CN110340877B - Mobile robot, positioning method thereof, and computer-readable storage medium - Google Patents
Mobile robot, positioning method thereof, and computer-readable storage medium Download PDFInfo
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- CN110340877B CN110340877B CN201910623793.5A CN201910623793A CN110340877B CN 110340877 B CN110340877 B CN 110340877B CN 201910623793 A CN201910623793 A CN 201910623793A CN 110340877 B CN110340877 B CN 110340877B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/10—Programme-controlled manipulators characterised by positioning means for manipulator elements
- B25J9/1005—Programme-controlled manipulators characterised by positioning means for manipulator elements comprising adjusting means
- B25J9/1015—Programme-controlled manipulators characterised by positioning means for manipulator elements comprising adjusting means using additional, e.g. microadjustment of the end effector
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1661—Programme controls characterised by programming, planning systems for manipulators characterised by task planning, object-oriented languages
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
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- Mechanical Engineering (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
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Abstract
The invention discloses a positioning method of a mobile robot, which comprises the following steps: determining a first area where the mobile robot is located, and scattering particles in the first area to perform Monte Carlo positioning; and after the positioning fails, scattering particles in a second region where the mobile robot is located to perform Monte Carlo positioning, wherein the area of the first region is smaller than that of the second region, and the number of particles scattered in the first region by the mobile robot is smaller than that of particles scattered in the second region by the mobile robot. The invention also discloses a mobile robot and a computer readable storage medium. The invention improves the positioning efficiency of the mobile robot.
Description
Technical Field
The present invention relates to the field of mobile robot technology, and in particular, to a mobile robot, a positioning method thereof, and a computer-readable storage medium.
Background
When the mobile robot works, the mobile robot needs to be positioned. In the prior art, the positioning technology of the mobile robot mainly uses monte carlo positioning, and the monte carlo positioning is performed by scattering particles. However, when the mobile robot performs monte carlo positioning, the global map scattering particles are generally positioned, and the number of the globally scattered particles is large, so that the number of the particles calculated by the mobile robot is large, the calculation resources consumed by the mobile robot are large, the calculation rate of the mobile robot is low, and the positioning efficiency of the mobile robot is low.
Disclosure of Invention
The invention mainly aims to provide a mobile robot, a positioning method thereof and a computer readable storage medium, aiming at solving the problem of low positioning efficiency of the mobile robot.
In order to achieve the above object, the present invention provides a method for positioning a mobile robot, comprising the steps of:
determining a first area where the mobile robot is located, and scattering particles in the first area to perform Monte Carlo positioning;
and after the positioning fails, scattering particles in a second region where the mobile robot is located to perform Monte Carlo positioning, wherein the area of the first region is smaller than that of the second region, and the number of particles scattered in the first region by the mobile robot is smaller than that of particles scattered in the second region by the mobile robot.
In an embodiment, the method for positioning a mobile robot further includes:
when a mobile robot starts to work, acquiring a memory map of the mobile robot;
determining the current pose of the mobile robot according to the memory map;
and when the current pose is not in the charging pile, executing the step of determining the first area where the mobile robot is located.
In an embodiment, after the step of determining the current pose of the mobile robot according to the memory map, the method further includes:
performing ICP relocation when the current pose is in the charging pile;
and after the positioning fails, executing the step of determining the first area where the mobile robot is located.
In an embodiment, after the step of scattering particles in the first region for local monte carlo localization, the method further includes:
after the positioning is successful, ICP relocation is carried out;
and after the positioning fails, performing the step of scattering particles in a second area where the mobile robot is located to perform Monte Carlo positioning.
In an embodiment, after the step of scattering particles in the second area where the mobile robot is located to perform global monte carlo positioning, the method further includes:
after the positioning is successful, ICP relocation is performed.
In an embodiment, after the positioning is successful, the pose of the mobile robot is determined, so as to determine a walking route according to the pose, and control the mobile robot to move according to the walking route. In an embodiment, after the step of scattering particles in the second area where the mobile robot is located to perform monte carlo positioning, the method further includes:
after the positioning fails, determining whether the second area is a global area;
and when the second region is a global region, taking the pose corresponding to the pose of the particle with the highest score in the second region as the pose of the mobile robot.
