CN115436881B - Positioning method, positioning system, computer equipment and readable storage medium - Google Patents
Positioning method, positioning system, computer equipment and readable storage medium Download 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
- G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
- G01S5/22—Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The application belongs to the field of ultrasonic ranging, and discloses a positioning method, a positioning system, computer equipment and a readable storage medium, wherein the positioning method comprises the following steps: a plurality of first chaotic ultrasonic signals with different frequency bands are sent, and the first chaotic ultrasonic signals are generated and emitted by a plurality of ultrasonic generating modules on the component in combination with a chaotic system; receiving a plurality of corresponding second chaotic ultrasonic signals formed by a plurality of first chaotic ultrasonic signals propagated in the air; splicing the plurality of second chaotic ultrasonic signals to obtain spliced signals; calculating to obtain the position information of an ultrasonic generating module corresponding to the first chaotic ultrasonic signal by adopting a neural network according to the spliced signal; acquiring posture information according to the position information, and calculating to acquire a component posture adjustment instruction and a displacement instruction according to the position information and the posture information; according to the component posture adjustment instruction and the displacement instruction, the component posture adjustment and the displacement are controlled, and the effects of improving the component alignment efficiency and the component accuracy in industrial manufacturing are achieved.
Description
Technical Field
The present disclosure relates to the field of ultrasonic ranging technologies, and in particular, to a positioning method, a positioning system, a computer device, and a readable storage medium.
Background
In industrial production, the assembly and welding of two industrial product components requires that the assembly mechanisms and welds of the components be sufficiently close and aligned. However, large or ultra-large industrial product components are characterized by large mass and difficult handling, which makes them difficult to access and align and risky. If collision occurs during the approach of the components, the components can be damaged, production accidents can be caused, and even casualties can be caused. If the alignment of the components is greatly deviated, the quality of industrial products is not up to standard, and economic loss is caused. The components are accurately positioned, so that the problems of approaching collision, alignment deviation and the like of the components can be effectively solved, and the risk of accidents is reduced.
The existing method for accurately positioning the component comprises the following steps:
(1) Manual alignment method:
in the assembly and welding process of the components, the components are aligned in a mode of human eye observation and experience judgment. The method relies on subjective perception and experience accumulation of a person, the accuracy of component alignment can be influenced by the physical and mental states of the person, and each alignment is difficult to be ensured to be successful. In addition, manual alignment is inefficient and time consuming.
(2) Traditional alignment methods based on ultrasonic positioning:
based on the alignment method of traditional ultrasonic positioning, the ultrasonic positioning is utilized to position the components to be assembled and welded, so that the components can be accurately measured and aligned, and the alignment precision and efficiency are higher than those of the manual alignment method. However, the transmitted waveform signal has the problems of multipath effect, internal period and the like, which can cause deviation of time estimation and further affect positioning accuracy.
In addition, the accuracy of conventional ultrasound localization methods depends on the effectiveness of artificial design algorithms (e.g., TDOA, MUSIC, etc.). These algorithms often deviate from the actual situation in application, requiring a large number of additional experiments to compensate and correct the algorithm model. The implementation difficulty and the implementation cost are high.
(3) Alignment method based on electromagnetic wave positioning:
electromagnetic wave propagation speed is about 3×10 8 m/s, if the aligned system produces a small deviation in the measurement of time, positioning will resultA huge error occurs.
Disclosure of Invention
Therefore, the embodiments of the present application provide a positioning method, a positioning system, and a readable storage medium, which can solve the technical problems of low alignment efficiency and low accuracy of the existing components, and the specific technical solution is as follows:
in a first aspect, embodiments of the present application provide a positioning method, the method including:
a plurality of first chaotic ultrasonic signals with different frequency bands are sent, and the first chaotic ultrasonic signals are generated and emitted by a plurality of ultrasonic generation modules arranged on the component in combination with a chaotic system;
receiving a plurality of corresponding second chaotic ultrasonic signals formed by a plurality of first chaotic ultrasonic signals propagated in the air;
performing splicing processing on the plurality of second chaotic ultrasonic signals to obtain spliced signals;
calculating the position information of an ultrasonic transmitting module corresponding to the first chaotic ultrasonic signal by adopting a neural network according to the spliced signal; the neural network comprises a main network, a coarse positioning network and a fine positioning network, and output signals of the main network are input into the coarse positioning network and the fine positioning network; the output signal of the coarse positioning network is input into the fine positioning network;
acquiring posture information according to the position information, and acquiring a component posture adjustment instruction and a displacement instruction according to the position information and the posture information;
and controlling the component posture adjustment and displacement according to the component posture adjustment command and the displacement command.
