Advanced avoidance method and device based on mobile robot and computer storage medium

Abstract

The invention provides an advanced avoidance method based on a mobile robot, an advanced avoidance device based on the mobile robot and a computer storage medium. The advanced avoidance method comprises the following steps: in response to an advanced avoidance instruction, obtaining at least two target mobile robots corresponding to the advanced avoidance instruction; obtaining a plurality of candidate avoidance points between the at least two target mobile robots; obtaining avoidance cost values of the plurality of candidate avoidance points; sorting the plurality of candidate avoidance points according to the avoidance cost values, and determining a target avoidance point according to a sorting result; and taking the target avoidance point as an advanced avoidance point in the at least two target mobile robots, and controlling at least one target mobile robot to go to the advanced avoidance point. According to the advanced avoidance method based on the mobile robot, advanced avoidance is carried out by selecting the advanced avoidance point and planning the appropriate avoidance point of the target mobile robot, and the overall operation efficiency of a mobile robot system is improved.

Description

Technical Field

[0001] The present application relates to the technical field of robot path planning, and in particular to an advance avoidance method based on a mobile robot, an advance avoidance device based on a mobile robot, and a computer storage medium. Background Art

[0002] With the continuous improvement of industrial automation procedures, robots (such as handling robots) are being used more and more widely. They are mainly used in logistics, clothing, e-commerce, retail, manufacturing, medicine and other industries. They are responsible for handling work in smart warehouses or manufacturing workshops, and are playing an increasingly important role in improving system efficiency and saving manpower.

[0003] However, in the process of cluster scheduling, the Automated Guided Vehicles (AGVs) in unmanned factories will inevitably have conflicts in paths, causing one vehicle to wait for another or even multiple vehicles to pass before it can pass. This phenomenon will not only cause serious traffic jams, but also greatly affect the work efficiency of the entire unmanned factory. Summary of the invention

[0004] In order to solve the above technical problems, the present application proposes an advance avoidance method based on a mobile robot, an advance avoidance device based on a mobile robot, and a computer storage medium.

[0005] In order to solve the above technical problems, the present application proposes an advance avoidance method based on a mobile robot, and the advance avoidance method includes:

[0006] In response to the advance avoidance instruction, acquiring at least two target mobile robots corresponding to the advance avoidance instruction;

[0007] Acquire a number of candidate avoidance points between the at least two target mobile robots;

[0008] Obtaining avoidance cost values ​​of the plurality of candidate avoidance points;

[0009] Sorting the candidate avoidance points according to the avoidance cost values, and determining a target avoidance point according to the sorting result;

[0010] The target avoidance point is used as an advance avoidance point of the at least two target mobile robots, and at least one target mobile robot is controlled to go to the advance avoidance point.

[0011] In order to solve the above-mentioned technical problems, the present application also proposes an advance avoidance device based on a mobile robot, and the advance avoidance device based on a mobile robot includes a memory and a processor coupled to the memory; wherein the memory is used to store program data, and the processor is used to execute the program data to implement the advance avoidance method based on the mobile robot as mentioned above.

[0012] In order to solve the above technical problems, the present application also proposes a computer storage medium, which is used to store program data. When the program data is executed by a computer, it is used to implement the above-mentioned advance avoidance method based on a mobile robot.

[0013] Compared with the prior art, the beneficial effects of the present application are as follows: the advance avoidance device based on the mobile robot responds to the advance avoidance instruction, obtains at least two target mobile robots corresponding to the advance avoidance instruction; obtains several candidate avoidance points between the at least two target mobile robots; obtains avoidance cost values ​​of the several candidate avoidance points; sorts the several candidate avoidance points according to the avoidance cost values, and determines the target avoidance point according to the sorting result; uses the target avoidance point as the advance avoidance point among the at least two target mobile robots, and controls at least one target mobile robot to go to the advance avoidance point. Through the above-mentioned advance avoidance method based on the mobile robot, by selecting the advance avoidance point, the appropriate avoidance point of the target mobile robot is planned for advance avoidance, thereby improving the overall operation efficiency of the mobile robot system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] in:

