CN115129070B - Intelligent obstacle avoidance system and method for storage robot under Internet of things - Google Patents
Intelligent obstacle avoidance system and method for storage robot under Internet of things Download PDFInfo
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Abstract
The invention discloses an intelligent obstacle avoidance system and method for a storage robot under the condition of Internet of things, which relate to the technical field of robots and comprise an acquisition control module, a plane model establishment module and an obstacle avoidance analysis module; the acquisition control module is used for acquiring data and controlling a system; the plane model building module is used for building a plane model of the warehousing robot and the goods; the obstacle avoidance analysis module is used for carrying out plane analysis on goods, acquiring obstacle avoidance paths, detecting the distance of objects around the storage robot through the distance measurement unit, acquiring image data of the goods through the image acquisition unit, analyzing the image data, and fully considering the shape of the goods and the placement form of the goods on the storage robot, so that when the storage robot avoids obstacles, accidents caused by collision among the goods are avoided, and the obstacle avoidance of the storage robot is more intelligent.
Description
Technical Field
The invention relates to the technical field of robots, in particular to an intelligent obstacle avoidance system and method for a storage robot under the Internet of things.
Background
The warehousing robot is an intelligent robot for transporting goods in a warehouse, the pressure on goods transportation in the warehousing field is greatly reduced due to the fact that the warehousing robot is invented, unmanned management of the warehouse is achieved, and the warehouse storage gradually moves to high-end and intelligent storage;
in the prior art, in order to avoid collision among warehousing robots for goods transportation, a transportation path gridding mode is generally adopted for goods transportation, but the transportation distance of the robots is increased invisibly by the mode, the efficiency of goods transportation is reduced, and if an unordered transportation path is realized, collision among the warehousing robots is only ensured to be avoided during avoidance, and if the width of the transported goods is greater than the width of the warehousing robots, collision among the transported goods can be caused, so that normal goods transportation is influenced;
therefore, people urgently need an intelligent obstacle avoidance system and method for a storage robot under the internet of things to solve the technical problems.
Disclosure of Invention
The invention aims to provide an intelligent obstacle avoidance system and method for a storage robot under the Internet of things, and aims to solve the problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme: an intelligent obstacle avoidance method for a storage robot under the Internet of things comprises the following steps:
s1, measuring the distance of objects around the warehousing robot by using a distance measuring unit, and sending a measuring result to a central control unit;
s2, selectively starting an image acquisition unit by using a central control unit according to the measurement data to acquire image data;
s3, extracting the contour of the acquired image data by using a plane model building module, building a plane rectangular coordinate system, and giving a coordinate value to each point on the contour;
s4, analyzing an obstacle avoidance path of the storage robot by using an obstacle avoidance analysis module, and sending an analysis result to a central control unit;
and S5, analyzing the obstacle avoidance mode by using the central control unit, sending the final obstacle avoidance path to the instruction sending unit, and sending an obstacle avoidance instruction to the warehousing robot by using the instruction sending unit.
According to the technical scheme, in S1-S2, the number of the distance measuring units is n, the distance measuring units are installed on the warehousing robot, the measured value of the distance measuring units is Li, wherein i represents the ith distance measuring unit on the warehousing robot, the central control unit is installed at the master control center of the warehousing robot, and the n distance measuring units send the collected distance data Qj = { L1, L2, L3, · Ln } to the central control unit, wherein Qj represents the set of the distance data collected by the n distance measuring units on the warehousing robot with the number j;
when at least one distance data of Qj = { L1, L2, L3,.. And Ln } is smaller than or equal to a set threshold value L, the central control unit starts an image acquisition unit to acquire image data around the warehousing robot, the image acquisition unit is installed on the warehousing robot, and the image acquisition unit sends the acquired image data to the contour extraction unit.
According to the technical scheme, the plane model building module comprises a contour extraction unit, an origin positioning unit and a coordinate value endowing unit of a coordinate system building unit;
the outline extraction unit adopts a Sobel operator to extract the outline of the image data acquired by the image acquisition unit, an origin locating point is arranged on a central symmetrical line of the warehousing robot, the origin locating unit searches for the origin locating point on the outline image extracted by the outline extraction unit, a plane rectangular coordinate system is established by using the origin locating point as the origin through the coordinate system establishment unit, and a coordinate value (Xk, yk) is given to each point in the coordinate system through the coordinate value giving unit, wherein k represents the kth coordinate point, and a set P = { (X1, Y1), (X2, Y2), (X3, Y3),. }, (Xm, ym) } of the coordinate points is formed, wherein m represents m coordinate points.