Preferably, the backbone network consists of five bottleneck structures, a global average pooling layer and two attention modules;
wherein, the convolution layer of the first bottleneck structure is two-dimensional convolution, and the convolution layers of the rest bottleneck structures are one-dimensional convolution;
the output signal of the first bottleneck structure is input into the global average pooling layer and then sequentially input into the rest bottleneck structures;
the output signal of the last bottleneck structure is sequentially input to the attention module.
Preferably, the bottleneck structure comprises 3 convolution layers, 3 ReLU activation functions and 1 downsampling layer;
the convolution layer and the ReLU activation function are arranged in a matched mode, and an output signal of the convolution layer is input into the ReLU activation function and then output;
and accumulating the output signals of the ReLU activation function and inputting the accumulated output signals into the downsampling layer.
Preferably, the coarse positioning network comprises 3 convolutional layers, 3 ReLU activation functions and 1 adaptive average pooling layer;
the convolution layer and the ReLU activation function are arranged in a matched mode, and an output signal of the convolution layer is input into the ReLU activation function and then output;
and accumulating the output signals of the ReLU activation function and inputting the accumulated signals into the adaptive average pooling layer.
Preferably, the fine positioning network comprises 5 convolutional layers, 5 ReLU activation functions, and 1 adaptive average pooling layer;
the convolution layer and the ReLU activation function are arranged in a matched mode, and an output signal of the convolution layer is input into the ReLU activation function and then output;
and accumulating the output signals of the ReLU activation function and inputting the accumulated signals into the adaptive average pooling layer.
Preferably, the receiving the plurality of corresponding second chaotic ultrasonic signals formed by the plurality of first chaotic ultrasonic signals propagated in the air is:
and setting a receiving window with the length longer than that of the first chaotic ultrasonic signals so as to receive a plurality of corresponding second chaotic ultrasonic signals formed by a plurality of first chaotic ultrasonic signals propagated in the air.
Preferably, the splicing processing is performed on the plurality of second chaotic ultrasonic signals to obtain a spliced signal, including:
zero padding is carried out on the pre-stored first chaotic ultrasonic signal, so that the length of the pre-stored first chaotic ultrasonic signal is the same as that of the second chaotic ultrasonic signal;
and splicing the first chaotic ultrasonic signal subjected to zero padding with a second chaotic ultrasonic signal with the same frequency band to obtain a spliced signal.
Preferably, the calculating, by using a neural network according to the spliced signal, the position information of the ultrasonic transmitting module corresponding to the first chaotic ultrasonic signal includes:
the method comprises the steps of constructing a sample set by placing an ultrasonic transmitting module at a preset space coordinate and receiving a transmitting waveform signal of the ultrasonic transmitting module;
training the neural network through the sample set in a supervised learning mode, and calculating the position information by learning using a plurality of spliced signals;
and inputting the spliced signals into the trained neural network to obtain the position information of the ultrasonic transmitting module corresponding to the first chaotic ultrasonic signal.
In a second aspect, embodiments of the present application provide a positioning system, the system comprising:
an ultrasonic transmitting module: a plurality of first chaotic ultrasonic signals with different frequency bands are sent, and the first chaotic ultrasonic signals are generated and transmitted by an ultrasonic transmitting module on a component in combination with a chaotic system;
an ultrasonic acquisition module: receiving a plurality of corresponding second chaotic ultrasonic signals formed by a plurality of first chaotic ultrasonic signals propagated in the air;
and a data preprocessing module: splicing the received second chaotic ultrasonic signals to obtain spliced signals;
deep neural network module: calculating the position information of an ultrasonic transmitting module corresponding to the first chaotic ultrasonic signal by adopting a neural network according to the spliced signal; the neural network comprises a main network, a coarse positioning network and a fine positioning network, and output signals of the main network are input into the coarse positioning network and the fine positioning network; the output signal of the coarse positioning network is input into the fine positioning network;
the position information post-processing module: acquiring posture information according to the position information, and acquiring a component posture adjustment instruction and a displacement instruction according to the position information and the posture information;
component alignment module: and receiving the gesture adjustment instruction and the displacement instruction, and controlling gesture adjustment and displacement of a component.
