot and the size of an obstacle;
judging the geometric intersection condition of the robot according to the position and the moving mode of the robot;
the path is maintained or re-planned in conjunction with the geometric intersection condition and the map information.
According to one preferred embodiment of the present invention, before determining the intersection condition of the robots according to the robot intersection function, a loose inequality is established according to the positions of the two robots, and if the positional relationship between the two robots does not satisfy the loose inequality, the robots are determined to be not intersected, wherein the loose inequality is as follows:
Figure BDA0002596844240000031
Figure BDA0002596844240000032
Figure BDA0002596844240000033
is a coordinate of the center of movement, rmaxIs the maximum distance, x, between the displacement center and four corner points of the robot rectangleright,xleft,ytop,ybottomAnd respectively, the value ranges of x and y coordinates in the region, and if the displacement center coordinates of the two robots do not satisfy inequality (1) or inequality (2), the robots are judged to be disjoint.
According to another preferred embodiment of the invention, the angle of the position where the mobile robot is located and the angle of the shelf are obtained, the feasibility and the driving cost of the robot moving to the adjacent node are calculated through a heuristic function, and a multi-dimensional Open set is established according to the type of the robot and the load, wherein the solution space of the Open set is state ═ id, θ, where id is the identifier of the node and θ is the angle of the mobile robot and the angle of the shelf when the load is applied.
According to another preferred embodiment of the present invention, a regular-angle movement and rotation scheme is established according to the topological map, the swept areas of the robots in the regular-angle movement and rotation scheme are calculated, and for different swept areas of the robots, simultaneous inequality groups are established, and the intersection condition between the robots is determined according to the simultaneous inequality groups.
According to another preferred embodiment of the invention, static shelf position, angle and geometric structure information are obtained, and the intersection condition between the robot and the static shelf is judged according to the swept area of the robot.
According to another preferred embodiment of the invention, the height limit and the width limit of the map barrier and the position of the additional barrier point of the map node are obtained, the intersection condition of the mobile robot and the map barrier and the intersection condition of the additional barrier point are calculated by adopting a geometric intersection function, and the path is planned according to the calculation result.
According to another preferred embodiment of the invention, a first extended set of the Open sets moving forwards, backwards and transversely is established according to the optimal solution node of the robot in the Open set solution space, and a second extended set of the Open sets of the robot and the gantry rotating clockwise or anticlockwise at the node.
According to another preferred embodiment of the present invention, a relationship function between the rotation angle change and the rotation weight is established during the scheduling process, and the relationship function is set as a convex function as follows:
Figure BDA0002596844240000034
wherein r in the formula (3) is the ratio of the rotation angle to 90 DEG, w0And (3) screening out the rotation state of the open set expansion set which is not in place in one step according to the formula (3) for increasing the rotation preference of the in-place in one step.
According to another preferred embodiment of the present invention, the route richness is determined according to the total number of reachable nodes in the map and the number of passable one-way segments and two-way segments, wherein the route richness determination formula is:
Figure BDA0002596844240000041
wherein D is the route richness, N is the total number of reachable nodes, and LSheetAnd LDouble isRespectively are a passable one-way line segment and a two-way line segment, alpha is a discount coefficient, and the value of alpha is as follows: 0<Alpha is less than or equal to 1, and the route re-planning frequency and the queuing depth are determined according to the D value.
According to another preferred embodiment of the invention, if D is greater than 3, the planning cost of the forward path of the planned robot is increased, the planning cost of the reverse path is greatly increased, the queuing depth of the same-direction path is reduced, and the re-planning frequency is increased;
if D is less than 3, the planning cost of the forward path of the robot is not planned any more, the planning cost of the reverse path is increased slightly, the queuing depth of the same-direction path is increased, and the robot is prohibited from re-planning.
According to another preferred embodiment of the invention, the robot priority is set according to the robot model and the load state, wherein the load robot priority is greater than the unloaded robot, the position and the node of each robot in the topological map are acquired, wherein the low-priority robot is used as an avoidance robot to acquire the planned path of each high-priority avoided robot, and the low-priority robot is driven to move to the nearest node outside the planned path of the high-priority robot according to an avoidance function for performing triggering unlocking on the robot.