[0016] Figure 1 It is a flowchart of an embodiment of an advance avoidance method based on a mobile robot provided by the present application;

[0017] Figure 2 is a flow chart of another embodiment of the advance avoidance method based on a mobile robot provided by the present application;

[0018] Figure 3 It is a schematic diagram of the scenarios of three conflict detection situations provided by this application;

[0019] Figure 4 yes Figure 1 The specific flow chart of step S12 of the advance avoidance method is shown;

[0020] Figure 5 This is a specific flow chart of step S13 of the advance avoidance method provided by the present application;

[0021] Figure 6 This is a schematic diagram of a scenario of an embodiment of avoidance point screening provided by the present application;

[0022] Figure 7 It is a schematic diagram of an embodiment of an advance avoidance scenario provided by the present application;

[0023] Figure 8 It is a structural schematic diagram of an embodiment of an advance avoidance device based on a mobile robot provided by the present application;

[0024] Fig. 9 It is a structural diagram of an embodiment of a computer storage medium provided by the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] The advance avoidance method proposed in this application can achieve continuous avoidance of a single vehicle or multiple vehicles through conflict detection and optimal avoidance point selection, which can effectively solve the traffic jam caused by waiting on the spot and greatly improve the overall scheduling efficiency.

[0028] Please refer to Figure 1 , Figure 1 It is a flow chart of an embodiment of an advance avoidance method based on a mobile robot provided in the present application.

[0029] The advance avoidance method based on a mobile robot of the present application is applied to an advance avoidance device based on a mobile robot, wherein the advance avoidance device based on a mobile robot of the present application can be a server, or a terminal device, or a system in which a server and a terminal device cooperate with each other. Accordingly, the various parts included in the advance avoidance device based on a mobile robot, such as various units, subunits, modules, and submodules, can all be set in the server, or all be set in the terminal device, or can be set in the server and the terminal device respectively.

[0030] Furthermore, the above-mentioned server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or it can be implemented as a single server. When the server is software, it can be implemented as multiple software or software modules, such as software or software modules used to provide distributed servers, or it can be implemented as a single software or software module, which is not specifically limited here.

[0031] Specifically, the mobile robot-based advance avoidance device of the present application can be a processing unit or processor in any mobile robot, or can be a processing platform that remotely controls all mobile robots, etc.

[0032] It should be noted that the mobile robot involved in this application is specifically a robot system with mobile functions composed of mechanisms such as sensors, remote control operators and automatic controllers. It can replace humans to perform operations in dangerous or harsh environments, and has greater mobility and flexibility than ordinary robots. It is divided into ground, air, surface and underwater mobile robots, and its mobile mechanisms include wheeled, tracked, footed, hybrid, special and other types. In the following embodiments, an AGV car is used as an example to introduce the advance avoidance method of this application, but it does not limit the specific type of mobile robot.

[0033] like Figure 1 As shown, the specific steps are as follows:

[0034] Step S11: In response to the advance avoidance instruction, obtaining at least two target mobile robots corresponding to the advance avoidance instruction.

[0035] In the embodiment of the present application, when a cluster scheduling conflict occurs, the advance avoidance device can generate an advance avoidance instruction and determine at least two vehicles in conflict according to the advance avoidance instruction. The cluster scheduling conflict is when multiple vehicles overlap in the opposite direction during the scheduling process. For example, if vehicle A is going from point n to point m, and vehicle B is going from point m to point n, a scheduling conflict occurs.

[0036] When conflicts arise in AGV cluster scheduling, the mainstream solution is to let car A wait in place, and then let car A pass after car B passes. This method will cause car A to wait for a long time and cause certain traffic jams. The advance avoidance method proposed in this application is that when cars AB detect that a path conflict is about to occur, one of the cars will choose a suitable avoidance point between cars AB to avoid in advance, thereby improving the overall operating efficiency. The following focuses on the conflict detection and avoidance point selection logic in the algorithm.