Preferably, the ultrasonic transmitting module comprises at least four ultrasonic transmitting devices, and different ultrasonic transmitting devices transmit a plurality of first chaotic ultrasonic signals with different frequency bands.
Preferably, the ultrasonic acquisition module comprises at least four ultrasonic receiving devices, and each ultrasonic receiving device is provided with a plurality of signal acquisition channels.
In a third aspect, embodiments of the present application provide a computer device comprising a memory, a processor and a computer program stored in the memory and running on the processor, the processor implementing the steps of any one of the preceding positioning methods when the computer program is executed.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the positioning method of any of the preceding claims.
In summary, compared with the prior art, the technical scheme provided by the embodiment of the application has the beneficial effects that at least:
the first chaotic ultrasonic signal is used as an ultrasonic transmitting module to be combined with a transmitting waveform signal of a chaotic system, so that the matching error of a signal phase caused by multipath effect and signal internal period is effectively avoided, the accuracy of component alignment is further improved, a neural network comprising a main network, a coarse positioning network and a fine positioning network is further used as a signal processing means of a calculation back end, the distance from the ultrasonic transmitting module at a specific position of a component to an ultrasonic receiving module in an assembly and welding field is measured, and the spatial coordinates of the component are positioned. The method has higher precision and adaptability compared with the traditional measurement and positioning method, and the ultrasonic ranging and positioning method is lower in indoor error compared with the electromagnetic wave ranging method.
Drawings
Fig. 1 is a flow chart of a positioning method according to an embodiment of the present application.
Fig. 2 is a schematic flow chart of a positioning method according to another embodiment of the present application.
Fig. 3 is a second flowchart of a positioning method according to another embodiment of the present application.
Fig. 4 is a main block diagram of a neural network of a positioning method according to an exemplary embodiment of the present application.
Fig. 5 is a main network structure diagram of a neural network according to an exemplary embodiment of the present application.
Fig. 6 is a schematic diagram of a bottleneck structure of a backbone network of a neural network according to an exemplary embodiment of the present application.
Fig. 7 is a diagram of a coarse positioning network structure of a neural network according to an exemplary embodiment of the present application.
Fig. 8 is a diagram of a fine positioning network structure of a neural network according to an exemplary embodiment of the present application.
Fig. 9 is a schematic structural diagram of a positioning system according to an embodiment of the present application.
Detailed Description
The present embodiment is merely illustrative of the present application and is not intended to be limiting, and those skilled in the art, after having read the present specification, may make modifications to the present embodiment without creative contribution as required, but is protected by patent laws within the scope of the claims of the present application.
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
In addition, the term "and/or" in this application is merely an association relationship describing an association object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In this application, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.
The terms "first," "second," and the like in this application are used to distinguish between identical or similar items that have substantially the same function and function, and it should be understood that there is no logical or chronological dependency between the "first," "second," and "nth" terms, nor is it limited to the number or order of execution.
The term "at least one" in this application means one or more, meaning "a plurality of" means three or more, for example, a plurality of first positions means three or more first positions.
Embodiments of the present application are described in further detail below with reference to the drawings attached hereto.
Referring to fig. 1, in one embodiment of the present application, a positioning method is provided, and the main steps of the method are described as follows:
s1: a plurality of first chaotic ultrasonic signals with different frequency bands are sent, and the first chaotic ultrasonic signals are generated and emitted by a plurality of ultrasonic generating modules on the component in combination with a chaotic system;
s2: receiving a plurality of corresponding second chaotic ultrasonic signals formed by a plurality of first chaotic ultrasonic signals propagated in the air;
s3: performing splicing processing on the plurality of second chaotic ultrasonic signals to obtain spliced signals;
s4: calculating the position information of an ultrasonic transmitting module corresponding to the first chaotic ultrasonic signal by adopting a neural network according to the spliced signal; the neural network comprises a main network, a coarse positioning network and a fine positioning network, and output signals of the main network are input into the coarse positioning network and the fine positioning network; the output signal of the coarse positioning network is input into the fine positioning network;
s5: acquiring posture information according to the position information, and acquiring a component posture adjustment instruction and a displacement instruction according to the position information and the posture information;
s6: and controlling the component posture adjustment and displacement according to the component posture adjustment command and the displacement command.
