WO2021254369A1

WO2021254369A1 - Robot repositioning method and apparatus, electronic device, and storage medium

Info

Publication number: WO2021254369A1
Application number: PCT/CN2021/100284
Authority: WO
Inventors: 马福强; 王超; 姚秀军; 桂晨光; 蔡禹丞; 蔡小龙; 李振; 郭新然; 崔丽华
Original assignee: 京东科技信息技术有限公司
Priority date: 2020-06-18
Filing date: 2021-06-16
Publication date: 2021-12-23
Also published as: CN111765884B; CN111765884A

Abstract

Provided are a robot repositioning method and apparatus, an electronic device, and a storage medium. The method comprises: when determining that a robot is not located at an origin, obtaining a grid map corresponding to the current region (S101); performing downsampling on the grid map on the basis of a preset first depth rule to generate a first multi-resolution grid map (S102); performing iterative search on the first multi-resolution grid map to determine a first position of the robot in the grid map (S103); and determining the first position as the position of the robot with respect to the origin and outputting the first position (S104). The method can quickly complete repositioning and avoid the occurrence of repositioning errors in the presence of repeated scenarios.

Description

Robot relocation method, device, electronic equipment and storage medium

References to related applications

This disclosure requires an invention patent application filed with the State Intellectual Property Office of the People’s Republic of China on June 18, 2020, with an application number of 202010561916.X and an invention title of "a method, device, electronic equipment and storage medium for robot relocation" All rights and interests, and incorporate all its contents into this disclosure by way of reference.

field

The present disclosure generally relates to the technical field of mobile robot positioning and navigation, and more specifically to robot repositioning methods, devices, electronic equipment, and storage media.

background

With the continuous development of semiconductor and computer technologies, indoor robot technology based on multi-sensor fusion has gradually emerged, taking on more tasks such as indoor inspection, service, and transportation, and greatly reducing labor costs. Among them, navigation and positioning technology is one of the key technologies of indoor robot technology, and it is the key to realize the autonomous movement of mobile robots. Currently commonly used positioning technologies mainly include outside-in methods and inside-out methods. Among them, typical outside-in methods include visual recognition methods based on April tag codes and indoor positioning methods based on visible light communication, while typical inside-out methods include AMCL (Adaptive Monte Carlo Localization) based on lidar, and adaptive Monte Carlo localization. )method.

Because the outside-in method needs to set a priori information in the working environment in advance, and needs to modify the working environment, the usage scenarios are limited. The inside-out method actively locates the position in the working environment, without pre-setting prior information in the working environment, and without modifying the working environment. Therefore, the AMCL method based on lidar is currently the mainstream positioning method for indoor robots. In actual use, the AMCL method based on lidar supports the relocation function in theory, but its computational complexity is large, and it takes a long time to converge, and relocation cannot be completed quickly. It is extremely easy when there are repeated scenes. A relocation error has occurred.

Overview

In the first aspect, the present disclosure provides a robot relocation method, which includes:

When it is determined that the robot is not located at the origin, obtain the grid map corresponding to the current area;

Down-sampling the raster map based on a preset first depth rule to generate a first multi-resolution raster map;

Performing an iterative search on the first multi-resolution grid map to determine the first position of the robot in the grid map; and

The first position is determined to be the position of the robot relative to the origin and output.

In some embodiments, the iterative search on the first multi-resolution grid map to determine the first position of the robot in the grid map includes:

Determining a first iterative search order of the first multi-resolution raster map based on the resolution, wherein the iterative order includes an iterative search from low resolution to high resolution; and

Based on the first iterative search order, an iterative search is performed on the first multi-resolution grid map to determine the first position of the robot in the grid map.

In some embodiments, the iterative search on the first multi-resolution grid map based on the first iterative search order to determine the first position of the robot in the grid map includes :

Determining the search area of the robot in the grid map; and

Based on the first iterative search sequence, an iterative search is performed on the search area in the first multi-resolution grid map to determine the first position of the robot in the grid map.

