CN113108798A - Multi-storage robot indoor map positioning system based on laser radar - Google Patents

Multi-storage robot indoor map positioning system based on laser radar Download PDF

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CN113108798A
CN113108798A CN202110430352.0A CN202110430352A CN113108798A CN 113108798 A CN113108798 A CN 113108798A CN 202110430352 A CN202110430352 A CN 202110430352A CN 113108798 A CN113108798 A CN 113108798A
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warehouse
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高扬华
楼卫东
陆海良
单宇翔
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China Tobacco Zhejiang Industrial Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
    • 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
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
    • G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas

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Abstract

The invention discloses a laser radar-based indoor map positioning system of a multi-storage robot, which comprises: the warehousing robot obtains an actual pose and an error pose according to warehouse signposts arranged in a warehouse, a robot kinematic equation is adopted to obtain a linear velocity control law and an angular velocity control law of the warehousing robot, after self pose adjustment is carried out, laser scanning is carried out to obtain a laser data point set, a local grid map under the current pose is constructed according to the laser data point set and is uploaded to the warehousing system; the warehousing system is used for receiving the local grid maps uploaded by the warehousing robots, and fusing the local grid maps constructed by the warehousing robots to obtain and store a global grid map. The indoor mapping positioning system disclosed by the invention realizes high-efficiency and accurate synchronous mapping and positioning in a large-area environment.

Description

Multi-storage robot indoor map positioning system based on laser radar
Technical Field
The invention belongs to the field of warehouse map building, and particularly relates to a laser radar-based indoor map positioning system for a multi-warehouse robot.
Background
The core technology of a mobile robot working in an unknown environment is synchronous positioning and Mapping (SLAM) of the mobile robot. With the rapid development of the robot technology, researchers provide a plurality of excellent SLAM algorithms, but most of the algorithms are concentrated on a single robot, and a single robot is improved in the aspects of capability, robustness, reliability, efficiency and the like, however, when the robots are used for complex tasks which need efficient and parallel completion, the single robot is difficult to perform, and when the robots work under the condition of large environmental scale or complex environment, the single robot cannot stably realize SLAM.
When the robot is oriented to a large-scale environment, the following problems exist in the single robot: firstly, the single robot is low in mapping efficiency and hard to perform with some real-time requirements; secondly, due to error accumulation, the single robot has lower drawing building accuracy; thirdly, the fault tolerance and robustness of single robot mapping are poor. In view of the shortcomings of single robot SLAM, it is desirable to compensate for this by coordinating cooperation between multiple robots. The cooperative multi-robot has wide application prospect in various fields such as production and life.
Thus, multi-robot SLAM inoculation occurred. However, although multiple-robot SLAM can effectively solve the problem of a single robot, the literature (Thrun S, Burgard W, Fox D.A real-time algorithm for mobile robot mapping with applications to multi-robot and 3Dm mapping in Proceedings of the IEEE Integrated Conference reference and Automation. san Francisco, USA, 2000.321-328) reports the incremental approach to solve the problem of multiple-robot SLAM, the literature (Howard A. Multi-robot localization registration details, in Proceedings of robot Science and Systems, cache, USA,2005.201
208) The problem of multi-robot SLAM based on the particle filter plate method is reported, the two methods only directly popularize the SLAM method of a single robot into the multi-robot SLAM method, and the advantage of multi-robot cooperation map building is not fully utilized.
Multi-robot collaboration also faces challenges not present in single robots. Firstly, the robots do not know the initial relative position relationship of the robots, so that the contact between the robots cannot be directly established, and further, a proper strategy cannot be predicted to fuse a plurality of robot maps into a map with integrity and continuity. Secondly, the positioning of each robot has accumulated errors, and the accumulated errors are overlapped after map fusion, so that errors of the fused map are caused, and how to eliminate the influence of the errors is also a difficulty. Therefore, the problems generated in the multi-robot research have provided a serious test for researchers to research the multi-robot.