Specifically, the method is suitable for a system for positioning the components in the assembly and welding processes of the components, and the system comprises an ultrasonic transmitting module, an ultrasonic acquisition module and a data processing module, wherein the ultrasonic transmitting module adopts a chaotic ultrasonic signal as a signal, so that the multipath effect and the matching error of signal phases caused by the internal period of the signal are effectively avoided.
Specifically, in this embodiment, the ultrasonic transmitting module transmits a plurality of first chaotic ultrasonic signals with different frequency bands, and adopts a plurality of groups of first chaotic ultrasonic signals with different frequency bands, so as to achieve the effects of preventing interference and improving measurement accuracy.
The chaotic ultrasonic signal is a continuous and traversal (all samples in the space can appear at least once) non-periodic signal, and the non-periodicity can effectively avoid the matching error of the signal phase, thereby being beneficial to improving the positioning precision.
The chaotic ultrasonic signal is generated in various modes, and the chaotic signal is analyzed and solved by the method. The analytic chaotic system is a chaotic system proposed by Corron N.J. et al in 2010. The kinetic equation of the first chaotic ultrasonic signal may be represented by a differential equation:
wherein, the liquid crystal display device comprises a liquid crystal display device,
in the time-course of which the first and second contact surfaces,
wherein w represents the angular frequency of the mixed chaotic ultrasonic signal;
the negative attenuation coefficient is a left-right opening and closing interval with the value of 0 to ln 2; s=sgn (x) is a switching function, s is a symbol sequence, and takes on a value of 1 or-1;
the method comprises the steps of carrying out a first treatment on the surface of the x is a continuous first chaotic ultrasonic signal.
The formula (1) is a differential equation, g is a negative attenuation coefficient, the value is about 0 to ln2, and the system is chaotic only in the range. x is a function of time t, the amplitude of the x is changed along with t, and the x is a continuous chaotic ultrasonic signal, is a one-dimensional signal and can be transmitted only according to the intensity of x (t).
s=sgn (x) is a switching function, s is defined as a switching condition, i.e. when the first derivative of x is 0, s (t) is assigned to sgn (u (t)), s is a symbol sequence, s is a value of ±1, s changes according to the case that the signal derivative is 0, and the remaining time s (t) remains unchanged until the next switching condition is satisfied.
Deriving an analytical solution from equation (1) and equation (2)
In the case of a situation in which the number of the elements,
is the sign value at t=n,
the sampling value of the chaotic ultrasonic signal when t=n satisfies the following iterative relationship:
according to the analysis solution iteration, a preferable value of x (0) is-0.3776, so that chaotic waveforms can be generated and transmitted by an ultrasonic generating module on the component.
Referring to fig. 2, further, S2 is S2':
s2': and setting a receiving window with the length longer than that of the first chaotic ultrasonic signals so as to receive a plurality of corresponding second chaotic ultrasonic signals formed by a plurality of first chaotic ultrasonic signals propagated in the air.
The ultrasonic acquisition module cannot synchronously receive the data sent by the ultrasonic transmission module, and the receiving window of the ultrasonic acquisition module is set to be larger than the data volume sent by the ultrasonic transmission module, so that the condition that the ultrasonic receiving module is missed or less in receiving can be effectively reduced. For example, a signal of 1.5 seconds is transmitted and received in a window of two seconds.
Referring to fig. 3, further, S3 includes:
s31: zero padding is carried out on the pre-stored first chaotic ultrasonic signal, so that the length of the pre-stored first chaotic ultrasonic signal is the same as that of the second chaotic ultrasonic signal;
s32: and splicing the first chaotic ultrasonic signal subjected to zero padding with a second chaotic ultrasonic signal with the same frequency band to obtain a spliced signal.
Through the arrangement of the embodiment, the first chaotic ultrasonic signal and the second chaotic ultrasonic signal are processed in the same computing device, and after the computing device generates the first chaotic ultrasonic signal, the first chaotic ultrasonic signal is stored in advance and is used for being spliced with the second chaotic ultrasonic signal to form a spliced signal.
