KR20220062857A - Trajectory prediction method and apparatus - Google Patents

Abstract

Disclosed are a method and device for predicting a trajectory. The method for predicting the trajectory according to one embodiment comprises: a step of detecting inertia data of a projecting body; a step of acquiring an initial launch condition of the projecting body based on the inertia data; a step of acquiring a reference trajectory of the projecting body; and a step of predicting a trajectory of the projecting body by inputting the initial launch condition and the reference trajectory into at least one neural network.

Description

Trajectory prediction method and apparatus {TRAJECTORY PREDICTION METHOD AND APPARATUS}

Embodiments relate to a trajectory prediction method and apparatus using a neural network.

Various techniques exist for estimating the trajectory of a flying object or projectile. The estimation method using the inertial sensor mathematically calculates and provides information on the position and attitude of the projectile at every point in time by using the acceleration and angular velocity information under the assumption that the initial state of the vehicle or the projectile is known.

GNSS (Global Navigation Satellite System) uses satellite signals to mathematically determine the user's location, and motion capture analyzes the image captured by a camera by attaching a discrimination device to the target to be measured, thereby providing location and posture information of the target. way to obtain it.

However, these trajectory estimation methods have problems in that expensive equipment is required and prediction performance for an object having a limited environment or rapid movement is very poor.

For example, since projectiles such as discs, baseballs, and shells are small in size and move at the initial stage of launch is very large, accurate trajectory estimation is difficult with the above-described trajectory estimation methods due to problems such as exceeding the measurement range, signal omission, and miniaturization. impossible.

The above-described projectiles have a characteristic in which a flight trajectory is determined depending on initial conditions, unlike an aircraft, a drone, and the like. Therefore, if an accurate aerodynamic model of the projectile and sufficient initial launch conditions exist, the flight trajectory of the projectile can be predicted thereafter.

However, in the case of the above-described projectiles, the aerodynamic model is different depending on the shape, and the process for calculating the aerodynamic model is very complicated and consuming.

Embodiments may provide a technique for predicting a trajectory using a neural network. However, the technical tasks are not limited to the above-described technical tasks, and other technical tasks may exist.

A trajectory prediction method according to an embodiment includes the steps of detecting inertial data of a projectile, obtaining an initial firing condition of the projectile based on the inertial data, and obtaining a reference trajectory of the projectile and predicting the trajectory of the projectile by inputting the initial firing condition and the reference trajectory into at least one neural network.

The initial firing condition may include a posture of the projectile, a speed of the projectile, a rotation speed of the projectile, and an initial position of the projectile.

The acquiring of the reference trajectory may include acquiring a function corresponding to the reference trajectory based on the position of the projectile.

The function may include a polynomial.

The number of the at least one neural network may be determined based on the degree of the polynomial.

The predicting may include extracting a trajectory coefficient representing the trajectory based on the reference trajectory, and predicting the trajectory based on the initial launch condition and the trajectory coefficient.

The trajectory prediction method may further include learning the at least one neural network based on the initial launch condition and the reference trajectory.

The training may include training the at least one neural network to output a trajectory coefficient based on the reference trajectory by inputting the initial launch condition.

A trajectory prediction apparatus according to an embodiment includes a sensor for detecting inertial data of a projectile, obtaining an initial firing condition of the projectile based on the inertial data, obtaining a reference trajectory of the projectile, and obtaining the initial and a processor for predicting a trajectory of the projectile by inputting a firing condition and the reference trajectory into at least one neural network.

The initial firing condition may include a posture of the projectile, a speed of the projectile, a rotation speed of the projectile, and an initial position of the projectile.

The processor may obtain a function corresponding to the reference trajectory based on the position of the projectile.

The function may include a polynomial.

The number of the at least one neural network may be determined based on the degree of the polynomial.

The processor may extract a trajectory coefficient representing the trajectory based on the reference trajectory, and predict the trajectory based on the initial launch condition and the trajectory coefficient.

The processor may train the at least one neural network based on the initial launch condition and the reference trajectory.