Disclosure of Invention
The invention provides a laser radar-based indoor map positioning system for a multi-storage robot, which can form a multi-robot collaborative map building and realize high-efficiency and accurate synchronous map building and positioning in a large-area environment.
A multi-storage robot indoor map positioning system based on laser radar comprises:
the warehousing robot obtains an actual pose according to warehouse signposts arranged in a warehouse, obtains an error pose based on the actual pose and a reference pose, obtains a linear velocity control law and an angular velocity control law of the warehousing robot based on the error pose by adopting a robot kinematic equation, adjusts the self pose through the linear velocity control law and the angular velocity control law of the warehousing robot, performs laser scanning to obtain a laser data point set, constructs a local grid map under the current pose according to the laser data point set, and uploads the local grid map to a warehousing system;
the warehousing system is used for receiving the local grid maps uploaded by the warehousing robots, and fusing the local grid maps constructed by the warehousing robots to obtain and store a global grid map.
The warehouse robot realizes the repositioning of the robot according to warehouse road signs arranged in a warehouse, and reduces the motion accumulated error of the robot through the comparison calculation of an actual pose and a reference pose, thereby realizing accurate synchronous drawing and positioning; the warehousing system fuses the local grid maps with the warehouse landmark indexes, and synchronous drawing and positioning of the warehousing robots in a large-area environment are efficiently realized.
The method for obtaining the linear velocity control law and the angular velocity control law of the warehousing robot by adopting the robot kinematics equation comprises the following specific steps:
actual pose [ x ] based on warehousing robotc,yc,θc]TAnd a reference pose [ x ]r,yr,θr]TObtaining the pose error e ═ x of the warehousing robote,ye,θe]TAs shown in the following formula:
Figure BDA0003031228820000021
wherein x and y are the positions of the warehousing robots, theta is the attitude angle of the warehousing robots, the linear velocity of the warehousing robots is obtained by adopting a robot kinematic equation based on the pose errors of the warehousing robots, and the differential equation of the angular velocity and the pose errors of the warehousing robots is as follows: and the pose error is ensured to be continuously close to zero:
Figure BDA0003031228820000031
wherein v is the linear velocity of the storage robot, omega is the angular velocity of the storage robot, vrFor the speed of the warehousing robot reference, omegarFor the reference angular velocity of the warehousing robot,
Figure BDA0003031228820000032
derivative the differential of the pose error so that the error vector e is (x)e,ye,θe)TAnd the whole pose error value is gradually converged to 0 along with the change of time by adjusting the linear velocity control law and the angular velocity control law of the storage robot.
And obtaining the linear velocity and the angular velocity of the storage robot through a linear velocity control law and an angular velocity control law of the storage robot, and adjusting the self pose according to the linear velocity and the angular velocity of the storage robot.
The specific steps of obtaining and storing the global grid map by fusing the local grid maps constructed by the warehousing robots are as follows: the storage system determines the positions of the local grid maps uploaded by the storage robots in the warehouse based on warehouse signposts, splices the parts, which are not overlapped, of the local grid maps uploaded by the storage robots, fuses the overlapped parts of the local grid maps uploaded by the storage robots, and obtains and stores the global grid maps according to the splicing and fusing results.
The storage robot continuously adjusts the self pose according to the warehouse road signs arranged in the warehouse, reduces the positioning error of the storage robot, reduces the deviation of the storage system in the process of fusing the grid map, and accurately constructs the global grid map.
The warehousing robot comprises a laser radar, a wireless radio frequency reader, a driving module, an anti-collision module, a communication module, a power supply system and a core processing module;
the laser radar is used for laser scanning an indoor environment to obtain a laser data point set;
and the wireless radio frequency reader is used for identifying warehouse road signs, acquiring the actual pose of the warehousing robot, and assisting the laser radar to reposition the warehousing robot.