For example, the computer respectively transmits signals with the same frequency band (the same frequency band is transmitted by an ultrasonic transmitting device on the same ultrasonic transmitting module) received by the ultrasonic collecting modules of the four collecting channels, and then the first chaotic ultrasonic signals subjected to zero padding and the second chaotic ultrasonic signals with the same frequency received by the four collecting channels are spliced. For example, the data length of the transmission and reception waveform signal is [1, 20000], and the spliced data shape is [5, 20000].
Referring to fig. 3, further, S4 includes:
s41: the method comprises the steps of constructing a sample set by placing an ultrasonic transmitting module at a preset space coordinate and receiving a transmitting waveform signal of the ultrasonic transmitting module;
s42: training the neural network through the sample set in a supervised learning mode, and calculating the position information by learning using a plurality of spliced signals;
s43: and inputting the spliced signals into the trained neural network to obtain the position information of the ultrasonic transmitting module corresponding to the first chaotic ultrasonic signal.
Knowing that the propagation velocity of the ultrasonic wave in the air medium at room temperature is about 340m/s, the distance from the ultrasonic transmission module to the ultrasonic acquisition module can be calculated by using the time required for the signal from the ultrasonic transmission module to the ultrasonic acquisition module. When the spatial position of the ultrasonic acquisition module is fixed (i.e. the spatial coordinates of the ultrasonic acquisition module are known), the spatial coordinates of the ultrasonic transmission module can be obtained by calculating the distances from the ultrasonic transmission module to the plurality of ultrasonic acquisition modules. It is noted that positioning an ultrasound emission module in a two-dimensional plane requires at least three ultrasound acquisition modules. The ultrasonic emission module needs at least four ultrasonic acquisition modules in three-dimensional space, if the number of ultrasonic acquisition devices is less than four, the received information is not enough used for neural network learning, namely the neural network is difficult to learn effective information, the ultrasonic acquisition devices are too much in number, the processing amount of data can be increased, and the cost can also be increased, so that the number of the ultrasonic acquisition devices is preferably four in the application.
Along with the increase of the number of the ultrasonic acquisition modules, the system is more robust, but because unavoidable noise exists in a real system, the system can calculate a plurality of space coordinates for an ultrasonic transmitting device of the same ultrasonic transmitting module, so that the precision of the traditional ultrasonic positioning system is difficult to ensure, a large number of additional experiments are required for compensating and correcting the system, and the implementation difficulty and the implementation cost are high.
Referring to fig. 4, the structure of the neural network of the present application includes a backbone network, a coarse positioning network, and a fine positioning network. After the preprocessed signals are input into the backbone network, the output signals of the backbone network are input into the coarse positioning network and the fine positioning network, and the output signals of the coarse positioning network are input into the fine positioning network.
When the accuracy is required to be performed in a centimeter-level range, the fine positioning network of the neural network can not work, and the position information can be directly output through the coarse positioning network of the neural network.
Referring to fig. 5, the backbone network structure consists of five bottleneck structures, one global averaging pooling layer, and two attention modules. The attention module includes a channel attention module and a spatial attention module. The two attention modules can enable the neural network to pay more attention to important information, and reduce attention to local invalid information, so that the purposes of simplifying a model and accelerating calculation are achieved.
Wherein, the convolution layer of the first bottleneck structure is two-dimensional convolution, and the convolution layers of other bottleneck structures are one-dimensional convolution. The global averaging pooling layer maps the output of the first bottleneck structure to [1,10000], which is then sequentially input into the remaining bottleneck structures.
The number C of convolution kernels with bottleneck structures, the size K of the convolution kernels and the value P supplemented by the convolution process are set. In the first bottleneck structure (i.e., bottleneck structure-1), C32K5 represents a convolution kernel of 32 5x5, and in the remaining bottleneck structures, K is fixedly set to 1x9 and p is set to 4, so as to prevent data from missing boundary data and supplementing the data due to convolution characteristics.
The final bottleneck structure output result is multiplied by the channel attention module and then is multiplied by the space attention module, and then is output, and the final output size of the backbone network is [512,625].
Referring to fig. 6, the bottleneck structure includes 3 convolutional layers, 3 ReLU activation functions, and 1 downsampling layer. The convolution layer and the ReLU activation function are arranged in a matching way, and an output signal of the convolution layer is input into the ReLU activation function and then output; the output signals of the ReLU activation function are accumulated and then input into a downsampling layer.