[0037] For details on the conflict detection logic, that is, the process of generating early avoidance instructions, please continue to refer to Figure 2 , Figure 2 It is a flow chart of another embodiment of the advance avoidance method based on a mobile robot provided in the present application.

[0038] like Figure 2 As shown, the specific steps are as follows:

[0039] Step S21: Obtain a first prediction segment starting point and a first prediction segment lock grid of a first target mobile robot, and a second prediction segment starting point and a second prediction segment lock grid of a second target mobile robot among at least two target mobile robots.

[0040] In an embodiment of the present application, each vehicle will perform safety checks on the segment paths of the real-time segment and the predicted segment during movement, and the advance avoidance device can determine whether there is a scheduling conflict between multiple vehicles by performing safety checks on the predicted segment of the vehicle.

[0041] Specifically, the advance avoidance device needs to obtain the real-time segment-by-segment path and predicted segment-by-segment path of each vehicle, wherein the real-time segment-by-segment path is the path that the vehicle is currently taking, and the predicted segment-by-segment path is the path that the vehicle is about to take.

[0042] The advance avoidance device determines the predicted segment lock grid according to the predicted segment path of the vehicle and the vehicle size, such as length and width. The lock grid is the area range of a segment of vehicle movement generated according to the real-time segment path of the vehicle, the predicted segment path and the length and width of the vehicle body. Therefore, the predicted segment lock grid is the area range generated according to the predicted segment path of the vehicle and the size of the vehicle.

[0043] In addition, the advance avoidance device also needs to determine the predicted segment starting point of the vehicle within the predicted segment lock grid.

[0044] It should be noted that this application uses a locked grid method for conflict detection, which can effectively take into account the actual road conditions and the actual vehicle conditions. The area range of the locked grid takes into account the path range and vehicle size of the vehicle. For scenarios such as a single channel, that is, there is only one two-way road in the channel, if the road width is sufficient to accommodate vehicles of two sizes passing at the same time, then there may be no scheduling conflict. Compared with the conflict detection logic performed only through path points, the conflict detection logic performed by the locked grid in this application can better reflect the actual scheduling situation.

[0045] Step S22: In response to the starting point of the first prediction segment being located within the lock grid of the second prediction segment, and the starting point of the second prediction segment being located within the lock grid of the first prediction segment, generating advance avoidance instructions for at least two target mobile robots.

[0046] In the embodiments of this application, please refer to Figure 3 , Figure 3 Schematic diagram of three conflict detection scenarios provided by this application. Figure 3 As shown, the conflict detection of this application is divided into the following three situations:

[0047] 1. The two vehicles cannot detect each other. Figure 3 As shown in (a), the prediction segment locks of the two vehicles cannot detect the starting point of the other vehicle's prediction segment, so there is no conflict between the vehicles.

[0048] 2. One of the cars detects the other car. Figure 3 As shown in (b), the prediction segment lock of vehicle A detects the starting point of the prediction segment of vehicle B, but the prediction segment of vehicle B does not detect the starting point of the prediction segment of vehicle A, so there is no conflict between the vehicles.

[0049] 3. The two vehicles detect each other. Figure 3 As shown in (c), the prediction segment lock grids of vehicles AB detect each other's prediction starting point, so there is a prediction conflict, and a vehicle collision is about to occur. At this time, the advance avoidance device can generate an advance avoidance instruction according to vehicles AB.

[0050] Step S12: Acquire several candidate avoidance points between at least two target mobile robots.

[0051] In an embodiment of the present application, after determining that a scheduling conflict has occurred, the advance avoidance device continues to execute the avoidance point selection logic, and further searches for the optimal avoidance point between the vehicles having the scheduling conflict for early avoidance.