The laser radar is located at the top of the storage robot, so that the laser radar can perform 0-360-degree shielding-free rotary scanning.
The warehouse road signs are distributed at different positions of the warehouse and are movable magnetic stripe tags or buried electromagnetic tags.
The density of the whole set of warehouse road signs in the warehouse determines the mapping time and accuracy of the whole system, if the labels are too dense, pose recalculation needs to be frequently carried out, and if the labels are too sparse, blind areas in mapping fusion can exist.
The communication module is a multicast mode of the ZigBee communication module, and multi-node real-time communication between each robot and the storage system is realized. And the timeliness and the accuracy of information interaction of the robot in the process of map building comparison and fusion are ensured.
The warehousing robot adopts a wireless radio frequency reader to identify warehouse signposts to obtain position tag information, and is used for marking the positions of the local grid maps in the warehouse when the warehousing robot constructs the local grid maps.
Compared with the prior art, the invention has the beneficial effects that:
(1) the warehousing robot adjusts the self pose according to warehouse road signs arranged in a warehouse and relocates, so that the motion accumulated error of the robot is reduced, and the warehousing system is ensured to accurately construct a global grid map.
(2) The warehouse signposts distributed in the warehouse are used as indexes of the local grid map, so that the multi-robot cooperative mapping is realized, the problems of low efficiency, small task amount and weak system robustness of a single robot are solved, and the high-efficiency synchronous mapping and positioning under the large-area environment are realized.
Drawings
FIG. 1 is a diagram of a positioning system for an indoor map of a laser radar-based multi-storage robot according to an embodiment of the present invention;
FIG. 2 is a flow chart of a multi-robot warehouse map building and positioning system based on a 2D laser radar according to an embodiment of the present invention;
fig. 3 is a schematic diagram of error calculation of a multi-robot warehouse mapping and positioning system based on a 2D laser radar according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
On the contrary, the invention is intended to cover alternatives, modifications, equivalents and alternatives which may be included within the spirit and scope of the invention as defined by the appended claims. Furthermore, in the following detailed description of the present invention, certain specific details are set forth in order to provide a better understanding of the present invention. It will be apparent to one skilled in the art that the present invention may be practiced without these specific details.
As shown in fig. 1, a diagram of a positioning system for an indoor map of a multi-warehouse robot based on a laser radar comprises a warehouse machine with a 2D laser radar, and a warehouse system connected with the warehouse robot through a communication module, wherein the warehouse robot comprises the 2D laser radar, a Radio Frequency Identification (RFID) reader, a driving module, a collision prevention module, a communication module, a power supply system and a core processing module;
the 2D laser radar is used for laser scanning an indoor environment to obtain a laser data point set;
and the RFID reader is used for identifying warehouse road signs, obtaining the actual pose of the warehousing robot and assisting the 2D laser radar so as to reposition the warehousing robot.
The 2D laser radar is located at the top of the storage robot, and the 2D laser radar can perform 0-360-degree non-shielding rotary scanning.
The warehouse road signs are distributed at different positions of the warehouse and are movable magnetic stripe tags or buried electromagnetic tags.
The communication module is a multicast mode of the ZigBee communication module, and multi-node real-time communication between each robot and the storage system is realized. And the timeliness and the accuracy of information interaction of the robot in the process of map building comparison and fusion are ensured.
The warehousing robot adopts the RFID reader to identify warehouse signposts to obtain position tag information, and is used for marking the positions of the local grid maps in the warehouse when the warehousing robot constructs the local grid maps.
