CN110988795A - Mark-free navigation AGV global initial positioning method integrating WIFI positioning - Google Patents
Mark-free navigation AGV global initial positioning method integrating WIFI positioningDownload PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0257—Hybrid positioning
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0252—Radio frequency fingerprinting
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0278—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves involving statistical or probabilistic considerations
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
- G01S5/16—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using electromagnetic waves other than radio waves
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Abstract
Description
Technical Field
The invention relates to the technical field of autonomous positioning of robots, in particular to a global initial positioning method of a marker-free navigation AGV (automatic guided vehicle) integrating WIFI (wireless fidelity) positioning.
Background
The positioning navigation of the mobile robot means that the position and pose of the robot in a given environment are estimated by using the perception information of the vehicle-mounted sensor on the mobile robot, and based on the position and pose, the mobile robot can realize autonomous navigation walking and accurately reach a target point to execute a task.
At present, the common robot positioning and navigation methods are mainly divided into two types, namely marked methods and natural unmarked methods.
The robot adopts a sensor to detect the marks or signals sent by the marks in the walking process to determine the pose of the robot, and typical marked positioning navigation algorithms comprise positioning methods such as two-dimensional codes, magnetic nails, RFID, UWB and WIFI. The marked method often requires a large number of additional marks to be deployed, increasing the system deployment cost and time.
Therefore, the currently mainstream mobile robot (AGV) unmarked navigation method mainly includes two types, namely laser and vision:
1) the laser positioning method comprises the following steps: the laser positioning firstly establishes a two-dimensional contour map of the robot running environment, and the self position of the robot is obtained by calculating and matching contour features of the measured surrounding environment with the map in the robot walking process.
2) The visual positioning method comprises the following steps: the visual positioning method comprises the steps of firstly establishing a three-dimensional map of a robot running environment, wherein the map may contain visual feature points, geometric features, texture features and other information in a robot running space, acquiring image data information in real time through a camera in the robot walking process, and calculating to obtain the position of the robot through matching with map information.
The unmarked navigation method needs to perform initial positioning operation before the mobile robot starts autonomous positioning navigation walking or when the mobile robot is recovered from a fault, so as to obtain the initial position of the mobile robot. However, the initial positioning problem is one of the technical difficulties of the mobile robot using the unmarked positioning, and the reasons are as follows:
1) whether laser positioning or visual positioning, the distribution of the outline features and the visual marking features in the space is likely to have repeatability (for example, a laser method may encounter two rooms with identical size and shape, for example, a visual positioning may encounter a room with identical decoration style and identical arrangement), that is, if several areas with similar features exist in the environment, the mere dependence on the unmarked positioning method cannot determine in which area the mobile robot is located, and due to the above limitations, the complete error of the initial positioning position is easily caused.
2) The mobile robot is often very big in operation scene, which may reach 10000 square meters, and when initially positioning, the feature matching of the whole map is needed, all possible positions of the whole map are searched, all the features on the map are sequentially matched, the calculated amount is very huge, and tens of seconds may be consumed, so that the system practicability is very poor.
Based on the two reasons, in actual engineering at present, an operator is often required to judge an approximate position of the robot in a map in advance, the robot is input as an initial value of initial positioning, then the mobile robot starts a markerless positioning algorithm to be matched with scene features near the approximate position, and then autonomous navigation walking is started after an accurate initial positioning value is obtained. However, this method has a high precision requirement for the approximate position given by the operator, and if the operator inputs the position incorrectly due to inexperience or carelessness, the initial positioning value calculated by the robot is often completely wrong, and an autonomous navigation walking fault is caused.
On the other hand, some methods for assisting positioning using WIFI exist in the prior art.
For example, chinese patent publication No. CN107144852A, global hierarchical positioning system and method for AGVs, discloses a global hierarchical positioning system and method for AGVs, which is composed of a region positioning system, a local positioning system, a working point positioning system, and an AGV central control system. The area positioning system positions the area where the AGV is located according to the WIFI hotspot which can be connected with the area positioning system and the RSSI energy signal of the route or the ID number of the RSSI energy signal. The absolute position coordinates of the current robot within the local area are then calculated using the local positioning system. The global hierarchical positioning system and the method are a hierarchical system, and the mobile robot must execute the logic of firstly regional positioning and then local positioning in each positioning process in autonomous walking, so that the positioning process is complex.