In some embodiments, the determining and outputting the first position as the position of the robot relative to the origin includes:

Optimizing the first position by using a preset first target optimization algorithm to obtain the first target position; and

Determining that the first target position is the position of the robot relative to the origin and outputting;

Wherein, the first position and the first target position both include X-axis coordinates, Y-axis coordinates, and angular offset.

In some embodiments, the method further includes:

In the case where the relocation of the robot is completed, or in the case where it is determined that the robot is located at the origin, obtaining the initial position output by the AMCL method;

Down-sampling the raster map based on a preset second depth rule to generate a second multi-resolution raster map;

Performing an iterative search on the second multi-resolution grid map based on the initial position to determine the second position of the robot in the grid map; and

The second position is determined to be the position of the robot relative to the origin and output.

In some embodiments, the iterative search on the second multi-resolution grid map based on the initial position to determine the second position of the robot in the grid map includes:

Determining a second iterative search order of the second multi-resolution raster map based on the resolution, wherein the second iterative search order includes an iterative search from low resolution to high resolution; and

Based on the initial position and the second iterative search order, an iterative search is performed on the second multi-resolution grid map to determine the second position of the robot in the grid map.

In some embodiments, the determining and outputting the second position as the position of the robot relative to the origin includes:

Optimizing the second position by using a preset second target optimization function to obtain the second target position; and

Determining that the second target position is the position of the robot relative to the origin and outputting;

Wherein, the second position and the second target position both include X-axis coordinates, Y-axis coordinates, and angular offset.

In a second aspect, the present disclosure provides a robot relocation device, which includes:

The map acquisition module is configured to acquire the grid map corresponding to the current area when it is determined that the robot is not located at the origin;

The map down-sampling module is configured to down-sample the raster map based on a preset first depth rule to generate a first multi-resolution raster map;

A position determining module configured to perform an iterative search on the first multi-resolution grid map to determine the first position of the robot in the grid map; and

The position output module is configured to determine and output the first position as the position of the robot relative to the origin.

In a third aspect, the present disclosure provides an electronic device, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

Memory, configured to store computer programs;

The processor is configured to execute the program stored in the memory to implement the robot relocation method described in the present disclosure.

In a fourth aspect, the present disclosure provides a storage medium in which instructions are stored, which when run on a computer, cause the computer to execute the robot relocation method described in the present disclosure.

In a fifth aspect, the present disclosure provides a computer program product containing instructions, which when run on a computer, causes the computer to execute the robot relocation method described in the present disclosure.

In some embodiments, the robot relocation method, device, electronic device, and storage medium of the present disclosure achieve rapid completion of relocation and avoid relocation errors when there are repeated scenes.

In some embodiments, when it is determined that the robot is not located at the origin, the grid map corresponding to the current area is obtained, and the grid map is down-sampled based on the preset first depth rule to generate the first multi-resolution grid map , Perform iterative search on the first multi-resolution grid map, determine the first position of the robot in the grid map, and determine the first position as the position of the robot relative to the origin and output. In this way, the raster map is down-sampled by the preset first depth rule to generate the first multi-resolution raster map, and the first multi-resolution raster map is iteratively searched to determine the first position of the robot in the raster map , You can quickly complete the relocation, and avoid relocation errors when there are repeated scenes.

Brief description of the drawings

The drawings herein are incorporated into the specification and constitute a part of the specification, show embodiments consistent with the disclosure, and are used together with the specification to explain the principle of the disclosure.

In order to explain the technical solutions of the present disclosure more clearly, the following will briefly introduce the accompanying drawings needed for the present disclosure. Obviously, for those of ordinary skill in the art, they can also Other drawings are obtained from these drawings.