According to the technical scheme, the S4 specifically comprises the following steps:
the method comprises the following steps: analyzing the shape of the goods;
when Xk is larger than 0, dividing the coordinate point into a first quadrant of a plane rectangular coordinate system;
when Xk is less than 0, dividing the coordinate point into a second quadrant of the plane rectangular coordinate system;
calculating the coordinate points in the first quadrant and the second quadrant according to the following formulas;
wherein k represents the kth coordinate point, and t represents the tth coordinate point;
when in use
When the quantity of the goods is larger than a set threshold value, judging that the shape of the goods is a rectangle;
when in use
When the quantity of the goods is less than or equal to the set threshold value, judging that the shape of the goods is an irregular shape;
step two: determining a maximum width of the cargo;
the width of the cargo is calculated according to the following formula:
S=Xi-Xj；
where Xi represents the abscissa of the coordinate point in the first quadrant, xj represents the abscissa of the coordinate point in the second quadrant, yi = Yj;
extracting a maximum value Smax as the maximum width of the goods;
step three: judging whether the goods are placed on the warehousing robot in a centrosymmetric manner;
respectively extracting coordinate points with equal ordinate values in the first quadrant and the second quadrant, and forming a set Y = { X1, X2, X3,. Once, xa } by the maximum value of the abscissa absolute values of the coordinate points with equal ordinate values, wherein a represents a coordinate points with equal abscissa absolute values;
when Xe = -Xf in the set Y, judging that the goods are placed on the warehousing robot in a centrosymmetric placement, wherein Xe represents the abscissa of a coordinate point located in a first quadrant, and Xf represents the abscissa of a coordinate point located in a second quadrant;
the cargo width is Xe-Xf; then the obstacle avoidance width of the storage robot is as follows:
（Xe-Xf）/2；
when Xe in the set Y is not equal to Xf, judging that the goods are placed on the warehousing robot in a non-centrosymmetric mode, wherein Xe represents the abscissa of a coordinate point located in a first quadrant, and Xf represents the abscissa of a coordinate point located in a second quadrant;
the cargo width is Xe-Xf; then the obstacle avoidance width of the storage robot is as follows:
xe or Xf;
step four: selecting an obstacle avoidance path;
the distance measuring unit respectively detects obstacles in other directions of the warehousing robot A and the warehousing robot B;
if the distance between the obstacles in at least two directions of one storage robot is smaller than or equal to a set threshold value, the storage robot does not avoid the obstacles, and the other storage robot avoids the obstacles;
when the goods on the two warehousing robots are both placed in central symmetry, the avoiding distance is [ (Xe-Xf)/2 ]. X2;
when the goods on the two storage robots are placed in a non-centrosymmetric manner, the avoiding distance is Xe + Xe or Xe-Xf or | Xf | + | Xf |;
if the distances between the obstacles in at least two directions of the two warehousing robots are smaller than or equal to a set threshold value, the two warehousing robots stop waiting;
and the obstacle avoidance analysis module sends an analysis result to the central control unit.
According to the technical scheme, in S5, the central control unit integrates analysis results of all the storage robots in the warehouse, judges the analysis results of the storage robots needing to avoid the obstacle, selects the obstacle avoiding path with the minimum obstacle avoiding distance, sends the final obstacle avoiding instruction to the corresponding storage robot through the instruction sending unit, and the storage robot executes the obstacle avoiding instruction of the central control unit to avoid the obstacle.
The utility model provides a barrier system is kept away to storage robot intelligence under thing networking which characterized in that: the obstacle avoidance system comprises an acquisition control module, a wireless connection module, a plane model establishing module and an obstacle avoidance analysis module;
the acquisition control module is used for acquiring data and controlling a system; the wireless connection module is used for sending and receiving data, and the plane model building module is used for building a plane model of the warehousing robot and goods; the obstacle avoidance analysis module is used for carrying out plane analysis on goods to obtain an obstacle avoidance path;
the output end of the acquisition control module is connected with the input end of the plane model establishing module, the output end of the plane model establishing module is connected with the input end of the obstacle avoidance analysis module, the output end of the obstacle avoidance analysis module is connected with the input end of the acquisition control module, and the plane model establishing module is connected with the wireless connection module.