Chinese patent CN 108381554a, "visual tracking mobile robot based on WIFI assisted positioning", discloses a visual tracking mobile robot based on WIFI assisted positioning, which starts WIFI positioning when a visual positioning target is lost or drifts. In the scheme of the invention, the WIFI positioning algorithm is used as a supplement of the visual positioning algorithm, and the WIFI positioning algorithm and the visual positioning algorithm are used in parallel, namely only one positioning algorithm is used at any time.
Although the above related patent documents all relate to WIFI positioning methods, none of the WIFI positioning is used in the global initial positioning process.
Disclosure of Invention
At present, in the field of unmarked navigation of mobile robots, a global initial positioning method which can be fast and accurate and does not need manual assistance is lacked. Therefore, the invention provides a global initial positioning method of a marker-free navigation AGV (automatic guided vehicle) integrating WIFI positioning, which is characterized in that when global initial positioning is carried out, global position search is carried out by utilizing the distribution condition of WIFI signals deployed in the environment, the approximate position of a robot is further determined, and then feature matching is carried out in a local range near the approximate position so as to generate an accurate initial positioning position.
The method is realized by the following technical scheme:
a global initial positioning method for a marker-free navigation AGV (automatic guided vehicle) integrating WIFI positioning comprises the following steps:
s1, establishing a fusion map, wherein the fusion map comprises environmental characteristic information and WIFI source position distribution information;
s2, carrying out global search positioning on the mobile robot based on the WIFI source position distribution information in the fusion map;
and S3, according to the global search positioning result, the local accurate positioning of the mobile robot is carried out based on the environmental characteristic information in the fusion map.
Optionally, the environment feature information includes contour feature information of a mobile robot running area, and/or visual feature points, geometric features and texture feature information in a robot running space.
Further, step S1 specifically includes:
s101, establishing a basic map for describing the characteristic information of the running environment of the robot by adopting a markerless map building algorithm;
and S102, adding position distribution information of the WIFI source on the basic map to form a complete fusion map.
Further, in step S101:
in a laser positioning mode, a laser SLAM algorithm is adopted as a mapping algorithm;
in the visual positioning mode, an ORB-SLAM algorithm is adopted as a mapping algorithm.
Further, step S2 specifically includes:
s201, the mobile robot searches all WIFI signals in the area, and if the searched WIFI signals are matched with a certain WIFI source contained in the WIFI source position distribution information in the fusion map, the signal intensity of the WIFI signals is recorded;
s202, based on the recorded signal intensity, the mobile robot is initially positioned through a positioning algorithm.
Optionally, the positioning algorithm includes, but is not limited to, a triangulation algorithm, a fingerprint positioning algorithm, or a maximum likelihood estimation algorithm.
Further, step S3 specifically includes:
s301, detecting the surrounding environment by using a self sensor of the mobile robot, and recording all feature sets which can be sensed as Qc;
s302, taking the approximate position p obtained through global searching and positioning in the step S2 as an initial position, and acquiring an environment feature set Qmap in the fusion map corresponding to the initial position p based on the environment feature information in the fusion map;
s303, projecting Qc to the coordinate system of the fusion map to obtain Qcw which is mapped to the coordinate system of the fusion map, comparing the matching degree of the Qcw and the Qmap, and calculating a matching score;
s304, setting a search area based on the initial position p, searching all possible positions in the p _ search range by a preset search step, acquiring the environment feature set in the fusion map corresponding to each position, comparing the environment feature set with Qcw to calculate a matching score, and taking the position with the highest matching score including the initial position p as the accurate positioning position of the current mobile robot.
Without loss of generality, in steps S303 and S304, the matching score is calculated as follows:
in the laser positioning mode, the coincidence degree of the contour in the fusion map and the contour scanned by the current laser radar in the Qcw is judged to be used as a matching score, or the matching score is calculated by using a Normal distribution transformation method (NDT);
in the visual positioning mode, the spatial distance error between the visual characteristic point in the fusion map and the visual characteristic point scanned by the current laser radar is calculated and used as a matching score.
The invention has the following beneficial technical effects or advantages:
1) the initial positioning method provided by the invention does not need to manually give a rough initial position by an operator, so that the positioning navigation fault caused by the error of the operator can be effectively avoided.