In order to achieve at least one of the above objects, the present invention further provides a mobile robot hybrid scheduling system based on a topological map, where the system employs the above mobile robot hybrid scheduling method based on a topological map, including:
a control module for controlling the operation of the electronic device,
a signal receiving module;
a drive module;
the signal receiving module receives robot position information, map node information, passable line segment information and barrier information in a map, and the control module controls the driving module to operate on the topological map.
Drawings
FIG. 1 is a schematic flow chart of a mobile robot hybrid scheduling method based on a topological map according to the present invention;
FIG. 2 is a schematic diagram showing a swept area of an eccentric robot in a moving state in a mobile robot hybrid scheduling method based on a topological map according to the present invention;
FIG. 3 is a schematic diagram showing a swept area of an eccentric robot in a rotating state in a mobile robot hybrid scheduling method based on a topological map according to the present invention;
FIG. 4A is a schematic diagram showing obstacle-detouring turning in a mobile robot hybrid scheduling method based on a topological map according to the present invention;
fig. 4B is a schematic diagram showing a narrow lane turning method in the mobile robot hybrid scheduling method based on the topological map according to the present invention;
fig. 5 is a schematic unlocking diagram of a mobile robot hybrid scheduling method based on a topological map when multiple vehicles are locked.
Detailed Description
The following description is presented to disclose the invention so as to enable any person skilled in the art to practice the invention. The preferred embodiments in the following description are given by way of example only, and other obvious variations will occur to those skilled in the art. The underlying principles of the invention, as defined in the following description, may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
It will be understood by those skilled in the art that in the present disclosure, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in an orientation or positional relationship indicated in the drawings for ease of description and simplicity of description, and do not indicate or imply that the referenced devices or components must be in a particular orientation, constructed and operated in a particular orientation, and thus the above terms are not to be construed as limiting the present invention.
It is understood that the terms "a" and "an" should be interpreted as meaning that a number of one element or element is one in one embodiment, while a number of other elements is one in another embodiment, and the terms "a" and "an" should not be interpreted as limiting the number.
Referring to the flowchart shown in fig. 1, the robot types and the types of obstacles are classified in advance, and data such as the size, the volume, the eccentric condition, the spatial structure and the like of the robots of different types are recorded and stored; the method includes the steps of simultaneously recording the space structures of different obstacles, further obtaining the swept areas of robots of different models in a topological map, and establishing geometric intersection judgment functions according to different swept areas.
Referring to fig. 2 and fig. 3, it can be seen that the swept area of the eccentric robot is a rectangular parallelepiped structure when the eccentric robot translates, and referring to fig. 2, the swept area M of the translating state0Can be expressed as:
Figure BDA0002596844240000061
Figure BDA0002596844240000062
Figure BDA0002596844240000063
Figure BDA0002596844240000064
wherein
Figure BDA0002596844240000065
Is the vector of the geometric center of the robot deviating from the center of movement (the center of movement is the origin of the image) to the right,
Figure BDA0002596844240000066
is the translation vector, l and w are the length and width of the robot rectangle, respectively, and the above inequality satisfies the description of the swept area of the eccentric robot at all positions in the topological map.
It should be noted that, the intersection judgment of the robots is performed according to the swept area of the robots, in the above-mentioned inequality of the translational sweep of the eccentric robots, vectors deviating from the center of motion may be different for different types of geometric centers of the robots, and different l and w are set according to the types of the robots, and the translational vectors
Figure BDA0002596844240000067
Therefore, parameters such as a vector of the geometric center of the robot deviating from the moving center, the length and the width of the robot and the like need to be obtained in advance, for the intersection situation of different translational swept areas of the robot, the coincidence degree is judged according to the inequality, and if the coincidence occurs, the intersection situation of the robot can be judged.
The square area formed at an angle to the eccentric robot can be expressed as:
Figure BDA0002596844240000071
where the R operator is a degree-of-rotation rotor and θ is a rotation angle in the horizontal direction, the R operator can be expressed as:
Figure BDA0002596844240000072
thus when translating the vector
Figure BDA0002596844240000073
When the length direction is parallel to the side length direction, the swept area of the robot is a cuboid ifTranslation vector
Figure BDA0002596844240000074
When the value is any value, the robot is required to support omnidirectional movement or pseudo-omnidirectional movement, that is, the robot is required to support rotation of any angle while translating.