According to the technical scheme, the acquisition control module comprises a distance measuring unit, a central control unit, an instruction sending unit and an image acquisition unit;
the distance measuring unit is used for measuring the distance of objects around the warehousing robot; the central control unit is used for intelligently controlling the whole system, the instruction sending unit is used for sending an obstacle avoidance path instruction, and the image acquisition unit is used for acquiring image data around the warehousing robot;
the output end of the distance measuring unit is connected with the input end of the central control unit, the output end of the central control unit is connected with the input ends of the image acquisition unit and the instruction sending unit, and the output end of the image acquisition unit is connected with the input end of the plane model establishing module.
According to the technical scheme, the plane model establishing module comprises a coordinate value endowing unit of a contour extracting unit, an origin positioning unit and a coordinate system establishing unit;
the contour extraction unit is used for extracting the contour of the image data acquired by the image acquisition unit; the origin positioning unit is used for positioning and analyzing the central origin of the image data to conveniently establish a coordinate system, and the coordinate system establishing unit is used for establishing a plane rectangular coordinate system on the image data after the outline is extracted; the coordinate value endowing unit is used for endowing each point on the image contour data with a coordinate value;
the output end of the image acquisition unit is connected with the input end of the contour extraction unit, the output end of the contour extraction unit is connected with the input end of the origin positioning unit, and the output end of the origin positioning unit is connected with the input end of the coordinate system establishing unit.
According to the technical scheme, the wireless connection module comprises a wireless sending module and a wireless receiving module;
the wireless transmitting module is used for transmitting the established plane rectangular coordinate system to the central control unit, and the wireless receiving module is used for receiving plane model data which are transmitted by the central control module and are related to other warehousing robots.
According to the technical scheme, the obstacle avoidance analysis module comprises a shape analysis unit, a width determination unit, a placement judgment unit and a path selection unit;
the shape analysis unit is used for analyzing and judging the shape of goods placed on the warehousing robot, the width determination unit is used for analyzing and judging the width of the goods, the placement judgment unit is used for analyzing and judging whether the placement of the goods on the warehousing robot is centrosymmetric, and the path selection unit is used for analyzing and judging the obstacle avoidance path of the warehousing robot;
the output end of the plane model building module is connected with the input end of the shape analysis unit, the output end of the shape analysis unit is connected with the input end of the width determination unit, the output end of the width determination unit is connected with the input end of the placement judgment unit, the output end of the placement judgment unit is connected with the input end of the path selection unit, and the output end of the path selection unit is connected with the input end of the central control unit.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, the distance measuring unit is used for detecting the distance of objects around the warehousing robot, the image acquisition unit is used for acquiring the image data of the goods, the image data is analyzed, and the shape of the goods and the placing form of the goods on the warehousing robot are fully considered, so that when the warehousing robot avoids obstacles, accidents caused by collision among the goods are avoided, and the obstacle avoidance of the warehousing robot is more intelligent.
2. The invention is provided with the plane model establishing module, so that the obstacle image acquired by the image acquiring unit can be converted into the digital plane model, the digital analysis is convenient, the analysis result is more accurate, and the analysis error is reduced.
Drawings
Fig. 1 is a schematic diagram of a module connection structure of an intelligent obstacle avoidance system of a warehousing robot under the internet of things;
fig. 2 is a schematic flow chart of steps of an intelligent obstacle avoidance method of a warehousing robot under the internet of things.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 to 2, the invention provides the following technical solutions, an intelligent obstacle avoidance method for a warehousing robot under the internet of things, the intelligent obstacle avoidance method comprising the following steps:
s1, measuring the distance of objects around the storage robot by using a distance measuring unit, and sending a measuring result to a central control unit;
the purpose of distance measurement by using the distance measurement unit is to judge whether the warehousing robot needs to carry out intelligent obstacle avoidance operation on the traveling route of the warehousing robot;
s2, selectively starting an image acquisition unit by using a central control unit according to the measurement data to acquire image data;
only when the obstacle avoidance operation is needed, the image acquisition unit is started to acquire the image, so that the calculation amount can be reduced;
s3, extracting the contour of the acquired image data by using a plane model building module, building a plane rectangular coordinate system, and giving a coordinate value to each point on the contour;
the goods transportation process of the storage robot is digitalized, so that the analysis of an obstacle avoidance path and the selection of an obstacle avoidance mode are convenient to perform at the later stage;
s4, analyzing an obstacle avoidance path of the storage robot by using an obstacle avoidance analysis module, and sending an analysis result to a central control unit;
and S5, analyzing the obstacle avoidance mode by using the central control unit, sending the final obstacle avoidance path to the instruction sending unit, and sending an obstacle avoidance instruction to the warehousing robot by using the instruction sending unit.