The processor may train the at least one neural network to output a trajectory coefficient based on the reference trajectory by inputting the initial launch condition.

1 is a schematic block diagram of an apparatus for predicting a trajectory according to an embodiment.
Fig. 2 shows a schematic block diagram of the processor shown in Fig. 1;
3 shows an example of a neural network used for trajectory prediction.
4 shows an example of a reference trajectory.
5 shows a process of predicting a trajectory through a plurality of neural networks.
6 shows an example of a predicted trajectory.
FIG. 7 shows an operation sequence of the trajectory prediction apparatus shown in FIG. 1 .

Specific structural or functional descriptions of the embodiments are disclosed for purposes of illustration only, and may be changed and implemented in various forms. Accordingly, the actual implementation form is not limited to the specific embodiments disclosed, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical spirit described in the embodiments.

Although terms such as first or second may be used to describe various elements, these terms should be interpreted only for the purpose of distinguishing one element from another. For example, a first component may be termed a second component, and similarly, a second component may also be termed a first component.

When a component is referred to as being “connected to” another component, it may be directly connected or connected to the other component, but it should be understood that another component may exist in between.

The singular expression includes the plural expression unless the context clearly dictates otherwise. In this specification, terms such as "comprise" or "have" are intended to designate that the described feature, number, step, operation, component, part, or combination thereof exists, and includes one or more other features or numbers, It should be understood that the possibility of the presence or addition of steps, operations, components, parts or combinations thereof is not precluded in advance.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present specification. does not

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. In the description with reference to the accompanying drawings, the same components are assigned the same reference numerals regardless of the reference numerals, and overlapping descriptions thereof will be omitted.

1 is a schematic block diagram of a trajectory prediction apparatus according to an embodiment, and FIG. 2 is a schematic block diagram of a processor shown in FIG. 1 .

1 and 2 , the trajectory prediction apparatus 10 may predict a trajectory of a projectile or a flying object. The trajectory prediction apparatus 10 may predict the trajectory of the projectile using a neural network.

Neural networks (or artificial neural networks) can include statistical learning algorithms that mimic the neurons of biology in machine learning and cognitive science. A neural network may refer to an overall model having problem-solving ability by changing the bonding strength of synapses through learning in which artificial neurons (nodes) formed a network by bonding of synapses.

The neural network may include a deep neural network. Neural networks include Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Perceptron, Feed Forward (FF), Radial Basis Network (RBF), Deep Feed Forward (DFF), Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Auto Encoder (AE), Variational Auto Encoder (VAE), Denoising Auto Encoder (DAE), Sparse Auto Encoder (SAE), Markov Chain (MC), Hopfield Network (HN), Boltzmann Machine (BM) ), Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Deep Convolutional Network (DCN), Deconvolutional Network (DN), Deep Convolutional Inverse Graphics Network (DCIGN), Generative Adversarial Network (GAN), Liquid State Machine (LSM) ), Extreme Learning Machine (ELM), Echo State Network (ESN), Deep Residual Network (DRN), Differential Neural Computer (DNC), Neural Turning Machine (NTM), Capsule Network (CN), Kohonen Network (KN), and AN (Attention Network) may be included.

The machine reading apparatus 10 may be implemented in the form of a mobile terminal. For example, the machine reading device 10 may be implemented as an IoT device, a machine-type communication device, or a portable electronic device.

Portable electronic devices include laptop computers, mobile phones, smart phones, tablet PCs, mobile internet devices (MIDs), personal digital assistants (PDAs), and enterprise digital assistants (EDAs). ), digital still camera, digital video camera, PMP (portable multimedia player), PND (personal navigation device or portable navigation device), handheld game console, e-book (e-book), may be implemented as a smart device. For example, the smart device may be implemented as a smart watch or a smart band.

The trajectory prediction apparatus 10 includes a processor 200 . The trajectory prediction apparatus 10 may further include a sensor 100 and a memory 300 .