In an embodiment, after the step of determining whether the second region is a global region, the method further includes:
and when the second area is not a global area, scattering particles in a third area where the mobile robot is located to perform Monte Carlo positioning, wherein the area of the second area is smaller than that of the third area, and the number of particles scattered in the second area by the mobile robot is smaller than that of particles scattered in the third area by the mobile robot.
In order to achieve the above object, the present invention further provides a mobile robot, which includes a memory, a processor, and a positioning program of the mobile robot stored in the memory and executable on the processor, wherein the positioning program of the mobile robot, when executed by the processor, implements the steps of the positioning method of the mobile robot as described above.
To achieve the above object, the present invention also provides a computer-readable storage medium including a positioning program of a mobile robot, which when executed by a processor, implements the steps of the positioning method of the mobile robot as described above.
The mobile robot determines a first area, particles are scattered in the first area to perform Monte Carlo positioning, if positioning fails, the particles are scattered in a second area to perform Monte Carlo positioning, and because the area of the first area is smaller than that of the second area and the number of the particles scattered in the first area is smaller than that of the particles scattered in the second area, namely, the mobile robot firstly scatters a small number of particles in a small area to perform positioning, and then scatters a large number of particles in a large area to perform positioning after positioning fails, the mobile robot is prevented from directly scattering more particles in a global area to perform positioning during positioning, and the positioning efficiency of the mobile robot is improved.
Drawings
Fig. 1 is a schematic diagram of a hardware architecture of a mobile robot according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a positioning method of a mobile robot according to a first embodiment of the present invention;
FIG. 3 is a flowchart illustrating a positioning method of a mobile robot according to a second embodiment of the present invention;
fig. 4 is a flowchart illustrating a positioning method of a mobile robot according to a third embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows: determining a first area where the mobile robot is located, and scattering particles in the first area to perform Monte Carlo positioning; and after the positioning fails, scattering particles in a second region where the mobile robot is located to perform Monte Carlo positioning, wherein the area of the first region is smaller than that of the second region, and the number of particles scattered in the first region by the mobile robot is smaller than that of particles scattered in the second region by the mobile robot.
Because the mobile robot firstly scatters a small number of particles in a small range for positioning, and then scatters a large number of particles in a large range for positioning after positioning failure, the situation that the mobile robot directly scatters more particles in a global area for positioning during positioning is avoided, and the positioning efficiency of the mobile robot is improved.
As an implementation, the mobile robot may be as shown in fig. 1.
The embodiment of the invention relates to a mobile robot, which comprises: a processor 101, e.g. a CPU, a memory 102, a communication bus 103. Wherein a communication bus 103 is used for enabling the connection communication between these components.
The memory 102 may be a high-speed RAM memory or a non-volatile memory (e.g., a disk memory). As shown in fig. 1, a positioning program of the mobile robot may be included in the memory 103 as a kind of computer storage medium; and the processor 101 may be configured to invoke a positioning program of the mobile robot stored in the memory 102 and perform the following operations:
determining a first area where the mobile robot is located, and scattering particles in the first area to perform Monte Carlo positioning;
and after the positioning fails, scattering particles in a second region where the mobile robot is located to perform Monte Carlo positioning, wherein the area of the first region is smaller than that of the second region, and the number of particles scattered in the first region by the mobile robot is smaller than that of particles scattered in the second region by the mobile robot.
In one embodiment, the processor 101 may be configured to invoke a positioning program for the mobile robot stored in the memory 102 and perform the following operations:
when a mobile robot starts to work, acquiring a memory map of the mobile robot;
determining the current pose of the mobile robot according to the memory map;
and when the current pose is not in the charging pile, executing the step of determining the first area where the mobile robot is located.
In one embodiment, the processor 101 may be configured to invoke a positioning program for the mobile robot stored in the memory 102 and perform the following operations:
performing ICP relocation when the current pose is in the charging pile;
and after the positioning fails, executing the step of determining the first area where the mobile robot is located.
In one embodiment, the processor 101 may be configured to invoke a positioning program for the mobile robot stored in the memory 102 and perform the following operations:
after the positioning is successful, ICP relocation is carried out;
and after the positioning fails, performing the step of scattering particles in a second area where the mobile robot is located to perform Monte Carlo positioning.