Referring to fig. 7, the coarse positioning network structure includes 3 convolutional layers, 3 ReLU activation functions, and 1 adaptive average pooling layer. The convolution layer and the ReLU activation function are arranged in a matching way, and an output signal of the convolution layer is input into the ReLU activation function and then output; the output signals of the ReLU activation function are accumulated and then input into the adaptive average pooling layer.
The coarse positioning network receives the output of the backbone network and outputs a vector of shape [1,3 ]. Three elements in the vector are respectively coarse positioning space coordinates of key nodes, and are expressed by (x, y, z) coordinates, and the precision is in the centimeter level.
Referring to fig. 8, the fine positioning network structure contains 5 convolutional layers, 5 ReLU activation functions, and 1 adaptive average pooling layer. The convolution layer and the ReLU activation function are arranged in a matching way, and an output signal of the convolution layer is input into the ReLU activation function and then output; the output signals of the ReLU activation function are accumulated and then input into the adaptive average pooling layer.
The fine positioning network receives a matrix formed by splicing the output of the main network and the coarse positioning network. The shape of the matrix is [515,625], the front 512 acts as the output matrix of the backbone network, and the back 3 acts are repeated by 3 coarsely positioned space coordinates. The output of the fine positioning network is a matrix of 1 [3, 11 ]. Where the location of the maximum element represents the output of the fine positioning. The 11 elements of the output vector respectively correspond to integers ranging from-5 to 5, and represent the correction of the positioning output result of the coarse positioning neural network under millimeter precision.
The characteristic information is extracted through the main network of the neural network, the coarse positioning network of the neural network outputs coordinates, and the coarse positioning network can output the position information of the key nodes through effective training due to the fact that the precision is in the centimeter level.
The fine positioning network of the neural network receives the position information output by the coarse positioning network and the characteristic information extracted by the main network, and the effective correction of the positioning error of the coarse positioning network is realized by classifying the offset under the centimeter precision.
Specifically, assume that the output of the coarse positioning network is
The output of the fine positioning network is
The neural network finally outputs the result
The method comprises the following steps:
wherein the method comprises the steps of
Respectively is
The coordinates on different coordinate axes are respectively obtained by the following formulas:
wherein the method comprises the steps of
All are in the three-dimensional coordinate form:
wherein, the liquid crystal display device comprises a liquid crystal display device,
、
and
respectively [3, 11] of fine positioning network output]The position numbers of the maxima in the first, second and third rows of the matrix. The method adopts a neural network (an artificial intelligence technology) as a signal processing means of the calculation back end, measures the distance from an ultrasonic generating system at a specific position of a component to an ultrasonic receiving system in an assembly and welding site, and positions the spatial coordinates of the component. The method disclosed by the application has higher precision and adaptability compared with the traditional measurement positioning method due to the strong learning capability of the deep neural network. In addition, the method only needs to collect limited component positioning sample data for fine adjustment training, can be directly used for positioning of components, avoids the defect that an algorithm model needs to be compensated and corrected before use, and is low in implementation difficulty and cost.
The neural network adopted by the application is a mathematical model with learning capability. In training, the neural network calculates an output result according to the input sample, and adjusts internal parameters according to the output result and the error of the sample label. Thus, the neural network can learn how to go from data to the desired result during the training process without having to manually design its computational flow.
In the application, a sample set required by neural network training can be constructed by placing an ultrasonic transmitting module at a preset space coordinate and receiving a waveform signal transmitted by the ultrasonic transmitting module. The sample is a preprocessed received waveform signal, and the sample label is a preset space coordinate. The positioning system provided by the application can automatically learn how to accurately position the space coordinates of the ultrasonic emission source by utilizing the received waveform signals acquired by a plurality of (at least four) ultrasonic receiving devices in a supervised learning mode, thereby avoiding a great amount of extra experiments of the traditional positioning system from compensating and correcting the system and reducing the implementation difficulty and cost.