The sensor 100 may be located inside or outside the trajectory prediction apparatus 10 . The sensor 100 may detect the movement of the projectile. The sensor 100 may detect inertial data of the projectile. The inertial data may include acceleration and angular velocity. For example, the sensor 100 may include an acceleration sensor, an angular velocity sensor, and a magnetic sensor. The sensor 100 may output the sensed inertia data to the processor 200 .

The processor 200 may process data stored in the memory 300 . The processor 200 may execute computer readable codes (eg, software) stored in the memory 300 and instructions induced by the processor 200 .

The “processor 200” may be a data processing device implemented in hardware having circuitry having a physical structure for performing desired operations. For example, desired operations may include code or instructions included in a program.

For example, a data processing device implemented as hardware includes a microprocessor, a central processing unit, a processor core, a multi-core processor, and a multiprocessor. , an Application-Specific Integrated Circuit (ASIC), and a Field Programmable Gate Array (FPGA).

The processor 200 may predict the trajectory of the projectile based on the inertia data. The processor 200 may obtain an initial firing condition of the projectile based on the inertia data. The initial firing condition of the projectile may include a pose of the projectile, a velocity of the projectile, a rotational velocity of the projectile, and an initial position of the projectile.

The processor 200 may acquire a reference trajectory of the projectile. The processor 200 may obtain a function corresponding to the reference trajectory based on the position of the projectile. The function corresponding to the reference trajectory may include a polynomial.

The processor 200 may predict the trajectory of the projectile by inputting the initial launch condition and the reference trajectory to at least one neural network. In other words, the processor 200 may predict the trajectory of the projectile using one or more neural networks.

The number of at least one neural network may be determined based on the degree of the polynomial. The processor 200 may extract a trajectory coefficient indicating the trajectory of the projectile based on the reference trajectory. The processor 200 may predict the trajectory of the projectile based on the initial firing condition and the trajectory coefficient. A process in which the processor 200 extracts the trajectory coefficient will be described in detail with reference to FIGS. 3 to 5 .

The processor 200 may train at least one neural network. The processor 200 may train at least one neural network based on the initial launch condition and the reference trajectory. The processor 200 may train at least one neural network to output a trajectory coefficient based on a reference trajectory by inputting an initial launch condition.

The processor 200 may include a reference trajectory measurer 210 , a reference trajectory preprocessor 220 , an inertial data preprocessor 230 , a learner 240 , and a trajectory predictor 250 . The sensor 100 may detect inertial data and output it to the inertial data preprocessor 220 .

The reference trajectory measurer 210 may acquire a reference trajectory for learning the neural network. The reference trajectory preprocessor 220 may extract trajectory coefficients based on the obtained reference trajectory. The reference trajectory preprocessor 220 may extract a trajectory coefficient by processing the reference trajectory of the projectile.

The inertial data preprocessor 220 may obtain an initial launch condition based on the inertial data. The inertia data preprocessor 220 may acquire the posture, speed, rotation speed, and initial position of the projectile at a specific point in time by using the received inertia data.

The learner 240 may train the neural network. The learner 240 may train the neural network based on the initial launch condition and the trajectory coefficient input from the reference trajectory preprocessor 220 and the inertial data preprocessor 230 .

In the neural network, the number of hidden layers and the type of neurons may vary depending on the type of input initial firing condition, the size of the input data, and the characteristics of the projectile. The learner 240 may generate an input/output model for predicting a trajectory by learning the neural network.

The trajectory predictor 250 may predict the trajectory of the projectile by using the input/output model generated by the learner 240 .

The memory 300 may store instructions (or programs) executable by the processor 200 . For example, the instructions may include instructions for executing an operation of a processor and/or an operation of each component of the processor.

The memory 300 may be implemented as a volatile memory device or a nonvolatile memory device.

The volatile memory device may be implemented as dynamic random access memory (DRAM), static random access memory (SRAM), thyristor RAM (T-RAM), zero capacitor RAM (Z-RAM), or twin transistor RAM (TTRAM).