In S1-S2, the number of the distance measuring units is n, and the distance measuring units are installed on the warehousing robot, for example: the storage robot is provided with distance measuring units around, the distance measuring units are distance sensors, and the measured value of the distance measuring unit is Li, wherein i represents the ith distance measuring unit on the storage robot, for example: l1 represents distance data collected by a distance measuring unit right ahead in the traveling direction of the warehousing robot, the central control unit is installed in a master control center of the warehousing robot, n distance measuring units send collected distance data Qj = { L1, L2, L3.., ln } to the central control unit, wherein Qi represents a set of distance data collected by n distance measuring units on the warehousing robot numbered j, for example: q1= { L1, L2, L3, L4} represents a set of distance data measured by 4 distance measurement units on the warehousing robot numbered 1;
when at least one distance data of Qj = { L1, L2, L3.,. Ln } is equal to or less than a set threshold L, for example: when L1 is equal to or less than L =2m, L1 is a distance measuring unit right ahead in the traveling direction of the warehousing robot, and the central control unit starts the image acquisition unit to acquire image data around the warehousing robot, for example: at the moment, the central control unit starts the image acquisition unit to acquire image data right ahead of the warehousing robot in the traveling direction, the image acquisition unit is installed on the warehousing robot, and the image acquisition unit sends the acquired image data to the contour extraction unit.
In S1, the plane model building module comprises a contour extraction unit, an origin positioning unit and a coordinate value endowing unit of a coordinate system building unit;
the outline extraction unit adopts a Sobel operator to extract the outline of the image data acquired by the image acquisition unit, an origin point locating point is arranged on a central symmetrical line of the warehousing robot, the origin point locating point is a circular icon with an obvious outline, the origin point locating unit searches the origin point locating point on the outline image extracted by the outline extraction unit, the purpose of searching the origin point locating point is to conveniently establish a plane rectangular coordinate system, the coordinate system establishment unit establishes the plane rectangular coordinate system by taking the origin point locating point as the origin point, and the purpose is to provide more convenience for maximum width calculation in the later period, a coordinate value endowing unit endows each point in the coordinate system with a coordinate value (Xk, yk), wherein k represents the kth coordinate point, so that a set P = { (X1, Y1), (X2, Y2), (X3, Y3),. And (Xm, ym) } of the coordinate points is formed, and m represents m coordinate points.
In S4, the method specifically includes the following steps:
the method comprises the following steps: analyzing the shape of the cargo;
when Xk is larger than 0, dividing the coordinate point into a first quadrant of a plane rectangular coordinate system;
when Xk is less than 0, dividing the coordinate point into a second quadrant of the plane rectangular coordinate system;
calculating the coordinate points in the first quadrant and the second quadrant according to the following formulas;
wherein k represents the kth coordinate point, and t represents the tth coordinate point;
when the temperature is higher than the set temperature
When the quantity of the cargos is larger than a set threshold value, judging that the shape of the cargos is a rectangle;
when in use
When the quantity of the goods is less than or equal to a set threshold value, judging that the shape of the goods is an irregular shape, wherein the irregular shape refers to other shapes except a rectangle;
step two: determining a maximum width of the cargo;
the width of the cargo is calculated according to the following formula:
S=Xi-Xj；
where Xi represents the abscissa of the coordinate point in the first quadrant, xj represents the abscissa of the coordinate point in the second quadrant, yi = Yj;
extracting a maximum value Smax as the maximum width of the goods;
step three: judging whether the goods are placed on the warehousing robot in a centrosymmetric manner;
respectively extracting coordinate points with equal ordinate values in a first quadrant and a second quadrant, and forming a set Y = { X1, X2, X3,. Once, xa } by using the maximum values of the absolute values of the abscissa of the coordinate points with equal ordinate values, wherein a represents a coordinate points with equal absolute values of the abscissa;
when Xe = -Xf in the set Y, determining that the goods are placed on the warehousing robot in a central symmetry manner, wherein Xe represents the abscissa of a coordinate point located in a first quadrant, and Xf represents the abscissa of a coordinate point located in a second quadrant;
the cargo width is Xe-Xf; then the obstacle avoidance width of the storage robot is as follows:
（Xe-Xf）/2；
when Xe in the set Y is not equal to Xf, judging that the goods are placed on the warehousing robot in a non-centrosymmetric mode, wherein Xe represents the abscissa of a coordinate point located in a first quadrant, and Xf represents the abscissa of a coordinate point located in a second quadrant;
the cargo width is Xe-Xf; then the obstacle avoidance width of the storage robot is as follows:
xe or Xf;
step four: selecting an obstacle avoidance path;
the distance measuring unit respectively detects obstacles in other directions of the warehousing robot A and the warehousing robot B;
if the distance between the obstacles in at least two directions of one storage robot is smaller than or equal to a set threshold value, the storage robot does not avoid, and the other storage robot avoids the obstacles;
when the goods on the two warehousing robots are placed in central symmetry, the avoiding distance is [ (Xe-Xf)/2 ]. Multidot.2;
when the goods on the two storage robots are placed in a non-centrosymmetric manner, the avoiding distance is Xe + Xe or Xe-Xf or | Xf | + | Xf |;
the above judging mode is that the types and sizes of the goods are consistent;
if the distances between the obstacles in at least two directions of the two warehousing robots are smaller than or equal to a set threshold value, the two warehousing robots stop waiting;
and the obstacle avoidance analysis module sends an analysis result to the central control unit.