2) The rough position of the mobile robot is determined by using the WIFI information in the initial positioning process, the algorithm is extremely high in calculation speed, the rough position is used for performing unmarked local accurate positioning, the problem of calculation complexity in the global feature matching of the traditional initial positioning method can be solved, the problem is simplified into local feature matching, and the time consumed in the whole initial positioning process can be greatly reduced.
3) Due to the uniqueness of all WIFI sources, the approximate position obtained by WIFI global search positioning is not necessarily high in precision, but the reliability of approximate region estimation is extremely high, so that initial positioning errors caused by repeated feature phenomena in the environment in the process of matching the whole image only by using a markerless navigation algorithm can be effectively avoided.
4) The WIFI information is used in the initial positioning process in a fusion mode, and because usually a WIFI network covering the running environment of all robots needs to be deployed when the mobile robot is communicated and interacted with an upper-layer system, no special equipment needs to be additionally laid for WIFI positioning, extra equipment does not need to be added when the method is used, and cost is hardly needed for corresponding system transformation and upgrading.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of the present invention.
Detailed Description
For a further understanding of the invention, reference will now be made to the preferred embodiments of the invention by way of example, and it is to be understood that the description is intended to further illustrate features and advantages of the invention, and not to limit the scope of the claims.
As shown in fig. 1, the overall initial positioning method for AGV with no marker navigation fused with WIFI positioning according to the embodiment of the present invention includes the following steps:
firstly, a fusion map is established.
The building of the fusion map refers to the pre-building of a map for the operation of the robot, the fusion map is a reference system required by the initial positioning operation, and specifically, the position distribution information and the environmental characteristic information of the WIFI source are stored in the map.
The WIFI source position distribution information refers to all WIFI source information distributed in a mobile robot operation area, wherein the WIFI source refers to a WIFI switch, a router, an AP and other devices capable of transmitting and receiving the WIFI information, and the WIFI source information includes names of the devices, space coordinates of device installation, signal names of the WIFI devices and the like.
The environment characteristic information refers to environment information on which the mobile robot depends on the unmarked autonomous positioning navigation, and the types of the characteristics are related to the type of the unmarked navigation algorithm which is actually used. For example, in the laser positioning method, the contour feature of the robot running area is the environment feature information, and in the visual positioning method, the information of the visual feature point, the geometric feature, the texture feature and the like in the robot running space is the environment feature information.
Without loss of generality, the process of building the fusion map in this embodiment is as follows:
step 1, establishing a basic map describing the running environment feature information of the robot by adopting a markerless mapping algorithm, specifically, sequentially adding the environment features to corresponding coordinates of the basic map according to the coordinates of the environment features in space according to a scale.
Generally, in a laser positioning mode, a laser SLAM algorithm is adopted as a mapping algorithm; in the visual positioning mode, an ORB-SLAM algorithm is adopted as a mapping algorithm.
And 2, adding the position distribution information of the WIFI sources on the basic map, specifically, sequentially adding the WIFI sources in the running environment of the mobile robot to corresponding coordinates of the basic map according to the coordinates of the WIFI sources in the space according to a scale, so that a complete fusion map is formed.
Specifically, the corresponding coordinate values of the WIFI sources in the basic map may be obtained by manual measurement, or by automatic shooting and calculation by a mapping robot.
Secondly, global searching and positioning of the mobile robot are carried out based on WIFI source position distribution information in the fusion map, and the specific steps are as follows:
step 1, signal searching and measuring, namely, the mobile robot searches all WIFI signals in an area by using a WIFI network card installed on the mobile robot, and if the searched WIFI signals have the same name as a certain WIFI source marked in a fusion map, the signal intensity of the WIFI signals is recorded. And 2, deducing the position of the mobile robot through a positioning algorithm. Specifically, the signal strength stored in step 1 reflects an approximate distance from the current mobile robot to the WIFI source, and according to the geometric relationship, as long as three WIFI source signals marked in the fusion map can be searched, the position of the current mobile robot can be calculated and obtained by a triangulation method.