Figure 1 shows a schematic diagram of the implementation process of a robot relocation method in an embodiment of the present disclosure;

FIG. 2 shows a schematic diagram of the implementation process of another robot relocation method in an embodiment of the present disclosure;

Fig. 3 shows a schematic diagram of grid map area division in an embodiment of the present disclosure;

FIG. 4 shows a schematic diagram of an implementation process of a robot positioning method in an embodiment of the present disclosure;

Figure 5 shows a schematic structural diagram of a robot relocation device in an embodiment of the present disclosure; and

Fig. 6 shows a schematic structural diagram of an electronic device in an embodiment of the present disclosure.

Detail

In order to make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments They are a part of the embodiments of the present disclosure, but not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present disclosure.

As shown in FIG. 1, it is a schematic diagram of the implementation process of the robot relocation method provided by the embodiments of the present disclosure. The method may include the following steps:

S101, in a case where it is determined that the robot is not located at the origin, obtain a grid map corresponding to the current area;

S102, down-sampling the raster map based on a preset first depth rule to generate a first multi-resolution raster map;

S103: Perform an iterative search on the first multi-resolution grid map to determine the first position of the robot in the grid map; and

S104: Determine that the first position is the position of the robot relative to the origin and output.

In some embodiments, for the robot, if the robot is located at the origin of (coordinates), subsequent positioning processing can be performed on the robot; if the robot is not located at the origin of (coordinates), the robot needs to be repositioned (that is, determining The position of the robot relative to the origin of the coordinate), and then the robot can be subjected to subsequent positioning processing.

Based on the prior information, it can be determined whether the robot is located at the origin of (coordinates). When it is determined that the robot is not at the origin of (coordinates), the robot needs to be repositioned. For this reason, the embodiment of the present disclosure needs to obtain the grid corresponding to the current area. map.

Among them, using laser radar and other equipment to scan the current area can generate a raster map. The current area may be an indoor working area or a designated outdoor area, which is not limited in the embodiment of the present disclosure.

It should be noted that when it is determined that the robot is not located at the origin (coordinates), the robot needs to be repositioned, and the repositioning process can be performed once when the robot is turned on.

In the process of relocating the robot, the embodiment of the present disclosure performs down-sampling on the raster map based on a preset first depth rule to generate a first multi-resolution raster map, where down-sampling refers to reducing The resolution of the raster map.

For the first multi-resolution raster map, it may include the first lowest resolution raster map, the first intermediate resolution raster map, and the first highest resolution raster map. For the first highest resolution raster map, that is The grid map corresponding to the above current area.

It should be noted that the first intermediate resolution raster map includes at least one first intermediate resolution raster map, and raster maps of different resolutions can form a first multi-resolution raster map with a depth of D1 .

In some embodiments, for the raster map corresponding to the current area, assuming that the raster map resolution is 1000*1000, the raster map is down-sampled based on the preset first depth rule, and the resolution can be generated as The first multi-resolution raster map of 1000*1000, 500*500, 250*250;

Among them, the first lowest resolution raster map includes: a raster map with a resolution of 250*250, the first intermediate resolution raster map includes: a raster map with a resolution of 500*500, and the first highest resolution raster map The grid map includes: a grid map with a resolution of 1000*1000. The grid maps of different resolutions can form the first multi-resolution grid map with a depth of D1 (that is, 3).

In some embodiments, for the raster map corresponding to the current area, assuming that the raster map resolution is 10000*10000, the raster map is down-sampled based on the preset first depth rule, and the resolution can be generated as 10000*10000, 5000*5000, 2500*2500, 1250*1250, 625*625 grid map;

Among them, the first lowest resolution raster map includes: a raster map with a resolution of 625*625, and the first intermediate resolution raster map includes: a raster with a resolution of 5000*5000, 2500*2500, 1250*1250 For maps, the first highest resolution raster map includes: a raster map with a resolution of 10000*10000. The raster maps of different resolutions can form the first multi-resolution raster map with a depth of D1 (that is, 5).