Through above-mentioned technical scheme, when carrying out the obstacle of keeping away of storage robot, the width of fully considering transportation goods on the storage robot and the mode of putting of goods for when keeping away the obstacle more intelligent.
In S5, the central control unit integrates the analysis results of all the storage robots in the warehouse, determines the analysis result of the storage robot that needs to avoid the obstacle, selects the obstacle avoiding path with the minimum obstacle avoiding distance, and sends the final obstacle avoiding instruction to the corresponding storage robot through the instruction sending unit, and the storage robot executes the obstacle avoiding instruction of the central control unit to avoid the obstacle.
The utility model provides a barrier system is kept away to storage robot intelligence under thing networking which characterized in that: the obstacle avoidance system comprises an acquisition control module, a wireless connection module, a plane model establishing module and an obstacle avoidance analysis module;
the acquisition control module is used for acquiring data and controlling a system; the wireless connection module is used for sending and receiving data, and the plane model building module is used for building a plane model of the warehousing robot and goods; the obstacle avoidance analysis module is used for carrying out plane analysis on goods to obtain an obstacle avoidance path;
the output end of the acquisition control module is connected with the input end of the plane model establishing module, the output end of the plane model establishing module is connected with the input end of the obstacle avoidance analysis module, the output end of the obstacle avoidance analysis module is connected with the input end of the acquisition control module, and the plane model establishing module is connected with the wireless connection module.
The acquisition control module comprises a distance measuring unit, a central control unit, an instruction sending unit and an image acquisition unit;
the distance measuring unit is used for measuring the distance of objects around the warehousing robot; the central control unit is used for intelligently controlling the whole system, the instruction sending unit is used for sending an obstacle avoidance path instruction, and the image acquisition unit is used for acquiring image data around the warehousing robot;
the output end of the distance measuring unit is connected with the input end of the central control unit, the output end of the central control unit is connected with the input ends of the image acquisition unit and the instruction sending unit, and the output end of the image acquisition unit is connected with the input end of the plane model establishing module.
The plane model building module comprises a contour extraction unit, an origin positioning unit and a coordinate value endowing unit of a coordinate system building unit;
the contour extraction unit is used for extracting the contour of the image data acquired by the image acquisition unit, so that a digital plane model can be conveniently established in the later stage; the origin positioning unit is used for positioning and analyzing the central origin of the image data to conveniently establish a coordinate system, and the coordinate system establishing unit is used for establishing a plane rectangular coordinate system on the image data after the contour is extracted; the coordinate value endowing unit is used for endowing each point on the image contour data with a coordinate value;
the output end of the image acquisition unit is connected with the input end of the contour extraction unit, the output end of the contour extraction unit is connected with the input end of the origin positioning unit, and the output end of the origin positioning unit is connected with the input end of the coordinate system establishing unit.
The wireless connection module comprises a wireless sending module and a wireless receiving module;
the wireless transmitting module is used for transmitting the established plane rectangular coordinate system to the central control unit, and the wireless receiving module is used for receiving plane model data which are transmitted by the central control module and are related to other warehousing robots.
The obstacle avoidance analysis module comprises a shape analysis unit, a width determination unit, a placement judgment unit and a path selection unit;
the shape analysis unit is used for analyzing and judging the shape of goods placed on the warehousing robot, the width determination unit is used for analyzing and judging the width of the goods, the placement judgment unit is used for analyzing and judging whether the placement of the goods on the warehousing robot is centrosymmetric, and the path selection unit is used for analyzing and judging the obstacle avoidance path of the warehousing robot;
the output end of the plane model building module is connected with the input end of the shape analysis unit, the output end of the shape analysis unit is connected with the input end of the width determination unit, the output end of the width determination unit is connected with the input end of the placement judgment unit, the output end of the placement judgment unit is connected with the input end of the path selection unit, and the output end of the path selection unit is connected with the input end of the central control unit.