In one embodiment, the processor 101 may be configured to invoke a positioning program for the mobile robot stored in the memory 102 and perform the following operations:
after the positioning is successful, ICP relocation is performed.
In one embodiment, the processor 101 may be configured to invoke a positioning program for the mobile robot stored in the memory 102 and perform the following operations:
and after the positioning is successful, determining the position and the posture of the mobile robot, determining a walking route according to the position and the posture, and controlling the mobile robot to move according to the walking route. In one embodiment, the processor 101 may be configured to invoke a positioning program for the mobile robot stored in the memory 102 and perform the following operations:
after the positioning fails, determining whether the second area is a global area;
and when the second region is a global region, taking the pose corresponding to the pose of the particle with the highest score in the second region as the pose of the mobile robot.
In one embodiment, the processor 101 may be configured to invoke a positioning program for the mobile robot stored in the memory 102 and perform the following operations:
and when the second area is not a global area, scattering particles in a third area where the mobile robot is located to perform Monte Carlo positioning, wherein the area of the second area is smaller than that of the third area, and the number of particles scattered in the second area by the mobile robot is smaller than that of particles scattered in the third area by the mobile robot.
According to the scheme, the mobile robot firstly determines the first area, particles are scattered in the first area for Monte Carlo positioning, if positioning fails, the particles are scattered in the second area for Monte Carlo positioning, and the number of the particles scattered in the first area is less than that of the particles scattered in the second area, namely, the mobile robot firstly scatters a small number of particles in a small area for positioning, and then scatters a large number of particles in a large area for positioning after positioning fails, so that the situation that the mobile robot directly scatters a large number of particles in a global area for positioning is avoided, and the positioning efficiency of the mobile robot is improved.
Based on the hardware architecture of the mobile robot, the embodiment of the positioning method of the mobile robot is provided.
Referring to fig. 2, fig. 2 is a first embodiment of a positioning method of a mobile robot according to the present invention, the positioning method of the mobile robot including the steps of:
step S10, determining a first area where the mobile robot is located, and scattering particles in the first area to perform Monte Carlo positioning;
in this embodiment, the execution main body is a mobile robot, and the mobile robot may be a robot capable of moving by itself, such as a sweeping robot. When the mobile robot works, the mobile robot needs to be positioned. In the positioning, the mobile robot determines the first area with its own pose as a base point, for example, the first area may be determined with its own pose as a center pose, and the area of the first area may be 4 × 4, and the unit may be cm, dm or m, that is, 4cm × 4cm, 4dm × 4dm or 4m × 4m, which is not limited thereto. It should be noted that the area of the first region is smaller than the area of the region where the mobile robot is currently located, for example, the region where the mobile robot is located is a bedroom, the area of the region where the mobile robot is currently located is the area of the bedroom, the area of the first region can be flexibly set according to the area of the region where the mobile robot is currently located, that is, it is only necessary to ensure that the area of the first region is smaller than the area of the region where the mobile robot is currently located.
Step S20, after the positioning fails, scattering particles in a second region where the mobile robot is located to perform monte carlo positioning, where an area of the first region is smaller than an area of the second region, and a number of particles scattered in the first region by the mobile robot is smaller than a number of particles scattered in the second region by the mobile robot.
After the mobile robot determines that the positioning has failed, a second area is further determined, which has an area larger than that of the second area, for example, the area of the first area is 4dm × 4dm, and then the area of the second area may be 8dm × 8 dm. The area of the second area is smaller than or equal to the area of the area where the mobile robot is located currently.
After the second area is determined, the mobile robot scatters particles in the second area for Monte Carlo positioning, and the area of the second area is larger than that of the first area, so that the number of particles scattered in the second area by the mobile robot is more than that of particles scattered in the first area by the mobile robot.
After the first area and the second area are determined, the mobile robot determines the obstacles present in the first area and the second area, the first area and the second area may be present in the form of an image, the color of the obstacle in the image corresponding to the first area and the second area is black, the color of the obstacle in the non-obstacle area in the image is white, and the mobile robot scatters particles only in the area having the color of white.
It should be noted that determining whether the monte carlo positioning is successful is common knowledge of those skilled in the art, and is not described herein.