A flow chart of a positioning system for indoor map of multi-storage robot based on lidar, as shown in fig. 2, includes:
s1: the warehousing robot obtains an actual pose according to warehouse landmarks arranged in a warehouse, and obtains an error pose based on the actual pose and a reference pose;
s2: based on the error pose, a linear velocity control law and an angular velocity control law of the storage robot are obtained by adopting a robot kinematics equation;
s3, adjusting the self pose through the linear velocity control law and the angular velocity control law of the warehousing robot, and then carrying out laser scanning to obtain a laser data point set;
s4, constructing a local grid map under the current pose according to the laser data point set and uploading the local grid map to a warehousing system;
s5: the warehousing system is used for receiving the local grid maps uploaded by the warehousing robots, and fusing the local grid maps constructed by the warehousing robots to obtain and store a global grid map.
The schematic diagram of the linear velocity control law and the angular velocity control law of the warehousing robot obtained by adopting the robot kinematics equation is shown in fig. 3, and the specific steps are as follows:
actual pose [ x ] based on warehousing robotc,yc,θc]TAnd a reference pose [ x ]r,yr,θr]TObtaining the pose error e ═ x of the warehousing robote,ye,θe]TAs shown in formula (1), formula (1) is developed into formulas (2), (3) and (4):
Figure BDA0003031228820000061
xe＝cosθ(xr-xc)+sinθ(yr-yc) (2)
ye＝-sinθ(xr-xc)+cosθ(yr-yc) (3)
θe＝θr-θc (4)
carrying out differential derivation according to a pose error equation, finding out the relation between the error and the linear velocity and the angular velocity, obtaining the most appropriate control law by a method of calculating the minimum value of the error, and carrying out differential derivation on the error value X in the X-axis directioneDerivation is performed to obtain formula (5):
Figure BDA0003031228820000062
wherein,
Figure BDA0003031228820000063
as a pose error x on the abscissaeThe derivative of (a) of (b),
Figure BDA0003031228820000064
being the derivative of the attitude angle theta of the robot,
Figure BDA0003031228820000065
as a reference pose x on the abscissarThe derivative of (a) of (b),
Figure BDA0003031228820000066
as the actual pose x on the abscissacThe derivative of (a) of (b),
Figure BDA0003031228820000067
as a reference pose y on the ordinaterThe derivative of (a) of (b),
Figure BDA0003031228820000068
as a reference pose y on the ordinatecAccording to the robot dynamics equation (6), the error value Y on the Y-axis is calculatedeSubstituting into formula (5) to obtain formula (7):
Figure BDA0003031228820000069
Figure BDA00030312288200000610
wherein x, y are the position of the robot, and theta is the attitude angle of the robot; v, ω are the linear and angular velocities of the reference robot. Using the same method for the error value Y in the Y-axis directioneThe derivation is carried out to obtain:
Figure BDA00030312288200000611
for the angle error value thetaeThe derivation is carried out to obtain:
Figure BDA00030312288200000612
after the above formula is arranged, the differential equation of the attitude error of the storage robot can be obtained as follows:
Figure BDA0003031228820000071
and the relation among the linear velocity, the angular velocity and the pose error change is obtained through establishing an error differential equation. The objective of trajectory tracking is to find the appropriate control laws v and ω such that the error vector e is (x)e,ye,θe)TThere is a bound and the entire pose error value gradually converges to 0 over time.
And obtaining the linear velocity and the angular velocity of the storage robot through a linear velocity control law and an angular velocity control law of the storage robot, and adjusting the self pose according to the linear velocity and the angular velocity of the storage robot.
The specific steps of obtaining and storing the global grid map by fusing the local grid maps constructed by the warehousing robots are as follows:
s51: the storage system determines the positions of local grid maps uploaded by the storage robots in the warehouse based on warehouse signposts;
s52: splicing the parts, which are not overlapped, of the local grid maps uploaded by the storage robots, and fusing the overlapped parts of the local grid maps uploaded by the storage robots;
s53: and obtaining and storing the global grid map according to the splicing and fusion result.