The first embodiment is as follows:
the number of the distance measuring units is 2, the distance measuring units are installed on the warehousing robot, and the 2 distance measuring units send collected distance data L1=10m and L2=2m to the central control unit;
the system comprises a set threshold value with L2=2m being not more than L =2.5m, an image acquisition unit is started by a central control unit to acquire image data around the warehousing robot and is installed on the warehousing robot, and the image acquisition unit sends the acquired image data to a contour extraction unit.
The contour extraction unit extracts a contour from the image data acquired by the image acquisition unit by using a Sobel operator, an origin locating point is arranged on a central symmetry line of the warehousing robot, the origin locating unit searches an origin locating point on the contour image extracted by the contour extraction unit, a rectangular plane coordinate system is established by using the origin locating point as an origin through the coordinate system establishment unit, and a coordinate value (Xk, yk) is given to each point in the coordinate system through the coordinate value giving unit, wherein k represents the kth coordinate point, and constitutes a set of coordinate points P = { (X1, Y1), (X2, Y2), (X3, Y3),. }, (Xm, ym) = { (5,1), (5,2), (5,3), (5,4), (5,5), (-5364 zxft 53865) (-2 zxft 8652), (-3265), (-3579).
The method specifically comprises the following steps:
the method comprises the following steps: analyzing the shape of the cargo;
when Xk is larger than 0, dividing the coordinate point into a first quadrant of a plane rectangular coordinate system;
when Xk is less than 0, dividing the coordinate point into a second quadrant of the plane rectangular coordinate system;
calculating the coordinate points in the first quadrant and the second quadrant according to the following formulas;
wherein k represents the kth coordinate point, and t represents the tth coordinate point;
when in use
When the number of the goods is 10 and is more than the set threshold value of 5, judging that the shape of the goods is rectangular;
step two: determining a maximum width of the cargo;
the width of the cargo is calculated according to the following formula:
S=Xi-Xj；
where Xi represents the abscissa of the coordinate point in the first quadrant, xj represents the abscissa of the coordinate point in the second quadrant, yi = Yj;
extracting a maximum value Smax =10 as the maximum width of the cargo;
step three: judging whether the goods are placed on the warehousing robot in a central symmetry manner or not;
respectively extracting coordinate points with equal ordinate values in the first quadrant and the second quadrant, and forming a set Y = {5,5,5,5,5, -5, -5, -5, -5} by the maximum absolute values of the abscissas of the coordinate points with equal ordinate values;
5= - (-5), and judging that the goods are placed on the warehousing robot in a centrosymmetric manner;
the cargo width is Xe-Xf =10; then the obstacle avoidance width of the storage robot is as follows:
（Xe-Xf）/2=5；
step four: selecting an obstacle avoidance path;
the distance measuring unit respectively detects obstacles in other directions of the warehousing robot A and the warehousing robot B;
if the distance between the obstacles in at least two directions of one storage robot is smaller than or equal to a set threshold value, the storage robot does not avoid the obstacles, and the other storage robot avoids the obstacles;
when the goods on the two warehousing robots are placed in central symmetry, the avoiding distance is [ (Xe-Xf)/2 ] × 2=10;
if the distances between the obstacles in at least two directions of the two warehousing robots are smaller than or equal to a set threshold value, the two warehousing robots stop waiting;
and the obstacle avoidance analysis module sends an analysis result to the central control unit.