[0052] Therefore, the advance avoidance device needs to first determine several candidate avoidance points that may become the optimal avoidance points. Among them, the screening schemes for several candidate avoidance points include but are not limited to: directly using all currently available path points as candidate avoidance points; obtaining a range set of candidate avoidance points by manually marking the range; screening all currently available path points according to preset rules to exclude path points that cannot become the optimal avoidance points.

[0053] Please continue to read Figure 4 , Figure 4 yes Figure 1 The specific flow chart of step S12 of the advance avoidance method is shown.

[0054] like Figure 4 As shown, the specific steps are as follows:

[0055] Step S121: Acquire all original avoidance points between at least two target mobile robots.

[0056] In an embodiment of the present application, the advance avoidance device uses the currently available path points of the environment where the vehicles that need to be planned for advance avoidance, such as vehicles AB, are located as the original avoidance points.

[0057] Step S122: Exclude the original avoidance points outside the circular area with the positions of at least two target mobile robots as the diameter.

[0058] In the embodiment of the present application, the advance avoidance device generates a circular area with the line segment connecting the current positions of the two vehicles AB as the diameter, and excludes the original avoidance points outside the circular area. The reason is that the original avoidance points outside the circular area will cause the two vehicles AB to travel a longer distance, resulting in a larger dispatch range.

[0059] Step S123: excluding the original avoidance point where the stationary mobile robot is located between at least two target mobile robots.

[0060] In the embodiment of the present application, the advance avoidance device excludes the original avoidance point where the stationary vehicle is located in the environment where the two vehicles AB are currently located. The reason is that the path point where the stationary vehicle is located is defaulted to be unreachable by the two vehicles AB.

[0061] Step S124: Eliminate the original avoidance point on the conflict path between at least two target mobile robots.

[0062] In the embodiment of the present application, the advance avoidance device excludes the original avoidance point on the conflict path between vehicles AB, such as Figure 3 As shown in (c), the path between the predicted grid start point of car A and the predicted grid start point of car B is the conflict path between cars AB. The reason is that both cars AB have to pass through the points on the conflict path, and they cannot become avoidance points.

[0063] Step S125: The original avoidance points remaining after the exclusion are used as several candidate avoidance points.

[0064] In the embodiment of the present application, the advance avoidance device excludes some original avoidance points through one or more steps from step S122 to step S124, and the remaining original avoidance points can be used as candidate avoidance points for subsequent avoidance point selection logic. Through the above exclusion scheme, the complexity of subsequent avoidance point cost value calculation can be effectively reduced, and the avoidance point selection efficiency can be improved.

[0065] Furthermore, considering the multi-vehicle scenario, there may be advance avoidance points used by other vehicles among the candidate avoidance points of vehicles A and B. Vehicles A and B cannot use these advance avoidance points to avoid scheduling conflicts with other vehicles. Therefore, the advance avoidance device can further exclude advance avoidance points used by other mobile robots.

[0066] Step S13: Obtain avoidance cost values ​​of several candidate avoidance points.

[0067] In the embodiment of the present application, the advance avoidance device calculates the avoidance cost values ​​of all candidate avoidance points. The lower the avoidance cost value, the higher the possibility of becoming the optimal avoidance point. When calculating the cost value of the candidate avoidance point, the path distance to the avoidance point, the number of turns required, etc. can be considered.

[0068] Please refer to Figure 5 , Figure 5 It is a specific flow chart of step S13 of the advance avoidance method provided in this application. Figure 5 Display the calculation process of the avoidance cost for each candidate avoidance point. The advance avoidance device needs to traverse all candidate avoidance points and repeat the process. Figure 6 The calculation logic is shown.

[0069] like Figure 5 As shown, the specific steps are as follows:

[0070] Step S131: Obtain candidate intersections before the candidate avoidance point.