It should be noted that, when the conventional non-eccentric robot rotates, the swept area is in a sector or circle shape, and the swept area is easy to calculate, and if the robot is eccentric, the rotating swept area is a set of multiple images, as shown in fig. 3, when the rotation angle of the eccentric robot is right, a rectangular swept area Z is formed0The inequality is calculated as:
Figure BDA0002596844240000075
in the above inequality (8), the following inequality is used
Figure BDA0002596844240000076
Is the vector of the eccentric robot with the geometric center deviating from the displacement center to the right, and l and w are the length and width of the robot rectangle respectively.
Referring to fig. 3, the rectangular rotation yields the swept area:
Figure BDA0002596844240000077
wherein (9) the angle of the R rotation operator in the formula
Figure BDA0002596844240000078
For the initial angle of the robot in the map, θ is the robot rotation angle, and the four fan-shaped sweep areas formed by the moving center and the four corner connecting lines can be expressed as:
Figure BDA0002596844240000079
Figure BDA00025968442400000710
Figure BDA00025968442400000711
Figure BDA00025968442400000712
in the above formulae (11), (12), (13) and (14), arg (z) represents the argument of complex z. For the rotation of a general eccentric robot, the sweep area is generally composed of 6 areas in fig. 2, and therefore, the sweep area can be calculated as
Figure BDA00025968442400000713
There are various ways for the eccentric robot to intersect on the map, including:
intersection of eccentric robots in translation and in translation, i.e. different eccentric robots M0The intersection condition of the sweep areas is judged by the superposition state of inequalities of the sweep areas in the moving process of the two eccentric robots, and if the sweep areas are not superposed, the non-intersection can be judged.
Or the intersection condition of the translational state of an eccentric robot and the rotational state of another eccentric robot, i.e. M during the movement of the eccentric robot0Judging the superposition state of the sweep area and the sweep area of the other eccentric robot Z, and if the two sweep areas are not superposed, judging that the two eccentric robots are not crossed.
Or the intersection condition of the rotation state of the eccentric robot and the rotation state of the other eccentric robot, namely the coincidence degree of the Z swept areas is judged when the two eccentric robots respectively rotate, and if the two eccentric robots do not coincide, the robots can be judged to be not intersected.
It should be noted that the intersection function is obtained according to the planned path, and the overlap ratio of the swept area is compared, and the swept area changes according to the real-time change of the planned path, and the design including the motion mode, the robot type, the target position, and the like all affect the change of the real-time swept area of the robot, that is, the swept area of the robot changes dynamically, but because the position of the robot, the geometric parameters of the robot, and the planned path are preset in the system, the possibility of collision can be found by calculating the intersection function through the computer.
In another preferred embodiment of the present invention, the initial angle to the robot in the real scene is used
Figure BDA0002596844240000085
And the rotation angle theta is typically a regular angle, i.e. a multiple of 90 deg., so the intersection function decision for the robot will become more simplified, for example:
a non-eccentric robot is arranged to move horizontally from left to right, and the sweeping area of the non-eccentric robot is as follows:
Figure BDA0002596844240000081
wherein (15) is represented by
Figure BDA0002596844240000082
Is the coordinate of the moving center, another robot of the same type rotates counterclockwise by an angle of 90 degrees along the horizontal right direction, and the sweep area is as follows:
Figure BDA0002596844240000083
will be provided with
Figure BDA0002596844240000084
The intersection condition of the two areas can be obtained by combining the Z (0 degrees and 90 degrees).
It should be noted that, because the geometric intersection judgment between any two robots in the large-scale robot scheduling may bring a huge amount of calculation, but for robots far away from each other, the calculation of the geometric judgment is unnecessary, the invention provides a simple screening method, which specifically comprises the following steps:
establishing a machine position-related loose inequality before geometric judgment between robots;
judging the position relation of the robot according to the loose inequality, and if the position relation is far away, directly judging that the robot is not intersected, wherein the loose inequality can be the following inequality:
Figure BDA0002596844240000091
Figure BDA0002596844240000092
wherein xright,xleft,ytop,ybottomThe value ranges of x and y coordinates in the area are respectively, and the intersection condition can be judged according to the formula (1) or (2). By combining regular simplification and judgment of a loose inequality, geometric intersection judgment between robots can be greatly reduced, and high map utilization rate of the geometric intersection judgment is considered at the same time.