The central control unit integrates analysis results of all storage robots in the warehouse, judges the analysis results of the storage robots needing to avoid the obstacle, selects an obstacle avoiding path with the minimum obstacle avoiding distance, sends the final obstacle avoiding instruction to the corresponding storage robots through the instruction sending unit, and the storage robots execute the obstacle avoiding instruction of the central control unit to avoid the obstacle.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (7)
1. An intelligent obstacle avoidance method for a storage robot under the Internet of things is characterized in that: the intelligent obstacle avoidance method comprises the following steps:
s1, measuring the distance of objects around the storage robot by using a distance measuring unit, and sending a measuring result to a central control unit;
s2, selectively starting an image acquisition unit by using a central control unit according to the measurement data to acquire image data;
s3, extracting the contour of the acquired image data by using a plane model building module, building a plane rectangular coordinate system, and giving a coordinate value to each point on the contour;
s4, analyzing an obstacle avoidance path of the storage robot by using an obstacle avoidance analysis module, and sending an analysis result to a central control unit;
s5, analyzing the obstacle avoidance mode by using the central control unit, sending the final obstacle avoidance path to the instruction sending unit, and sending an obstacle avoidance instruction to the storage robot by using the instruction sending unit;
in S1-S2, the number of the distance measuring units is n, the distance measuring units are installed on the warehousing robot, and the measured value of the distance measuring units is Li, where i represents the ith distance measuring unit on the warehousing robot, the central control unit is installed in the general control center of the warehousing robot, and the n distance measuring units transmit the collected distance data Qj = { L1, L2, L3,.., ln } to the central control unit, where Qj represents the set of the distance data collected by the n distance measuring units on the warehousing robot with the number j;
when at least one distance data in Qj = { L1, L2, L3, ·, ln } is smaller than or equal to a set threshold value L, the central control unit starts an image acquisition unit to acquire image data around the warehousing robot, the image acquisition unit is installed on the warehousing robot, and the image acquisition unit sends the acquired image data to a contour extraction unit;
the plane model building module comprises a contour extraction unit, an origin positioning unit and a coordinate value endowing unit of a coordinate system building unit;
the contour extraction unit extracts a contour from image data acquired by the image acquisition unit by using a Sobel operator, an origin locating point is arranged on a central symmetry line of the warehouse robot, the origin locating unit searches for the origin locating point on a contour image extracted by the contour extraction unit, a coordinate system establishment unit establishes a planar rectangular coordinate system by using the origin locating point as an origin, and a coordinate value endowing unit endows each point in the coordinate system with a coordinate value (Xk, yk), wherein k represents a k-th coordinate point, and a set P = { (X1, Y1), (X2, Y2), (X3, Y3),. And (Xm, ym) } of coordinate points is formed, wherein m represents m coordinate points;
in S4, the method specifically includes the following steps:
the method comprises the following steps: analyzing the shape of the cargo;
when Xk is larger than 0, dividing the coordinate point into a first quadrant of a plane rectangular coordinate system;
when Xk is less than 0, dividing the coordinate point into a second quadrant of the plane rectangular coordinate system;
calculating the coordinate points in the first quadrant and the second quadrant according to the following formulas;
wherein k represents a kth coordinate point, and t represents a tth coordinate point;
when in use
When the quantity of the goods is larger than a set threshold value, judging that the shape of the goods is a rectangle;
when in use
When the quantity of the goods is less than or equal to the set threshold value, judging that the shape of the goods is an irregular shape;
step two: determining a maximum width of the cargo;
the width of the cargo is calculated according to the following formula:
S=Xi-Xj；
where Xi represents the abscissa of the coordinate point in the first quadrant, xj represents the abscissa of the coordinate point in the second quadrant, yi = Yj;
extracting a maximum value Smax as the maximum width of the goods;
step three: judging whether the goods are placed on the warehousing robot in a centrosymmetric manner;
respectively extracting coordinate points with equal ordinate values in the first quadrant and the second quadrant, and forming a set Y = { X1, X2, X3,. Once, xa } by the maximum value of the abscissa absolute values of the coordinate points with equal ordinate values, wherein a represents a coordinate points with equal abscissa absolute values;
when Xe = -Xf in the set Y, judging that the goods are placed on the warehousing robot in a centrosymmetric placement, wherein Xe represents the abscissa of a coordinate point located in a first quadrant, and Xf represents the abscissa of a coordinate point located in a second quadrant;
the cargo width is Xe-Xf; then the obstacle avoidance width of the storage robot is as follows:
（Xe-Xf）/2；
when Xe ≠ -Xf in the set Y, judging that the goods are placed on the storage robot in a non-centrosymmetric mode, wherein Xe represents the abscissa of a coordinate point located in a first quadrant, and Xf represents the abscissa of a coordinate point located in a second quadrant;
the cargo width is Xe-Xf; then the obstacle avoidance width of the storage robot is as follows:
xe or Xf;
step four: selecting an obstacle avoidance path;
the distance measuring unit respectively detects obstacles in other directions of the warehousing robot A and the warehousing robot B;
if the distance between the obstacles in at least two directions of one storage robot is smaller than or equal to a set threshold value, the storage robot does not avoid, and the other storage robot avoids the obstacles;
when the goods on the two warehousing robots are placed in central symmetry, the avoiding distance is [ (Xe-Xf)/2 ]. Multidot.2;
when the goods on the two storage robots are placed in a non-centrosymmetric manner, the avoiding distance is Xe + Xe or Xe-Xf or | Xf | + | Xf |;
if the distances between the obstacles in at least two directions of the two warehousing robots are smaller than or equal to a set threshold value, the two warehousing robots stop waiting;
and the obstacle avoidance analysis module sends an analysis result to the central control unit.