[0071] In an embodiment of the present application, the advance avoidance device determines a candidate intersection of a candidate avoidance point, wherein the candidate intersection is a path point on the conflict path of vehicles AB. At the candidate intersection, the vehicle that needs to be avoided goes to the candidate avoidance point, and the other vehicle directly passes through the candidate intersection, thereby completing the scheduling effect of the present application.

[0072] Step S132: Obtain a first path distance from the first target mobile robot to the candidate avoidance point.

[0073] Step S133: Obtain a second path distance from the candidate avoidance point to the second target mobile robot.

[0074] Step S134: Obtain a third path distance from the candidate intersection to the candidate avoidance point.

[0075] In the embodiments of the present application, Figure 6 As shown in the figure, assume that the vehicles are moving at a constant speed of v; the avoidance point is e4; the location of vehicle A is a; and the location of vehicle B is b. a_e4 is the first path distance from point a to e4; e4_b is the second path distance from e4 to b; and r_e4 is the third path distance from point r to e4. Figure 6 It is a scene diagram of an embodiment of avoidance point screening provided by the present application.

[0076] in, Figure 6 Among them, e1, e2, e3, e5, and e6 are all candidate avoidance points.

[0077] Step S135: Obtain the first turning number of the first target mobile robot to the candidate avoidance point.

[0078] Step S136: Obtain the second turning number of the second target mobile robot to the candidate intersection.

[0079] In this embodiment of the present application, it is assumed that the time taken for car A and car B to turn on the spot is equivalent to the time taken to run a distance of 5 meters. Car A needs to make na turns to reach the avoidance point, and car B needs to make nb turns at intersection r before reaching the avoidance point.

[0080] Step S137: Calculate the avoidance cost value of the candidate avoidance point based on the first path distance, the second path distance, the third path distance, the first number of turns, and the second number of turns.

[0081] In the embodiment of the present application, if the time Ta from point a to e4 is equal to the time Tb from point b to r, it is an ideal avoidance point, so the smaller |Ta-Tb| is, the better. By simplification, the following avoidance point cost value Cost_e4 can be obtained:

[0082] Cost_e4=|a_e4–e4_b|+r_e4+(na–nb)*5

[0083] Furthermore, in the process of multiple vehicle avoidance, if there is a used avoidance point (such as e5) in the path of vehicle A to avoidance point e4, then avoidance point e4 is excluded.

[0084] Step S14: sorting a number of candidate avoidance points according to the avoidance cost values, and determining a target avoidance point according to the sorting result.

[0085] In an embodiment of the present application, the advance avoidance device selects an avoidance point with the smallest avoidance cost as a target avoidance point in a two-vehicle collision scenario.

[0086] Step S15: Using the target avoidance point as an advance avoidance point among at least two target mobile robots, and controlling at least one target mobile robot to move toward the advance avoidance point.

[0087] In an embodiment of the present application, the advance avoidance device selects vehicle A to go to the target avoidance point, and at the same time controls vehicle B to travel according to the original predicted segment path, thereby completing cluster scheduling.

[0088] During the cluster dispatching process, multiple vehicles may conflict and need to avoid each other in advance. The following uses four vehicles ABCD as an example to demonstrate the effect of the algorithm for multiple vehicles avoiding each other in advance. Figure 7 , Figure 7 It is a schematic diagram of an embodiment of an advance avoidance scenario provided in the present application.

[0089] The end points of cars AB are END2, and the end points of cars CD are END1. The dotted arrow 2 represents the avoidance path triggered by car B, the dotted arrow 1 represents the avoidance path triggered by car A, and the black solid arrows 3 and 4 represent the movement directions of cars CD, respectively. Figure 7 Among them, e1 to e8 are avoidance points.

[0090] First, if Figure 7 As shown in (a), both cars AB conflict with the predicted segment safety detection of car C, and both trigger the early avoidance function. Car A first selects the optimal avoidance point e4, and car B chooses the optimal avoidance point e1 to avoid car C because the avoidance point e4 has been used.