In the technical scheme provided by this embodiment, the mobile robot determines the first area first, particles are scattered in the first area for monte carlo positioning, if positioning fails, particles are scattered in the second area for monte carlo positioning, and since the area of the first area is smaller than that of the second area and the number of particles scattered in the first area is less than that of particles scattered in the second area, that is, the mobile robot firstly scatters a smaller number of particles in a small area for positioning, and then scatters a larger number of particles in a larger area for positioning after positioning fails, it is avoided that the mobile robot directly scatters a larger number of particles in a global area for positioning during positioning, and positioning efficiency of the mobile robot is improved.
Referring to fig. 3, fig. 3 is a second embodiment of the positioning method for a mobile robot according to the present invention, and based on the first embodiment, the positioning method for a mobile robot further includes:
step S30, when the mobile robot starts working, a memory map of the mobile robot is obtained;
step S40, determining the current pose of the mobile robot according to the memory map;
step S50, when the current pose is not in the charging pile, executing the step of determining a first area where the mobile robot is located, and scattering particles in the first area to perform Monte Carlo positioning;
step S60, when the current pose is in the charging pile, ICP relocation is carried out;
step S70, after the positioning fails, executing the step of determining the first area where the mobile robot is located, and scattering particles in the first area to perform monte carlo positioning.
In this embodiment, the mobile robot stores a memory map, and when the mobile robot starts to work, the current pose of the mobile robot can be determined according to the memory map, so as to determine whether the current pose of the mobile robot is a charging seat. It should be noted that the pose refers to a position and a heading.
The mobile robot can adopt ICP relocation to position when in a charging seat. The mobile robot stores an ICP algorithm, the mobile robot carries out ICP relocation through the ICP algorithm, the ICP algorithm calculates the relative pose transformation relation between two frames in a laser point cloud adjacent frame matching mode, and the ICP algorithm only uses adjacent frames, so that the positioning efficiency of ICP relocation is higher than that of Monte Carlo.
And when the positioning fails, the mobile robot determines the first area again to perform Monte Carlo positioning. And when the mobile robot is determined not to be in the charging seat, namely the mobile robot is at a stop point or a pause point after the last work, the mobile robot directly determines the first area so as to carry out Monte Carlo positioning.
It should be noted that determining whether ICP relocation is successful is common knowledge of those skilled in the art, and is not described herein.
In the technical scheme provided by the embodiment, when the mobile robot works, whether the current pose of the mobile robot is the charging seat is determined according to the memory map, if yes, ICP relocation is carried out, and the locating efficiency of the ICP relocation is higher than that of Monte Carlo, so that the locating efficiency of the mobile robot is further improved.
In one embodiment, after the mobile robot scatters particles in the first area to perform monte carlo positioning, if the positioning is successful, ICP relocation needs to be performed to perform secondary confirmation; of course, after the mobile robot scatters particles in the second area to perform monte carlo positioning, if the positioning is successful, ICP relocation is required to be performed for secondary confirmation. It can be understood that after the monte carlo positioning of the mobile robot is successful, ICP relocation is required to perform secondary confirmation, so that the positioning accuracy of the mobile robot is improved.
It should be noted that, when the mobile robot performs monte carlo positioning, the mobile robot is moving, that is, the pose of ICP relocation performed after monte carlo positioning is successful does not coincide with the pose of the charging base, so that the positioning result of ICP relocation performed by the charging base is not necessarily the same as the positioning result of ICP relocation performed after monte carlo positioning is successful, and the positioning result is positioning success or positioning failure.
In an embodiment, after the positioning is successful, that is, after the ICP relocation is successful, the monte carlo positioning corresponding to the first area is successful, or the monte carlo positioning corresponding to the second area is successful, the mobile robot determines the pose, so that the walking route is determined according to the pose, and the mobile robot moves according to the walking route. If the mobile robot is a sweeping robot, the walking route is the sweeping route.
Referring to fig. 4, fig. 4 is a third embodiment of the positioning method for a mobile robot according to the present invention, and based on the first or second embodiment, after step S20, the method further includes:
step S80, after the positioning fails, determining whether the second area is a global area;
and step S90, when the second region is a global region, taking the pose corresponding to the pose of the particle with the highest score in the second region as the pose of the mobile robot.