It should be noted that the raster map is down-sampled based on the preset first depth rule to generate the first multi-resolution raster map. For details, please refer to the resolution of the raster map corresponding to the current area. The resolution of the grid map is relatively high, and the grid map can be down-sampled in more levels based on the preset first depth rule to generate the first multi-resolution grid map (for example, the above-mentioned resolution of 10000*10000 Processing process of raster map), if the resolution of the raster map is low, the raster map can be down-sampled at a lower level based on the preset first depth rule to generate the first multi-resolution raster map (Such as the above processing process for the raster map with a resolution of 1000*1000).

In some embodiments, for the first multi-resolution raster map, including multiple raster maps of different resolutions, an iterative search is performed on the first multi-resolution raster map to determine the robot's position in the raster map. One location.

Among them, the iterative search from coarse to fine on the first multi-resolution grid map can determine the first position of the robot in the grid map. Wherein, the first position includes X-axis coordinates, Y-axis coordinates, and angular offset.

The angular offset can be understood as the angular offset relative to the preset direction of the robot. For example, suppose that the initial direction of the robot (ie, the preset direction) is true north, and the robot direction is true south during the subsequent movement of the robot, and the angle offset of the robot at this time is 180 degrees.

For the first position of the robot in the grid map, it can be understood as the position of the robot relative to the origin, and the first position can be output. So far, the relocation of the robot is completed.

For the first position of the robot in the grid map, it can be used as the particle with the highest weight in the AMCL to perform filter calculation in the next positioning calculation, so that the loop of the technology chain is realized.

Based on the above description of the technical solutions provided by the embodiments of the present disclosure, when it is determined that the robot is not located at the origin, the grid map corresponding to the current area is obtained, and the grid map is down-sampled based on the preset first depth rule to generate the first Multi-resolution raster map, iterative search is performed on the first multi-resolution raster map, the first position of the robot in the raster map is determined, and the first position is determined as the position of the robot relative to the origin and output. In this way, the raster map is down-sampled by the preset first depth rule to generate the first multi-resolution raster map, and the first multi-resolution raster map is iteratively searched to determine the first position of the robot in the raster map , You can quickly complete the relocation, and avoid relocation errors when there are repeated scenes.

As shown in FIG. 2, it is a schematic diagram of the implementation process of another robot relocation method provided by an embodiment of the present disclosure. The method may include the following steps:

S201, in a case where it is determined that the robot is not located at the origin, obtain a grid map corresponding to the current area;

S202: Down-sampling the raster map based on a preset first depth rule to generate a first multi-resolution raster map;

S203: Determine a first iterative search order of the first multi-resolution raster map based on the resolution, where the iterative order includes an iterative search from low resolution to high resolution;

S204: Perform an iterative search on the first multi-resolution grid map based on the first iterative search order, and determine the first position of the robot in the grid map; and

S205: Determine that the first position is the position of the robot relative to the origin and output.

In the embodiment of the present disclosure, step S201 is similar to the foregoing step S101, which is not limited in the embodiment of the present disclosure.

In the embodiment of the present disclosure, step S202 is similar to the foregoing step S102, which is not limited in the embodiment of the present disclosure.

For the first multi-resolution raster map, it may include the first lowest resolution raster map, the first intermediate resolution raster map, and the first highest resolution raster map. Due to the difference in resolution, it can be determined based on the resolution The first iterative search order of the first multi-resolution raster map, where the first iterative order includes an iterative search from low resolution to high resolution.

In some embodiments, the first multi-resolution raster map may include the first lowest resolution raster map, the first intermediate resolution raster map, and the first highest resolution raster map, the first lowest resolution raster map The rate raster map includes: a raster map with a resolution of 250*250, the first intermediate resolution raster map includes: a raster map with a resolution of 500*500, and the first highest resolution raster map includes: resolution For a 1000*1000 raster map, the first iterative search order of the first multi-resolution raster map is determined based on the resolution, as shown in Table 1 below.