In practical application, acquired ultrasonic signal samples (corresponding to different ultrasonic transmitting devices) in different frequency bands are respectively input into a trained neural network, so that the space coordinates of the different ultrasonic transmitting devices can be obtained.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
Referring to fig. 9, in one embodiment of the present application, a positioning system is provided, which is used in conjunction with the positioning method of the above-described embodiment. The positioning system comprises:
an ultrasonic transmitting module: a plurality of first chaotic ultrasonic signals with different frequency bands are sent, and the first chaotic ultrasonic signals are generated and transmitted by an ultrasonic transmitting module on a component in combination with a chaotic system;
an ultrasonic acquisition module: receiving a plurality of corresponding second chaotic ultrasonic signals formed by a plurality of first chaotic ultrasonic signals propagated in the air;
and a data preprocessing module: splicing the received second chaotic ultrasonic signals to obtain spliced signals;
deep neural network module: calculating the position information of an ultrasonic transmitting module corresponding to the first chaotic ultrasonic signal by adopting a neural network according to the spliced signal; the neural network comprises a main network, a coarse positioning network and a fine positioning network, and output signals of the main network are input into the coarse positioning network and the fine positioning network; the output signal of the coarse positioning network is input into the fine positioning network;
the position information post-processing module: acquiring posture information according to the position information, and acquiring a component posture adjustment instruction and a displacement instruction according to the position information and the posture information;
component alignment module: and receiving the gesture adjustment instruction and the displacement instruction, and controlling gesture adjustment and displacement of a component.
Further, the ultrasonic transmitting module comprises at least four ultrasonic transmitting devices, and different ultrasonic transmitting devices transmit a plurality of first chaotic ultrasonic signals with different frequency bands.
The ultrasonic acquisition module comprises at least four ultrasonic receiving devices, and each ultrasonic receiving device is provided with a plurality of signal acquisition channels.
The above-described modules of the positioning system may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment of the present application, a computer device is provided, which may be a server. The computer device includes a processor, a memory, and a network interface connected by a system bus. Among them, processors include, but are not limited to, CPUs (central processing units), GPUs (graphics processors), the processors of which are used to provide computing and control capabilities. The memory of the computer device may be implemented by any type of volatile or nonvolatile memory device, including but not limited to: magnetic disk, optical disk, EEPROM (Electrically erasable programmable Read Only Memory), EPROM (Erasable Programmable Read Only Memory ), SRAM (Static Random Access Memory, static random Access Memory), ROM (Read-Only Memory), magnetic Memory, flash Memory, PROM (Programmable Read-Only Memory). The memory of the computer device provides an environment for the running of an operating system and computer programs stored therein. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when being executed by a processor, implements the positioning method steps described in the above embodiments.
In one embodiment of the present application, a computer readable storage medium is provided, which stores a computer program which, when executed by a processor, implements the positioning method steps described in the above embodiments. The computer readable storage medium includes ROM (Read-only memory), RAM (Random-access memory), CD-ROM (compact disc Read-only memory), magnetic disk, floppy disk, and the like.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the system described in the present application is divided into different functional units or modules to perform all or part of the above-described functions.
Claims (13)
1. A method of positioning, the method comprising:
a plurality of first chaotic ultrasonic signals with different frequency bands are sent, and the first chaotic ultrasonic signals are generated and transmitted by an ultrasonic transmitting module on a component in combination with a chaotic system;
receiving a plurality of corresponding second chaotic ultrasonic signals formed by a plurality of first chaotic ultrasonic signals propagated in the air;
performing splicing processing on the plurality of second chaotic ultrasonic signals to obtain spliced signals;
calculating the position information of an ultrasonic transmitting module corresponding to the first chaotic ultrasonic signal by adopting a neural network according to the spliced signal; the neural network comprises a main network, a coarse positioning network and a fine positioning network, and output signals of the main network are input into the coarse positioning network and the fine positioning network; the output signal of the coarse positioning network is input into the fine positioning network;
acquiring posture information according to the position information, and acquiring a component posture adjustment instruction and a displacement instruction according to the position information and the posture information;
and controlling the component posture adjustment and displacement according to the component posture adjustment command and the displacement command.
2. The positioning method according to claim 1, wherein the backbone network consists of five bottleneck structures, one global averaging pooling layer and two attention modules;
wherein, the convolution layer of the first bottleneck structure is two-dimensional convolution, and the convolution layers of the rest bottleneck structures are one-dimensional convolution;
the output signal of the first bottleneck structure is input into the global average pooling layer and then sequentially input into the rest bottleneck structures;
the output signal of the last bottleneck structure is sequentially input to the attention module.