Step S100, when the second region is not the global region, scattering particles in a third region where the mobile robot is located to perform monte carlo positioning, where an area of the second region is smaller than an area of the third region, and a number of particles scattered in the second region by the mobile robot is smaller than a number of particles scattered in the third region by the mobile robot.
In this embodiment, the mobile robot is provided with a plurality of regions, the areas of the regions are sequentially increased, and the area of the largest region is the area of the region where the mobile robot is currently located. And carrying out Monte Carlo positioning on the first area by the mobile robot, carrying out Monte Carlo positioning on the second area after the positioning fails, and so on until the mobile robot carries out Monte Carlo positioning on the area with the largest area, namely positioning the global map of the mobile robot.
In contrast, after determining that the monte carlo positioning corresponding to the second region fails, the mobile robot determines whether the second region is a global region, that is, whether the second region is a region with the largest area, if not, performs monte carlo positioning of the third region, wherein the area of the third region is larger than that of the second region, and the number of particles scattered by the mobile robot in the third region is greater than that of particles scattered by the mobile robot in the second region. And if the second region is determined to be the global region, taking the pose corresponding to the pose of the particle with the highest score in the second region as the pose of the mobile robot.
In the present embodiment, the second area is for distinguishing from the first area, and the first and second areas do not indicate the number of areas, but it is understood that the first area may be the monte carlo positioning of the third area by the mobile robot or the monte carlo positioning of the fourth area by the mobile robot.
In addition, in the above embodiment, if the positioning is successful and the ICP repositioning is required, if the ICP repositioning is failed, the monte carlo positioning of the next larger area region is performed.
In the technical solution provided in this embodiment, the mobile robot sets a plurality of regions, and after the second region is unsuccessfully positioned, performs monte carlo positioning of a third region having an area larger than that of the second region, thereby ensuring that the mobile robot can be successfully positioned.
The present invention also provides a mobile robot, which includes a memory, a processor, and a positioning program of the mobile robot stored in the memory and executable on the processor, wherein the positioning program of the mobile robot, when executed by the processor, implements the steps of the positioning method of the mobile robot according to the above embodiments.
The present invention also provides a computer-readable storage medium containing a positioning program for a mobile robot, which when executed by a processor implements the steps of the positioning method for a mobile robot as described in the above embodiments.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method for positioning a mobile robot, comprising:
determining a first area where the mobile robot is located, and scattering particles in the first area to perform Monte Carlo positioning;
and after the positioning fails, scattering particles in a second region where the mobile robot is located to perform Monte Carlo positioning, wherein the area of the first region is smaller than that of the second region, and the number of particles scattered in the first region by the mobile robot is smaller than that of particles scattered in the second region by the mobile robot.
2. The method of positioning a mobile robot according to claim 1, further comprising:
when a mobile robot starts to work, acquiring a memory map of the mobile robot;
determining the current pose of the mobile robot according to the memory map;
and when the current pose is not in the charging pile, executing the step of determining the first area where the mobile robot is located.
3. The method of positioning a mobile robot according to claim 2, further comprising, after the step of determining the current pose of the mobile robot from the memory map:
performing ICP relocation when the current pose is in the charging pile;
and after the ICP positioning fails, executing the step of determining the first area where the mobile robot is located, wherein ICP is an iteration near point.
4. The method according to claim 1, wherein the step of scattering particles in the first region for local monte carlo localization further comprises:
after the Monte Carlo positioning is successful, ICP repositioning confirmation is carried out, wherein ICP is an iteration near point;
and after Monte Carlo positioning fails, performing the step of scattering particles in a second area where the mobile robot is located to perform Monte Carlo positioning.
5. The method according to claim 1, wherein the step of scattering particles in a second area where the mobile robot is located to perform monte carlo positioning further comprises:
after the monte carlo location is successful, ICP relocation is performed, where ICP is the iteration near point.
6. The method according to any one of claims 1 to 5, wherein after the positioning is successful, a pose of the mobile robot is determined to determine a walking route based on the pose, and the mobile robot is controlled to move according to the walking route.
7. The method according to any one of claims 1 to 5, wherein the step of scattering particles in a second area where the mobile robot is located to perform Monte Carlo positioning further comprises:
after the positioning fails, determining whether the second area is a global area;
and when the second region is a global region, taking the pose corresponding to the particle with the highest score in the second region as the pose of the mobile robot.