Table 1

第一迭代搜索顺序First iteration search order	不同分辨率的栅格地图Raster maps of different resolutions
11	分辨率为250250的栅格地图Raster map with a resolution of 250250
22	分辨率为500500的栅格地图Raster map with a resolution of 500500
33	分辨率为10001000的栅格地图Raster map with a resolution of 10001000

Based on the above-mentioned first iterative search sequence, the first multi-resolution raster map is searched from coarse to fine, and the first position of the robot in the raster map can be determined, as shown below, so that the robot can be quickly completed reset:

First of all, in the first lowest resolution raster map preset the first search box area (to avoid multiple similar scenes in the environment, if there are no similar scenes, you can perform a real global search) to search, confirm The optimal position of the robot in the first lowest resolution grid map, and the optimal position as the initial position for searching in the first search box area preset in the first intermediate resolution grid map next time;

Secondly, taking the optimal position of the robot in the first lowest resolution grid map as the initial position, search in the first search box area preset in the first intermediate resolution grid map to determine the robot at the first intermediate resolution The optimal position in the raster map, using the optimal position as the initial position for searching in the first search box area preset in the first highest resolution raster map next time;

Furthermore, taking the optimal position of the robot in the first intermediate resolution grid map as the initial position, search in the first search box area preset in the first highest resolution grid map to determine that the robot is at the first highest resolution The first position in the rate grid map (that is, the grid map corresponding to the above-mentioned current area).

It should be noted that each search can calculate the matching score between the current laser point cloud and the current resolution raster map, where the largest matching score in the first search box area is selected and the matching score score is greater than score_th (preset threshold). The corresponding position is the optimal position. One way to calculate the matching score is as follows:

Among them, N represents the number of points contained in the laser point cloud, α represents the weight parameter, which is usually calculated according to the distance of the laser point cloud, and the cell(x) function represents the rental table of x in the current resolution raster map The value, T(x) represents the transformation of point x to the specified coordinate system, and i represents the coordinates of the i-th laser point. It should be noted that in the above expression, i and x are both two-dimensional coordinates.

In some embodiments, in order to reduce the amount of calculation and further accelerate the search, in the embodiment of the present disclosure, based on a priori information, the search area of the robot in the grid map can be determined, so as to search based on the first iteration. In order, an iterative search is performed in the search area in the first multi-resolution grid map to determine the first position of the robot in the grid map.

In some embodiments, the grid map can be divided into four areas A, B, C, and D. As shown in Figure 3, based on the prior information, it can be determined that the robot is in the area A of the grid map, thus Based on the first iterative search order, first search in area A of the first lowest resolution raster map, then search in area A of the first intermediate resolution raster map, and finally search in the first highest resolution raster map. Search in area A of the grid map to determine the first position of the robot in the grid map. Such a pre-determined approximate range of the robot can further accelerate the search and further speed up the completion of the relocation of the robot.

For the first position of the robot in the grid map, in order to improve the accuracy of relocation, the embodiments of the present disclosure may optimize the first position, that is, optimize the first position by using a preset first target optimization algorithm. The first target position is obtained, and the first target position is determined to be the position of the robot relative to the origin and output. Wherein, the first target position includes X-axis coordinates, Y-axis coordinates, and angular offset.

It should be noted that the embodiment of the present disclosure completes the optimization of the first position on the first highest resolution raster map (ie, the raster map corresponding to the current area described above), where the first target optimization algorithm is as follows.

ξ ^* =arg min _ξ [1-cell(S _i (ξ))] ² ;

in:

Represents the state transition function in the current posture (ie position), where ξ represents the combination of rotation and translation, namely (p _x , p _y , ψ). In the optimization process, bilinear interpolation or trilinear interpolation is used to calculate the probability value of the grid at any point when calculating the derivative.

For the first position of the robot in the grid map, after optimization, the optimized result is directly used as the final relocation result and output, which can be used as the highest weighted particle in AMCL to perform filtering calculation during the next positioning calculation to realize the technology The loop of the chain.