It should be noted that, during the movement of the robot, please refer to fig. 4A and 4B, the passable conditions of the nodes in the map are determined by the passable conditions of the nodes themselves and the edges adjacent to the nodes, generally related to the placement and the positions and the sizes of the static obstacles on the topological map, and the robot prestores the passable conditions of the nodes and the edges of the nodes before moving. In one preferred embodiment of the present invention, traffic conditions of nodes and adjacent edges in a circle range with a certain node as a center and a preset length as a radius are prestored, a static obstacle such as a shelf exists in the range, wherein the range is larger than a geometric projection plane of the static obstacle on a topological map, and for different robot types, a node influence range on the topological map is established according to the sizes of different types of robots, the range can be a circle, a square or a triangle, and the traffic states of the nodes and the edges are set automatically in the influence range, wherein the influence range also sets different moving states according to the moving state of the robot per se to generate different changes on the topological map, and the moving states include linear displacement, curve displacement, rotation and the like. It is to be understood that the shape and area of the field of influence formed by the static obstacles on the topological map is not a limitation of the present invention.
Further, the invention records the width limit and passable conditions of the node and the adjacent edges of the node, and sets the permission of different types of robots according to the type of the node, for example, in a preferred embodiment, the passing state of the node is set according to the influence range of the node influenced by the static obstacle, and the passing state comprises: allowing small-size machines smaller than a certain width to pass through, not allowing any robot to pass through, allowing large-size robots to pass through, allowing no-load robots or load robots to pass through and the like, wherein the setting of the passing states is automatically set according to the passing states of nodes and adjacent edges in the influence range of the static obstacles, or according to the intersection condition of geometric intersection functions among the dynamic obstacles, the dynamic obstacles and the static obstacles. In another preferred embodiment of the invention, each robot, the static obstacle and the height of the bottom of the target shelf are obtained, and the heights are used as parameters of one dimension to be input into the set intersection function, so that the passing design that the robot has a certain height range of the set limit at the bottom can be set, that is, the passing conditions of different robots at the bottoms of different obstacles are automatically set according to the height limit of the bottoms of different obstacles.
According to the angle of the robot in the topological map and the angle of a shelf, calculating the feasibility and the driving cost of the robot moving to the adjacent node according to a heuristic function, adding a calculation result into an Open set, wherein the Open set is used as a set for storing nodes which are existed in the map but are not investigated, and calculating path information in the Open set by adopting an A-star algorithm, wherein the solution space of the Open set can be expressed as: state, where id is the identifier of a node, θ is the angle of the mobile robot, and is the angle of a shelf under load, where θ and its value will be added into the Open set according to the angle of a line segment connecting the robots and the passable manner of the robots, so as to form an extended set of the Open set, for example, in a grid map, the angle of the robot and an adjacent and connected line segment is ψ, and according to the passable conditions of the node and the line segment, and by determining that the robot can move forward, backward, and laterally through a geometric intersection function, θ may have the value set as: phi { [ psi, psi +0.5 pi, psi +1.5 pi }, and according to the value set of angles and the connected nodes of the nodes, the extended set of the Open set can be expressed as:
Figure BDA0002596844240000101
wherein i represents the ith edge connected by the id, and the node number connected with the i is the idiIn some preferred embodiments of the present invention, for setting the preference of the driving direction of the robot, the weight of the extended centralized preference angle may be increased, for example, if the reverse or lateral driving of the robot needs to be reduced, the weight of the forward driving angle of the robot may be increased in the extended centralized preference so as to reduce the reverse or lateral driving of the robot, in one preferred embodiment of the present invention, if the rotation mode of the robot in one-time position is increased, a relationship function between the rotation angle and the rotation weight of the robot may be set, where the relationship function is the following inequality set:
Figure BDA0002596844240000102
wherein r in the formula (3) is the ratio of the rotation angle to 90 DEG, w0The method is characterized in that a weight for rotating by 90 degrees is preset, weight is a rotation weight, theta is a rotation angle of the robot, a rotation state which is not in one step in place in an open set expansion set is screened out according to an equation (3), and a rotation preference for in one step in place is added in an A-star algorithm.
It should be noted that the invention preferably adopts the a-star algorithm as the path planning algorithm, can obtain the shortest path of the target position by automatic search, and makes the space utilization rate of the topological map higher under the accurate geometric judgment and the passable judgment of regional nodes and line segments.