It should be noted that, in this step, WIFI positioning methods based on other principles, such as fingerprint positioning algorithm, maximum likelihood estimation algorithm, etc., may also be used, and the number of WIFI sources used for positioning is not limited to 3. Generally, the more WIFI sources are deployed, the better the positioning accuracy is.
Thirdly, according to the global search positioning result, the local accurate positioning of the mobile robot is carried out based on the environmental feature information in the fusion map, and the specific steps are as follows:
step 1, detecting the surrounding environment by using a self sensor of the mobile robot, and recording all feature sets which can be sensed as Qc;
step 2, taking the approximate position p obtained by global searching and positioning in the step S2 as an initial position, and obtaining an environmental feature set Qmap in the fusion map corresponding to the initial position p based on the environmental feature information in the fusion map;
step 3, projecting the Qc to the coordinate system of the fusion map to obtain the Qcw mapped by the Qc, comparing the matching degree of the Qcw and the Qmap, and calculating a matching score;
and 4, setting a search area based on the initial position p, searching all possible positions in the p _ search range by using a preset search step, acquiring the environment feature set in the fusion map corresponding to each position, comparing the environment feature set with Qcw to calculate a matching score, and taking the position with the highest matching score including the initial position p as the accurate positioning position of the current mobile robot.
Without loss of generality, in the laser positioning mode, the matching score can be calculated by judging the overlapping degree of the contour in the fusion map and the contour scanned by the current laser radar in the Qcw as the matching score, or by using a method such as NDT (normal distribution transformation) commonly used by laser and the like; in the visual positioning mode, the spatial distance error between the visual feature point in the fusion map and the visual feature point scanned by the current laser radar can be calculated and used as the matching score.
The above description of the embodiments is only intended to facilitate the understanding of the method of the invention and its core idea. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (8)
1. A global initial positioning method for a marker-free navigation AGV (automatic guided vehicle) integrating WIFI positioning is characterized by comprising the following steps:
s1, establishing a fusion map, wherein the fusion map comprises environmental characteristic information and WIFI source position distribution information;
s2, carrying out global search positioning on the mobile robot based on the WIFI source position distribution information in the fusion map;
and S3, according to the global search positioning result, the local accurate positioning of the mobile robot is carried out based on the environmental characteristic information in the fusion map.
2. The method of claim 1, wherein the environmental characteristic information comprises contour characteristic information of a mobile robot running area and/or visual characteristic points, geometric characteristics and texture characteristic information in a robot running space.
3. The method of claim 1, wherein the step S1 specifically includes:
s101, establishing a basic map for describing the characteristic information of the running environment of the robot by adopting a markerless map building algorithm;
and S102, adding position distribution information of the WIFI source on the basic map to form a complete fusion map.
4. The AGV global initial positioning method with integrated WIFI positioning according to claim 3, wherein in step S101:
in a laser positioning mode, a laser SLAM algorithm is adopted as a mapping algorithm;
in the visual positioning mode, an ORB-SLAM algorithm is adopted as a mapping algorithm.
5. The method of claim 1, wherein the step S2 specifically includes:
s201, the mobile robot searches all WIFI signals in the area, and if the searched WIFI signals are matched with a certain WIFI source contained in the WIFI source position distribution information in the fusion map, the signal intensity of the WIFI signals is recorded;
s202, based on the recorded signal intensity, the mobile robot is initially positioned through a positioning algorithm.
6. The method of claim 5, wherein the localization algorithm includes but is not limited to triangulation, fingerprint localization or maximum likelihood estimation.
7. The AGV global initial positioning method with WIFI positioning integrated as claimed in claim 5 or 6, wherein the step S3 specifically comprises:
s301, detecting the surrounding environment by using a self sensor of the mobile robot, and recording all feature sets which can be sensed as Qc;
s302, taking the approximate position p obtained through global searching and positioning in the step S2 as an initial position, and acquiring an environment feature set Qmap in the fusion map corresponding to the initial position p based on the environment feature information in the fusion map;
s303, projecting Qc to the coordinate system of the fusion map to obtain Qcw which is mapped to the coordinate system of the fusion map, comparing the matching degree of the Qcw and the Qmap, and calculating a matching score;
s304, setting a search area based on the initial position p, searching all possible positions in the p _ search range by a preset search step, acquiring the environment feature set in the fusion map corresponding to each position, comparing the environment feature set with Qcw to calculate a matching score, and taking the position with the highest matching score including the initial position p as the accurate positioning position of the current mobile robot.