As shown in FIG. 4, it is a schematic diagram of the implementation process of the robot positioning method provided by the embodiments of the present disclosure. The method may include the following steps:

S401, when the relocation of the robot is completed, or when it is determined that the robot is located at the origin, obtain the initial position output by the AMCL method;

S402, down-sampling the raster map based on a preset second depth rule to generate a second multi-resolution raster map;

S403: Perform an iterative search on the second multi-resolution grid map based on the initial position, and determine the second position of the robot in the grid map; and

S404: Determine that the second position is the position of the robot relative to the origin and output.

In the embodiments of the present disclosure, if the robot is at the origin, subsequent positioning processing can be performed on the robot, that is, the initial position output by the AMCL method can be obtained, and the result output by the AMCL method can be corrected and optimized, which can improve the positioning accuracy;

or,

After the relocation of the robot is completed, subsequent positioning processing can also be performed on the robot, that is, the initial position output by the AMCL method can be obtained, and the results output by the AMCL method can be corrected and optimized, which can improve the positioning accuracy.

In the process of rectifying and optimizing the output result of the AMCL method, the raster map may be down-sampled based on a preset second depth rule to generate a second multi-resolution raster map.

For the second multi-resolution raster map, it may include the second lowest resolution raster map, the second intermediate resolution raster map, and the second highest resolution raster map. For the second highest resolution raster map, that is The grid map corresponding to the above current area.

For the process of down-sampling the raster map based on the preset second depth rule to generate the second multi-resolution raster map, refer to the above-mentioned relocation process based on the preset first depth rule for the process of generating the second multi-resolution raster map. The process of down-sampling the map to generate the first multi-resolution raster map is not repeated here in the embodiments of the present disclosure.

It should be noted that the second intermediate-resolution raster map includes at least one second intermediate-resolution raster map, and a second multi-resolution raster map with a depth of D2 can be composed of raster maps of different resolutions. , For D2, it is not greater than D1 in the above relocation process.

For the second multi-resolution raster map, it may include the second lowest resolution raster map, the second intermediate resolution raster map, and the second highest resolution raster map. Due to the difference in resolution, it can be determined based on the resolution The second iterative search order of the second multi-resolution raster map, where the second iterative order includes an iterative search from low resolution to high resolution.

In some embodiments, the second multi-resolution raster map may include the second lowest resolution raster map, the second intermediate resolution raster map, and the second highest resolution raster map, the second lowest resolution raster map The rate raster map includes: a raster map with a resolution of 250*250, the second intermediate resolution raster map includes: a raster map with a resolution of 500*500, and the second highest resolution raster map includes: resolution For a 1000*1000 raster map, the second iterative search order of the second multi-resolution raster map is determined based on the resolution, as shown in Table 2 below.

Table 2

第二迭代搜索顺序Second iteration search order	不同分辨率栅格地图Raster maps of different resolutions
11	分辨率为250250的栅格地图Raster map with a resolution of 250250
22	分辨率为500500的栅格地图Raster map with a resolution of 500500
33	分辨率为10001000的栅格地图Raster map with a resolution of 10001000

Based on the initial position and the second iterative search order, perform an iterative search from coarse to fine on the second multi-resolution grid map to determine the second position of the robot in the grid map, As shown below, you can quickly complete the positioning of the robot;

Wherein, the search area of the robot in the grid map can be determined based on the initial position, so that based on the second iterative search order, iterative search is performed on the search area in the second multi-resolution grid map , To determine the second position of the robot in the grid map, the specific processing flow can refer to the above step S204, the embodiment of the present disclosure will not be repeated here.

It should be noted that according to the initial position output by the AMCL method, the search area of the robot in the grid map can be roughly determined. In order to reduce the amount of calculation, the difference between the positioning process in the embodiment of the present disclosure and the above-mentioned relocation process is reflected in the search box The size is different, and the depth of the multi-resolution raster map is different.