[0091] When car C passes, Figure 7 As shown in (b), car A conflicts with the predicted segment safety detection of car D again, and will select the optimal avoidance point e5 for continuous avoidance. Then car B conflicts with the predicted segment safety detection of car D again, and selects the optimal avoidance point e4 for continuous avoidance.

[0092] Finally, if Figure 7 As shown in (c), ABCD have all arrived at the destination. The early avoidance function avoids the long waiting time of the vehicle and greatly improves the working efficiency of AGV.

[0093] In the present application, the advance avoidance device responds to the advance avoidance instruction, obtains at least two target mobile robots corresponding to the advance avoidance instruction; obtains several candidate avoidance points between the at least two target mobile robots; obtains avoidance cost values ​​of the several candidate avoidance points; sorts the several candidate avoidance points according to the avoidance cost values, and determines the target avoidance point according to the sorting result; uses the target avoidance point as the advance avoidance point among the at least two target mobile robots, and controls at least one target mobile robot to go to the advance avoidance point. Through the above-mentioned advance avoidance method based on mobile robots, by selecting advance avoidance points, planning appropriate avoidance points of the target mobile robots for advance avoidance, the overall operation efficiency of the mobile robot system is improved.

[0094] When two or more vehicles are predicted to conflict, the advance avoidance method of this application can initiate one or more continuous avoidances, avoiding vehicles from waiting for a long time due to path conflicts, and also avoiding traffic jams. This advance avoidance method can greatly improve the efficiency of AGV cluster scheduling, especially in narrow single-channel working environments.

[0095] The advance avoidance method of the present application normalizes factors such as the vehicle's movement speed, the cost of turning on the spot, and various unavoidable points into time costs. Through rigorous mathematical derivation, a more excellent avoidance point cost calculation method is obtained, which to a large extent ensures the feasibility of advance avoidance.

[0096] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0097] In order to realize the above-mentioned advance avoidance method based on mobile robot, the present application also proposes an advance avoidance device based on mobile robot, please refer to Figure 8 , Figure 8 It is a structural schematic diagram of an embodiment of an advance avoidance device based on a mobile robot provided in the present application.

[0098] The mobile robot-based advance avoidance device 400 of this embodiment includes a processor 41 , a memory 42 , an input / output device 43 , and a bus 44 .

[0099] The processor 41, the memory 42, and the input / output device 43 are respectively connected to the bus 44. The memory 42 stores program data. The processor 41 is used to execute the program data to implement the advance avoidance method based on the mobile robot described in the above embodiment.

[0100] In the embodiment of the present application, the processor 41 may also be referred to as a CPU (Central Processing Unit). The processor 41 may be an integrated circuit chip having the ability to process signals. The processor 41 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or the processor 41 may also be any conventional processor, etc.

[0101] This application also provides a computer storage medium, please continue to refer to Fig. 9 , Fig. 9 It is a structural diagram of an embodiment of a computer storage medium provided in the present application. The computer storage medium 600 stores a computer program 61. When the computer program 61 is executed by the processor, it is used to implement the advance avoidance method based on the mobile robot of the above embodiment.

[0102] When the embodiments of the present application are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk.

[0103] The above description is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. An advance avoidance method based on a mobile robot, characterized in that: The advance avoidance method comprises: In response to the advance avoidance instruction, acquiring at least two target mobile robots corresponding to the advance avoidance instruction; Acquire a number of candidate avoidance points between the at least two target mobile robots; Obtaining avoidance cost values ​​of the plurality of candidate avoidance points; Sorting the candidate avoidance points according to the avoidance cost values, and determining a target avoidance point according to the sorting result; The target avoidance point is used as an advance avoidance point of the at least two target mobile robots, and at least one target mobile robot is controlled to go to the advance avoidance point.