Further, in the process of path planning, it needs to be determined whether the path planning needs to be re-planned, and the re-planning needs to calculate the route richness of the machine at the current position, where the determination of the route richness can be performed according to the following function:
Figure BDA0002596844240000111
where N is the total number of nodes reachable in the map, LSheetAnd LDouble isRespectively the number of the one-way line segment and the two-way line segment which can be passed through, alpha is a discount coefficient and is generally 0<Alpha is less than or equal to 1, if the D value calculated at the current position of the robot is more than or equal to 3, the route richness of the current position of the robot can be judged to be higher, the forward planning route cost of the planned route can be considered to be increased, and the planning cost of the reverse route can be greatly increased. In other words, if the value D is larger than 3, the forward line segment length of the planned route is increased according to the A-star algorithm, the reverse line segment length of the planned route is greatly increased, the re-planning frequency of the A-star algorithm is increased, and the queuing depth of the A-star algorithm is reduced, so that the A-star algorithm is more prone to re-planning under the loop circulation condition. And when the D value is less than 3, the situation that the path richness of the current position of the robot is low is shown, the planning cost of the forward path of the planned robot is not increased any more, the planning cost of the reverse path is increased slightly, the queuing depth of the robot is increased, and the robot is forbidden to perform re-planning. It should be noted that, increasing the path cost by a small margin means extending the distance not greater than 1 topological map segment length, and increasing the path planning generation by a large margin means extending the distance not less than 2 topological map segment lengths.
Further, for a topological map with a D value close to 2, the situation that a route conflict is caused due to insufficient route resources and multiple deadlock is easily formed, and time is wasted when rescheduling is executed, so the present invention further provides an unlocking method according to the multiple deadlock phenomenon, please refer to the multiple deadlock diagram shown in fig. 5, and set priorities according to an evading vehicle, an evaded vehicle, a type of the vehicle and a load condition, wherein the priority of the evaded vehicle is higher than that of the evading vehicle, as shown in fig. 4. Robot B1、B2And B3For loading robots, robot A1、A2And A3Respectively, a non-loaded robot, B1Avoidance robot A with priority higher than no load as load robot1Robot B1The robot B can be known to move to the left target shelf according to a preset planning path and according to an unlocking function1Is located at a deadlock center, and is robot A1Will acquire robot B1Planning a route under barrier-free conditions, driving robot A1In a non-robot B1Nearest node stay on node due to robot A1Leading to robot a2As an avoidance vehicle, robot A2Will acquire robot A1And robot B1And drive robot A2Move to other than robot A1And robot B1Next nearest node outside the planned path, robot A1And robot A2Will move into the lane to the left. According to the unlocking method, the unlocking operation can be directly executed on the multi-vehicle locking phenomenon at one time, and the unlocking efficiency is greatly improved.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication section, and/or installed from a removable medium. The computer program, when executed by a Central Processing Unit (CPU), performs the above-described functions defined in the method of the present application. It should be noted that the computer readable medium mentioned above in the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be understood by those skilled in the art that the embodiments of the present invention described above and illustrated in the drawings are given by way of example only and not by way of limitation, the objects of the invention having been fully and effectively achieved, the functional and structural principles of the present invention having been shown and described in the embodiments, and that various changes or modifications may be made in the embodiments of the present invention without departing from such principles.

Claims (12)

1. A mobile robot hybrid scheduling method based on a topological map is provided, which pre-establishes the topological map and obtains the size information and the position information of a target robot and an obstacle, and is characterized by comprising the following steps:
acquiring a moving and rotating swept area of the robot according to the size and the position of the robot;
acquiring a robot planning path, and establishing a geometric intersection function according to a swept area of the robot and the size of an obstacle;
judging the geometric intersection condition of the robot according to the position and the moving mode of the robot;
the path is maintained or re-planned in conjunction with the geometric intersection condition and the map information.
2. The hybrid scheduling method of mobile robots based on topological map as claimed in claim 1, wherein before the intersection situation of robots is judged according to the robot intersection function, a loose inequality is established according to the positions of two robots, and if the positional relationship between two robots does not satisfy the loose inequality, the robots are judged to be not intersected, wherein the loose inequality is as follows:
Figure FDA0002596844230000011
Figure FDA0002596844230000012
Figure FDA0002596844230000013
is a coordinate of the center of movement, rmaxIs the maximum distance, x, between the displacement center and four corner points of the robot rectangleright,xleft,ytop,ybottomAnd respectively, the value ranges of x and y coordinates in the region, and if the displacement center coordinates of the two robots do not satisfy inequality (1) or inequality (2), the robots are judged to be not intersected.