For the second position of the robot in the grid map, in order to improve the positioning accuracy, the embodiment of the present disclosure may optimize the second position, that is, optimize the second position by using the preset second objective optimization function to obtain the first position. Two target positions, determining that the second target position is the position of the robot relative to the origin and outputting it. Wherein, the second position and the second target position both include X-axis coordinates, Y-axis coordinates, and angular offset.

It should be noted that the embodiment of the present disclosure completes the optimization of the second location on the second highest resolution raster map (ie, the raster map corresponding to the current area described above), where the second target optimization algorithm is as follows.

ξ ^* =arg min _ξ [α(1-cell(S _i (ξ)))+β(ξ ^* -ξ)] ² ;

Among them, α and β are the weight parameters of the two residuals, and other parameters can refer to the above-mentioned first objective optimization algorithm.

For the second position of the robot in the grid map, after optimization, the optimized result is directly used as the final positioning result and output, which can be inserted into the particle swarm as the particle with the highest weight in the AMCL during the next positioning calculation. Calculate to realize the loop of the technology chain.

The embodiment of the present disclosure also provides a robot relocation device. As shown in FIG. 5, the device may include: a map acquisition module 510, a map down-sampling module 520, a position determination module 530, and a position output module 540.

The map obtaining module 510 is configured to obtain a grid map corresponding to the current area when it is determined that the robot is not located at the origin;

The map down-sampling module 520 is configured to down-sample the raster map based on a preset first depth rule to generate a first multi-resolution raster map;

A position determining module 530, configured to perform an iterative search on the first multi-resolution grid map, and determine the first position of the robot in the grid map;

The position output module 540 is configured to determine and output the first position as the position of the robot relative to the origin.

The embodiment of the present disclosure also provides an electronic device, as shown in FIG. 6, including a processor 61, a communication interface 62, a memory 63, and a communication bus 64. Among them, the processor 61, the communication interface 62, and the memory 63 complete each other through the communication bus 64. Inter-communication,

The memory 63 is configured to store computer programs;

When the processor 61 is configured to execute the program stored in the memory 63, the following steps are implemented:

When it is determined that the robot is not located at the origin, obtain the grid map corresponding to the current area; down-sample the grid map based on the preset first depth rule to generate a first multi-resolution grid map; An iterative search is performed on a multi-resolution grid map to determine the first position of the robot in the grid map; and the first position is determined to be the position of the robot relative to the origin and output.

The communication bus mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into address bus, data bus, control bus and so on. For ease of representation, only one thick line is used to indicate in the figure, but it does not mean that there is only one bus or one type of bus.

The communication interface is used for communication between the above-mentioned electronic device and other devices.

The memory may include random access memory (Random Access Memory, RAM for short), and may also include non-volatile memory (non-volatile memory), such as at least one disk memory. Optionally, the memory may also be at least one storage device located far away from the foregoing processor.

The aforementioned processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP), etc.; it may also be a digital signal processor (Digital Signal Processing, DSP for short) , Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

The present disclosure also provides a storage medium in which instructions are stored, which when run on a computer, cause the computer to execute the robot relocation method described in the present disclosure.

The present disclosure also provides a computer program product containing instructions, which when run on a computer, causes the computer to execute the robot relocation method described in the present disclosure.

In the foregoing embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, it can be implemented in the form of a computer program product in whole or in part. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions described in the embodiments of the present disclosure are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a storage medium, or transmitted from one storage medium to another storage medium. For example, the computer instructions can be transmitted from a website, computer, server, or data center through a wired (such as coaxial cable, optical fiber) , Digital Subscriber Line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) to transmit to another website, computer, server or data center. The storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center integrated with one or more available media. The usable medium may be a magnetic medium (for example, a floppy disk, a hard disk, and a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)).