Claims (8)

1. A multi-storage robot indoor map positioning system based on laser radar comprises a plurality of storage robots with laser radar and a storage system in communication connection with the storage robots, and is characterized by comprising:
the warehousing robot obtains an actual pose according to warehouse signposts arranged in a warehouse, obtains an error pose based on the actual pose and a reference pose, obtains a linear velocity control law and an angular velocity control law of the warehousing robot based on the error pose by adopting a robot kinematic equation, adjusts the self pose through the linear velocity control law and the angular velocity control law of the warehousing robot, performs laser scanning to obtain a laser data point set, constructs a local grid map under the current pose according to the laser data point set, and uploads the local grid map to a warehousing system;
the warehousing system is used for receiving the local grid maps uploaded by the warehousing robots, and fusing the local grid maps constructed by the warehousing robots to obtain and store a global grid map.
2. The lidar-based multi-warehouse robot indoor map positioning system as claimed in claim 1, wherein the specific steps of obtaining the linear velocity control law and the angular velocity control law of the warehouse robot by using the robot kinematics equation are as follows:
actual pose [ x ] based on warehousing robotc,yc,θc]TAnd a reference pose [ x ]r,yr,θr]TObtaining the pose error e ═ x of the warehousing robote,ye,θe]TAs shown in the following formula:
Figure FDA0003031228810000011
wherein x and y are the positions of the warehousing robots, theta is the attitude angle of the warehousing robots, the linear velocity of the warehousing robots is obtained by adopting a robot kinematic equation based on the pose errors of the warehousing robots, and the differential equation of the angular velocity and the pose errors of the warehousing robots is as follows:
Figure FDA0003031228810000012
wherein v is the linear velocity of the storage robot, omega is the angular velocity of the storage robot, vrFor the speed of the warehousing robot reference, omegarFor the reference angular velocity of the warehousing robot,
Figure FDA0003031228810000013
derivative the differential of the pose error so that the error vector e is (x)e,ye,θe)TAnd the whole pose error value is gradually converged to 0 along with the change of time by adjusting the linear velocity control law and the angular velocity control law of the storage robot.
3. The indoor map positioning system based on the lidar for the multi-warehouse robot as claimed in claim 1, wherein the specific steps of obtaining and storing a global grid map by fusing local grid maps constructed by each warehouse robot are as follows: the warehousing system determines the positions of local grid maps uploaded by the warehousing robots in the warehouse based on warehouse signposts, splices the parts, which are not overlapped, of the local grid maps uploaded by the warehousing robots, fuses the overlapped parts of the local grid maps uploaded by the warehousing robots, and obtains and stores a global grid map according to the splicing and fusing results.
4. The indoor map positioning system based on the lidar for the multi-warehouse robot of claim 1, wherein the warehouse robot comprises a lidar, a wireless radio frequency reader, a driving module, a collision prevention module, a communication module, a power supply system and a core processing module;
the laser radar is used for laser scanning an indoor environment to obtain a laser data point set;
and the wireless radio frequency reader is used for identifying warehouse road signs, acquiring the actual pose of the warehousing robot, and assisting the laser radar to reposition the warehousing robot.
5. The lidar based multiple storage robot indoor map positioning system according to claim 1 or 4, wherein the lidar is located at the top of the storage robot to ensure that the lidar can scan in a 0-360 ° unobstructed rotation.
6. The lidar based multiple-storage-robot indoor map positioning system according to claim 1 or 4, wherein the warehouse road signs are distributed at different positions of the warehouse, and the warehouse road signs are mobile magnetic stripe tags or buried electromagnetic tags.
7. The lidar-based multi-warehouse robot indoor map positioning system according to claim 4, wherein the communication module is a multicast mode of a ZigBee communication module, and multi-node real-time communication between each robot and a warehousing system is realized.
8. The lidar-based multi-bin robot indoor map positioning system according to claim 4, wherein the warehousing robot adopts a radio frequency reader to identify warehouse road signs to obtain position tag information for marking the position of the local grid map in the warehouse when the warehousing robot constructs the local grid map.
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Application publication date: 20210713