3. The positioning method according to claim 2, wherein the bottleneck structure comprises 3 convolution layers, 3 ReLU activation functions and 1 downsampling layer;
the convolution layer and the ReLU activation function are arranged in a matched mode, and an output signal of the convolution layer is input into the ReLU activation function and then output;
and accumulating the output signals of the ReLU activation function and inputting the accumulated output signals into the downsampling layer.
4. The positioning method according to claim 1, wherein the coarse positioning network comprises 3 convolutional layers, 3 ReLU activation functions, and 1 adaptive averaging pooling layer;
the convolution layer and the ReLU activation function are arranged in a matched mode, and an output signal of the convolution layer is input into the ReLU activation function and then output;
and accumulating the output signals of the ReLU activation function and inputting the accumulated signals into the adaptive average pooling layer.
5. The positioning method according to claim 1, wherein the fine positioning network comprises 5 convolutional layers, 5 ReLU activation functions, and 1 adaptive averaging pooling layer;
the convolution layer and the ReLU activation function are arranged in a matched mode, and an output signal of the convolution layer is input into the ReLU activation function and then output;
and accumulating the output signals of the ReLU activation function and inputting the accumulated signals into the adaptive average pooling layer.
6. The positioning method according to claim 1, wherein the receiving a plurality of corresponding second chaotic ultrasonic signals formed by the plurality of first chaotic ultrasonic signals propagated in the air is:
and setting a receiving window with the length longer than that of the first chaotic ultrasonic signals so as to receive a plurality of corresponding second chaotic ultrasonic signals formed by a plurality of first chaotic ultrasonic signals propagated in the air.
7. The positioning method according to claim 6, wherein the performing the splicing processing on the plurality of second chaotic ultrasonic signals to obtain a spliced signal includes:
zero padding is carried out on the pre-stored first chaotic ultrasonic signal, so that the length of the pre-stored first chaotic ultrasonic signal is the same as that of the second chaotic ultrasonic signal;
and splicing the first chaotic ultrasonic signal subjected to zero padding with a second chaotic ultrasonic signal with the same frequency band to obtain a spliced signal.
8. The positioning method according to claim 7, wherein the calculating, by using a neural network, the position information of the ultrasonic transmitting module corresponding to the first chaotic ultrasonic signal according to the spliced signal includes:
the method comprises the steps of constructing a sample set by placing an ultrasonic transmitting module at a preset space coordinate and receiving a transmitting waveform signal of the ultrasonic transmitting module;
training the neural network through the sample set in a supervised learning mode;
and inputting the spliced signals into the trained neural network to obtain the position information of the ultrasonic transmitting module corresponding to the first chaotic ultrasonic signal.
9. A positioning system, the system comprising:
an ultrasonic transmitting module: a plurality of first chaotic ultrasonic signals with different frequency bands are sent, and the first chaotic ultrasonic signals are generated and transmitted by an ultrasonic transmitting module on a component in combination with a chaotic system;
an ultrasonic acquisition module: receiving a plurality of corresponding second chaotic ultrasonic signals formed by a plurality of first chaotic ultrasonic signals propagated in the air;
and a data preprocessing module: splicing the received second chaotic ultrasonic signals to obtain spliced signals;
deep neural network module: calculating the position information of an ultrasonic transmitting module corresponding to the first chaotic ultrasonic signal by adopting a neural network according to the spliced signal; the neural network comprises a main network, a coarse positioning network and a fine positioning network, and output signals of the main network are input into the coarse positioning network and the fine positioning network; the output signal of the coarse positioning network is input into the fine positioning network;
the position information post-processing module: acquiring posture information according to the position information, and acquiring a component posture adjustment instruction and a displacement instruction according to the position information and the posture information;
component alignment module: and receiving the gesture adjustment instruction and the displacement instruction, and controlling gesture adjustment and displacement of the component.
10. The positioning system of claim 9, wherein the ultrasound transmission module comprises at least four ultrasound transmission devices, different ones of the ultrasound transmission devices transmitting a plurality of first chaotic ultrasound signals of different frequency bands.
11. The positioning system of claim 10 wherein said ultrasound acquisition module comprises at least four stationary ultrasound receiving devices within a field, each of said ultrasound receiving devices being provided with a plurality of signal acquisition channels.
12. A computer device comprising a memory, a processor and a computer program stored in the memory and running on the processor, the processor implementing the steps of the positioning method according to any of claims 1 to 8 when the computer program is executed.
13. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the steps of the positioning method according to any of claims 1 to 8.
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