It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply one of these entities or operations. There is any such actual relationship or order between. Moreover, the terms "including", "including" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device that includes a series of elements includes not only those elements, but also those that are not explicitly listed Other elements of, or also include elements inherent to this process, method, article or equipment. If there are no more restrictions, the element defined by the sentence "including a..." does not exclude the existence of other identical elements in the process, method, article, or equipment that includes the element.

The various embodiments in this specification are described in a related manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, as for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and for related parts, please refer to the part of the description of the method embodiment.

The foregoing descriptions are only part of the embodiments of the present disclosure, and are not used to limit the protection scope of the present disclosure. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present disclosure are all included in the protection scope of the present disclosure.

Claims

The robot relocation method includes:

When it is determined that the robot is not located at the origin, obtain the grid map corresponding to the current area;

Down-sampling the raster map based on a preset first depth rule to generate a first multi-resolution raster map;

Performing an iterative search on the first multi-resolution grid map to determine the first position of the robot in the grid map; and

The first position is determined to be the position of the robot relative to the origin and output.
The method of claim 1, wherein the iterative search on the first multi-resolution grid map to determine the first position of the robot in the grid map comprises:

Determining a first iterative search order of the first multi-resolution raster map based on the resolution, wherein the iterative order includes an iterative search from low resolution to high resolution; and

Based on the first iterative search order, an iterative search is performed on the first multi-resolution grid map to determine the first position of the robot in the grid map.
The method of claim 2, wherein the iterative search is performed on the first multi-resolution raster map based on the first iterative search order to determine the robot's first position in the raster map One location, including:

Determining the search area of the robot in the grid map; and

Based on the first iterative search sequence, an iterative search is performed on the search area in the first multi-resolution grid map to determine the first position of the robot in the grid map.
The method according to any one of claims 1 to 3, wherein the determining and outputting the first position as the position of the robot relative to the origin includes:

Optimizing the first position by using a preset first target optimization algorithm to obtain the first target position; and

Determining that the first target position is the position of the robot relative to the origin and outputting;

Wherein, the first position and the first target position both include X-axis coordinates, Y-axis coordinates, and angular offset.
The method according to any one of claims 1 to 4, wherein the method further comprises:

In the case where the relocation of the robot is completed, or in the case where it is determined that the robot is located at the origin, obtaining the initial position output by the AMCL method;

Down-sampling the raster map based on a preset second depth rule to generate a second multi-resolution raster map;

Performing an iterative search on the second multi-resolution grid map based on the initial position to determine the second position of the robot in the grid map; and

The second position is determined to be the position of the robot relative to the origin and output.
The method of claim 5, wherein the iterative search on the second multi-resolution grid map based on the initial position to determine the second position of the robot in the grid map comprises :

Determining a second iterative search order of the second multi-resolution raster map based on the resolution, wherein the second iterative search order includes an iterative search from low resolution to high resolution; and

Based on the initial position and the second iterative search order, an iterative search is performed on the second multi-resolution grid map to determine the second position of the robot in the grid map.
The method according to claim 5 or 6, wherein the determining the second position as the position of the robot relative to the origin and outputting it comprises:

Optimizing the second position by using a preset second target optimization function to obtain the second target position; and

Determining that the second target position is the position of the robot relative to the origin and outputting;

Wherein, the second position and the second target position both include X-axis coordinates, Y-axis coordinates, and angular offset.
The robot relocation device includes:

The map acquisition module is configured to acquire the grid map corresponding to the current area when it is determined that the robot is not located at the origin;

The map down-sampling module is configured to down-sample the raster map based on a preset first depth rule to generate a first multi-resolution raster map;

A position determining module configured to perform an iterative search on the first multi-resolution grid map to determine the first position of the robot in the grid map; and

The position output module is configured to determine and output the first position as the position of the robot relative to the origin.
An electronic device, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete mutual communication through the communication bus;

The memory is configured to store computer programs; and

The processor is configured to implement the method described in any one of claims 1 to 7 when the processor is configured to execute the program stored in the memory.
A storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the method according to any one of claims 1 to 7.