2. The advance avoidance method according to claim 1, characterized in that: The advance avoidance method further includes: Obtain a first prediction segment starting point and a first prediction segment lock grid of a first target mobile robot, and a second prediction segment starting point and a second prediction segment lock grid of a second target mobile robot among the at least two target mobile robots; In response to the first prediction segment starting point being located within the second prediction segment lock grid, and the second prediction segment starting point being located within the first prediction segment lock grid, an advance avoidance instruction for the at least two target mobile robots is generated.

3. The advance avoidance method according to claim 2, characterized in that: The predicted segment lock grid is an area range generated according to the predicted segment path of the mobile robot and the size of the mobile robot.

4. The advance avoidance method according to claim 1, characterized in that: The step of obtaining a plurality of candidate avoidance points between the at least two target mobile robots comprises: Acquire all original avoidance points between the at least two target mobile robots; Excluding original avoidance points outside a circular area whose diameter is the location of the at least two target mobile robots; and / or, excluding an original avoidance point where a stationary mobile robot is located between the at least two target mobile robots; and / or, excluding original avoidance points on the conflicting paths between the at least two target mobile robots; The original avoidance points remaining after the elimination are used as the several candidate avoidance points.

5. The advance avoidance method according to claim 4, characterized in that: The at least two target mobile robots include three or more mobile robots; After obtaining all original avoidance points between the at least two target mobile robots, the advance avoidance method further includes: The advance avoidance point between the at least two target mobile robots that is used by other mobile robots is excluded.

6. The advance avoidance method according to claim 1, characterized in that: The obtaining of the avoidance cost values ​​of the plurality of candidate avoidance points includes: Obtain the avoidance cost value of each candidate avoidance point according to the following steps: obtain a candidate intersection before the candidate avoidance point; Obtaining a first path distance from the first target mobile robot to the candidate avoidance point; Obtaining a second path distance from the candidate avoidance point to the second target mobile robot; Obtaining a third path distance from the candidate intersection to the candidate avoidance point; Obtaining a first turning number of the first target mobile robot to the candidate avoidance point; Obtaining a second turning number of the second target mobile robot to the candidate intersection; An avoidance cost value of the candidate avoidance point is calculated based on the first path distance, the second path distance, the third path distance, the first number of turns, and the second number of turns.

7. The advance avoidance method according to claim 6, characterized in that: The at least two target mobile robots include three or more mobile robots; After obtaining the avoidance cost values ​​of the plurality of candidate avoidance points, the advance avoidance method further includes: Eliminating candidate avoidance points and avoidance cost values ​​of the avoidance points in advance used by other mobile robots from the plurality of candidate avoidance points; And / or, eliminate candidate avoidance points and their avoidance cost values ​​for which there are advance avoidance points used by other mobile robots on the paths toward the candidate avoidance points.

8. The advance avoidance method according to claim 6 or 7, characterized in that: The advance avoidance method further includes: Obtaining the moving speed of the target mobile robot; Obtaining a turning time of the target mobile robot turning in situ; Based on the turning time and the movement speed, obtaining an equivalent movement distance of the target mobile robot turning in situ; The calculating the avoidance cost value of the candidate avoidance point based on the first path distance, the second path distance, the third path distance, the first number of turns, and the second number of turns includes: An avoidance cost value of the candidate avoidance point is calculated based on the first path distance, the second path distance, the third path distance, the first number of turns, the second number of turns, and the equivalent motion distance.

9. An advance avoidance device based on a mobile robot, characterized in that: The mobile robot-based advance avoidance device includes a memory and a processor coupled to the memory; The memory is used to store program data, and the processor is used to execute the program data to implement the advance avoidance method based on a mobile robot as described in any one of claims 1 to 8.

10. A computer storage medium, characterized in that: The computer storage medium is used to store program data, and when the program data is executed by a computer, it is used to implement the advance avoidance method based on a mobile robot as described in any one of claims 1 to 8.

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