8. The method of claim 7, wherein in steps S303 and S304, the matching score is calculated as follows:
in the laser positioning mode, the coincidence degree of the contour in the fusion map and the contour scanned by the current laser radar in the Qcw is judged to be used as a matching score, or the matching score is calculated by using a normal distribution conversion method;
in the visual positioning mode, the spatial distance error between the visual characteristic point in the fusion map and the visual characteristic point scanned by the current laser radar is calculated and used as a matching score.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111578946A (en)* | 2020-05-27 | 2020-08-25 | 杭州蓝芯科技有限公司 | Laser navigation AGV repositioning method and device based on deep learning |
| CN112578333A (en)* | 2020-12-24 | 2021-03-30 | 江苏新冠亿科技有限公司 | Intelligent trolley initial coordinate detection method, intelligent trolley and storage medium |
| CN113618729A (en)* | 2020-05-06 | 2021-11-09 | 恩斯迈电子(深圳)有限公司 | Method for repositioning mobile carrier in SLAM map and mobile carrier |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105466421A (en)* | 2015-12-16 | 2016-04-06 | 东南大学 | Mobile robot autonomous cruise method for reliable WIFI connection |
| CN105547305A (en)* | 2015-12-04 | 2016-05-04 | 北京布科思科技有限公司 | Pose solving method based on wireless positioning and laser map matching |
| CN105792353A (en)* | 2016-03-14 | 2016-07-20 | 中国人民解放军国防科学技术大学 | Crowd-sensing Wi-Fi signal fingerprint-assisted image matching indoor positioning method |
| CN105959919A (en)* | 2016-06-29 | 2016-09-21 | 宁波市由乐讯通讯科技有限公司 | Position acquisition method and system for use in wireless communication |
| CN106291517A (en)* | 2016-08-12 | 2017-01-04 | 苏州大学 | Indoor cloud robot angle positioning method based on position and visual information optimization |
| CN107144852A (en)* | 2017-05-09 | 2017-09-08 | 深圳普智联科机器人技术有限公司 | Towards AGV global Hierarchical Location system and method |
| CN108297115A (en)* | 2018-02-02 | 2018-07-20 | 弗徕威智能机器人科技（上海）有限公司 | A kind of autonomous method for relocating of robot |
- 2020
- 2020-03-03CNCN202010137407.4Apatent/CN110988795A/enactivePending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105547305A (en)* | 2015-12-04 | 2016-05-04 | 北京布科思科技有限公司 | Pose solving method based on wireless positioning and laser map matching |
| CN105466421A (en)* | 2015-12-16 | 2016-04-06 | 东南大学 | Mobile robot autonomous cruise method for reliable WIFI connection |
| CN105792353A (en)* | 2016-03-14 | 2016-07-20 | 中国人民解放军国防科学技术大学 | Crowd-sensing Wi-Fi signal fingerprint-assisted image matching indoor positioning method |
| CN105959919A (en)* | 2016-06-29 | 2016-09-21 | 宁波市由乐讯通讯科技有限公司 | Position acquisition method and system for use in wireless communication |
| CN106291517A (en)* | 2016-08-12 | 2017-01-04 | 苏州大学 | Indoor cloud robot angle positioning method based on position and visual information optimization |
| CN107144852A (en)* | 2017-05-09 | 2017-09-08 | 深圳普智联科机器人技术有限公司 | Towards AGV global Hierarchical Location system and method |
| CN108297115A (en)* | 2018-02-02 | 2018-07-20 | 弗徕威智能机器人科技（上海）有限公司 | A kind of autonomous method for relocating of robot |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113618729A (en)* | 2020-05-06 | 2021-11-09 | 恩斯迈电子(深圳)有限公司 | Method for repositioning mobile carrier in SLAM map and mobile carrier |
| CN111578946A (en)* | 2020-05-27 | 2020-08-25 | 杭州蓝芯科技有限公司 | Laser navigation AGV repositioning method and device based on deep learning |
| CN112578333A (en)* | 2020-12-24 | 2021-03-30 | 江苏新冠亿科技有限公司 | Intelligent trolley initial coordinate detection method, intelligent trolley and storage medium |
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