8. The method of claim 7, wherein the step of determining whether the second area is a global area further comprises:
and when the second area is not a global area, scattering particles in a third area where the mobile robot is located to perform Monte Carlo positioning, wherein the area of the second area is smaller than that of the third area, and the number of particles scattered in the second area by the mobile robot is smaller than that of particles scattered in the third area by the mobile robot.
9. A mobile robot, characterized in that the mobile robot comprises a memory, a processor and a positioning program of the mobile robot stored in the memory and executable on the processor, the positioning program of the mobile robot, when executed by the processor, implementing the steps of the positioning method of the mobile robot according to any one of claims 1-8.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a positioning program of a mobile robot, which positioning program, when executed by a processor, carries out the steps of the positioning method of a mobile robot according to any one of claims 1-8.
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| CN201910623793.5A CN110340877B (en) | 2019-07-11 | 2019-07-11 | Mobile robot, positioning method thereof, and computer-readable storage medium |
| PCT/CN2020/099229 WO2021004328A1 (en) | 2019-07-11 | 2020-06-30 | Mobile robot and localization method therefor, and computer-readable storage medium |
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| CN201910623793.5A CN110340877B (en) | 2019-07-11 | 2019-07-11 | Mobile robot, positioning method thereof, and computer-readable storage medium |
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| CN110340877B (en) * | 2019-07-11 | 2021-02-05 | 深圳市杉川机器人有限公司 | Mobile robot, positioning method thereof, and computer-readable storage medium |
| CN112711249B (en) * | 2019-10-24 | 2023-01-03 | 科沃斯商用机器人有限公司 | Robot positioning method and device, intelligent robot and storage medium |
| CN111421548B (en) * | 2020-04-21 | 2021-10-19 | 武汉理工大学 | Mobile robot positioning method and system |
| CN113619423A (en) * | 2021-09-22 | 2021-11-09 | 国网河南省电力公司平顶山供电公司 | Movable intelligent charging stake |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102820238A (en) * | 2011-06-08 | 2012-12-12 | Fei公司 | High accuracy beam placement for local area navigation |
| CN105652871A (en) * | 2016-02-19 | 2016-06-08 | 深圳杉川科技有限公司 | Repositioning method for mobile robot |
| EP3112969A1 (en) * | 2015-07-03 | 2017-01-04 | Korea Advanced Institute of Science and Technology | Method and apparatus for relocation of mobile robot in indoor environment |
| CN109993794A (en) * | 2019-03-29 | 2019-07-09 | 北京猎户星空科技有限公司 | A kind of robot method for relocating, device, control equipment and storage medium |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100601946B1 (en) * | 2004-03-26 | 2006-07-14 | 삼성전자주식회사 | Method of global localization for robot |
| CN101493943B (en) * | 2008-01-25 | 2011-03-23 | 中国科学院计算技术研究所 | Particle filtering tracking method and tracking device |
| CN104180799A (en) * | 2014-07-15 | 2014-12-03 | 东北大学 | Robot localization method based on self-adaptive Monte Carlo localization method |
| CN107071735A (en) * | 2017-04-24 | 2017-08-18 | 兰州交通大学 | A kind of mobile node Monte Carlo localization method based on sequential |
| CN110340877B (en) * | 2019-07-11 | 2021-02-05 | 深圳市杉川机器人有限公司 | Mobile robot, positioning method thereof, and computer-readable storage medium |
-
2019
- 2019-07-11 CN CN201910623793.5A patent/CN110340877B/en active Active
-
2020
- 2020-06-30 WO PCT/CN2020/099229 patent/WO2021004328A1/en active Application Filing
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102820238A (en) * | 2011-06-08 | 2012-12-12 | Fei公司 | High accuracy beam placement for local area navigation |
| EP3112969A1 (en) * | 2015-07-03 | 2017-01-04 | Korea Advanced Institute of Science and Technology | Method and apparatus for relocation of mobile robot in indoor environment |
| CN105652871A (en) * | 2016-02-19 | 2016-06-08 | 深圳杉川科技有限公司 | Repositioning method for mobile robot |
| CN109993794A (en) * | 2019-03-29 | 2019-07-09 | 北京猎户星空科技有限公司 | A kind of robot method for relocating, device, control equipment and storage medium |
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