2. The intelligent obstacle avoidance method for the storage robot under the Internet of things according to claim 1, characterized in that: in S5, the central control unit integrates the analysis results of all the storage robots in the warehouse, determines the analysis result of the storage robot that needs to avoid the obstacle, selects the obstacle avoiding path with the minimum obstacle avoiding distance, and sends the final obstacle avoiding instruction to the corresponding storage robot through the instruction sending unit, and the storage robot executes the obstacle avoiding instruction of the central control unit to avoid the obstacle.
3. The intelligent obstacle avoidance system of the storage robot under the internet of things, applied to the intelligent obstacle avoidance method of the storage robot under the internet of things according to any one of claims 1-2, is characterized in that: the obstacle avoidance system comprises an acquisition control module, a wireless connection module, a plane model establishing module and an obstacle avoidance analysis module;
the acquisition control module is used for acquiring data and controlling a system; the wireless connection module is used for sending and receiving data, and the plane model building module is used for building a plane model of the warehousing robot and goods; the obstacle avoidance analysis module is used for carrying out plane analysis on goods to obtain an obstacle avoidance path;
the output end of the acquisition control module is connected with the input end of the plane model building module, the output end of the plane model building module is connected with the input end of the obstacle avoidance analysis module, the output end of the obstacle avoidance analysis module is connected with the input end of the acquisition control module, and the plane model building module is connected with the wireless connection module.
4. The intelligent obstacle avoidance system for the warehousing robot under the internet of things according to claim 3, wherein: the acquisition control module comprises a distance measuring unit, a central control unit, an instruction sending unit and an image acquisition unit;
the distance measuring unit is used for measuring the distance of objects around the warehousing robot; the central control unit is used for intelligently controlling the whole system, the instruction sending unit is used for sending an obstacle avoidance path instruction, and the image acquisition unit is used for acquiring image data around the warehousing robot;
the output end of the distance measuring unit is connected with the input end of the central control unit, the output end of the central control unit is connected with the input ends of the image acquisition unit and the instruction sending unit, and the output end of the image acquisition unit is connected with the input end of the plane model establishing module.
5. The intelligent obstacle avoidance system for the warehousing robot under the internet of things according to claim 4, wherein: the plane model establishing module comprises a contour extracting unit, an origin positioning unit and a coordinate value endowing unit of a coordinate system establishing unit;
the contour extraction unit is used for extracting the contour of the image data acquired by the image acquisition unit; the origin positioning unit is used for positioning and analyzing the central origin of the image data to conveniently establish a coordinate system, and the coordinate system establishing unit is used for establishing a plane rectangular coordinate system on the image data after the contour is extracted; the coordinate value endowing unit is used for endowing each point on the image contour data with a coordinate value;
the output end of the image acquisition unit is connected with the input end of the contour extraction unit, the output end of the contour extraction unit is connected with the input end of the origin positioning unit, and the output end of the origin positioning unit is connected with the input end of the coordinate system establishing unit.
6. The intelligent obstacle avoidance system for the warehousing robot under the internet of things as claimed in claim 5, wherein: the wireless connection module comprises a wireless sending module and a wireless receiving module;
the wireless transmitting module is used for transmitting the established plane rectangular coordinate system to the central control unit, and the wireless receiving module is used for receiving plane model data which are transmitted by the central control module and are related to other warehousing robots.
7. The intelligent obstacle avoidance system for the warehousing robot under the internet of things according to claim 6, wherein: the obstacle avoidance analysis module comprises a shape analysis unit, a width determination unit, a placement judgment unit and a path selection unit;
the shape analysis unit is used for analyzing and judging the shape of goods placed on the warehousing robot, the width determination unit is used for analyzing and judging the width of the goods, the placement judgment unit is used for analyzing and judging whether the placement of the goods on the warehousing robot is centrosymmetric, and the path selection unit is used for analyzing and judging the obstacle avoidance path of the warehousing robot;
the output end of the plane model building module is connected with the input end of the shape analysis unit, the output end of the shape analysis unit is connected with the input end of the width determination unit, the output end of the width determination unit is connected with the input end of the placement judgment unit, the output end of the placement judgment unit is connected with the input end of the path selection unit, and the output end of the path selection unit is connected with the input end of the central control unit.
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