3. The hybrid scheduling method of the mobile robot based on the topological map as claimed in claim 2, wherein an angle of a position where the mobile robot is located and a shelf angle are obtained, feasibility and driving cost of the robot moving to a neighboring node are calculated through a heuristic function, and a multi-dimensional Open set is established according to the type of the robot and a load, a solution space of the Open set is state [ id, θ ], wherein id is an identifier of the node, θ is an angle of the mobile robot, and is an angle of the shelf when the load is applied.
4. The hybrid scheduling method for mobile robots based on topological maps according to claim 3, wherein a first extended set of an Open set moving forward, backward and transversely is established according to an optimal solution node of a robot in an Open set solution space, and a second extended set of the Open set in which the robot and a gantry rotate clockwise or counterclockwise at the node.
5. The hybrid scheduling method of mobile robot based on topological map as claimed in claim 4, wherein a relationship function of rotation angle change and rotation weight is established during scheduling, and the relationship function is set as the following convex function:
Figure FDA0002596844230000021
wherein r in the formula (3) is the ratio of the rotation angle to 90 DEG, w0Presetting a weight for rotating by 90 degrees, wherein weight is the rotating weight, theta is the rotation angle of the robot, and an open set expansion set is screened out according to the formula (3)The non-one-step-in-place rotational state of (a) is used to increase the one-step-in-place rotational preference.
6. The mobile robot hybrid scheduling method based on the topological map as claimed in claim 1, wherein a regular-angle movement and rotation scheme is established according to the topological map, the swept areas of the robots in the regular-angle movement and rotation scheme are calculated, sets of inequalities are established simultaneously for the swept areas of different robots, and the intersection condition between the robots is judged according to the sets of inequalities.
7. The mobile robot hybrid scheduling method based on topological map as claimed in claim 1, wherein static shelf position, angle and geometric structure information are obtained, and the intersection condition between the robot and the static shelf is judged according to the swept area of the robot.
8. The mobile robot hybrid scheduling method based on the topological map as claimed in claim 1, wherein the height limit and the width limit of the map obstacle and the additional obstacle position of the map node are obtained, the intersection condition of the mobile robot with the map obstacle and the additional obstacle is calculated by adopting a geometric intersection function, and the path is planned according to the calculation result.
9. The hybrid scheduling method of mobile robot based on topological map as claimed in claim 1, wherein the route richness of the map is determined according to the total number of reachable nodes in the map and the number of passable one-way line segments and two-way line segments, wherein the route richness determination formula is:
Figure FDA0002596844230000022
wherein D is the route richness, N is the total number of reachable nodes, and LSheetAnd LDouble isRespectively are a passable one-way line segment and a two-way line segment, alpha is a discount coefficient, and the value of alpha is as follows: 0<Alpha is less than or equal to 1, and the route re-planning frequency is determined according to the D valueAnd the queuing depth.
10. The hybrid scheduling method of mobile robot based on topological map as claimed in claim 9, wherein if D >3, the planning cost of the planned robot for forward path is increased, and the planning cost of the reverse path is increased substantially, and at the same time, the queuing depth of the same-direction path is reduced, and the re-planning frequency is increased;
if D is less than 3, the planning cost of the forward path of the planned robot is not increased any more, the planning cost of the reverse path is increased slightly, the queuing depth of the same-direction path is improved, and the robot is forbidden to perform re-planning.
11. The mobile robot hybrid scheduling method based on the topology map as claimed in claim 1, wherein the robot priority is set according to the model and the load state of the robot, wherein the load robot priority is greater than the unloaded robot, the position and the node of each robot in the topology map are obtained, wherein the low-priority robot is used as an avoidance robot to obtain the planned path of each high-priority avoided robot, and the low-priority robot is driven to move to the nearest node outside the planned path of the high-priority robot according to an avoidance function for performing triggering unlocking on the robot.
12. A mobile robot hybrid scheduling system based on a topological map is provided, and the system adopts the mobile robot hybrid scheduling method based on the topological map, and is characterized by comprising the following steps:
a control module for controlling the operation of the electronic device,
a signal receiving module;
a drive module;
the signal receiving module receives robot position information, map node information, passable line segment information and barrier information in a map, and the control module controls the driving module to operate on the topological map.
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