Nonvolatile memory devices include EEPROM (Electrically Erasable Programmable Read-Only Memory), Flash memory, MRAM (Magnetic RAM), Spin-Transfer Torque (STT)-MRAM (Spin-Transfer Torque (STT)-MRAM), Conductive Bridging RAM (CBRAM) , FeRAM(Ferroelectric RAM), PRAM(Phase change RAM), Resistive RAM(RRAM), Nanotube RRAM(Nanotube RRAM), Polymer RAM(Polymer RAM(PoRAM)), Nano Floating Gate Memory (NFGM)), a holographic memory, a molecular electronic memory device, or an Insulator Resistance Change Memory.

Hereinafter, a process in which the processor 200 predicts the trajectory of the projectile will be described in detail with reference to FIGS. 3 to 6 .

3 shows an example of a neural network used for trajectory prediction, and FIG. 4 shows an example of a reference trajectory.

5 shows a process of predicting a trajectory through a plurality of neural networks, and FIG. 6 shows an example of the predicted trajectory.

3 to 6 , the processor 200 may predict a trajectory of a projectile using a neural network. The processor 200 may extract a trajectory coefficient indicating the trajectory of the projectile through the neural network.

The neural network may include a hidden layer 310 and an output layer 330 . The neural network may include a plurality of hidden layers 310 .

The processor 200 may input the initial firing condition of the projectile obtained from the inertial data to the neural network. For example, the processor 200 may input the posture, speed, rotation speed, and initial position of the projectile into the neural network to extract a trajectory coefficient representing the trajectory of the projectile.

The processor 200 may input an initial firing condition of the projectile to the neural network. As described above, the initial firing conditions may include the posture, speed, rotation speed, and initial position of the projectile obtained based on the inertial data.

The processor 200 may change the number of hidden layers 310 of the neural network according to an embodiment. The hidden layer 310 may include neurons in a sigmoid type.

The output layer 330 may include linear neurons. The output layer 330 may extract a trajectory coefficient by using the calculation result of the hidden layer 310 . After the learning of the neural network is completed, the processor 200 may predict the trajectory of the projectile using only the initial launch condition obtained from the inertial data preprocessor 220 .

The reference trajectory measurer 210 and the reference trajectory preprocessor 220 may generate training data for learning the neural network. The reference trajectory measurer 210 may be implemented using a platform such as motion capture or Global Navigation Satellite System (GNSS).

The reference trajectory generated by the reference trajectory measurer 210 may be expressed as a coordinate value. For example, when the reference trajectory appears on a two-dimensional coordinate space, the coordinate value may be expressed as (x ₁ , y ₁ ), and when the reference trajectory appears on a three-dimensional coordinate space, (x ₁ , y ) ₁ , z ₁ ).

The predicted trajectory of the projectile may have the same dimension as the reference trajectory. The example of FIG. 4 shows an example of a reference trajectory displayed on a two-dimensional coordinate space.

The reference trajectory measurer 210 may acquire coordinates given as points as shown in the left graph of FIG. 4 . For example, the reference trajectory measuring instrument 210 is (x ₁ , y ₂ ), (x ₂ , y ₂ )… Coordinates such as (x _n ,y _n ) can be obtained.

The reference trajectory preprocessor 220 may obtain a reference trajectory as shown in the graph on the right of FIG. 4 using coordinates given as points. The reference trajectory preprocessor 220 may extract the trajectory coefficients after converting the coordinate values obtained by the reference trajectory measuring device 210 into a curve equation through curve fitting.

The reference trajectory preprocessor 220 may acquire the reference trajectory in the form of a function corresponding to the reference trajectory. For example, the reference trajectory preprocessor 220 may obtain the trajectory coefficients from the equations or polynomials of the transformed curve.

For example, the polynomial may have the form y=a ₁ x ² +a ₂ x+b, and the reference trajectory preprocessor 220 may obtain trajectory coefficients a1 and a2 using curve fitting.

The dimension of the polynomial may be changed according to the complexity of the trajectory. A second-order polynomial may have two trajectory coefficients, and a third-order polynomial may have three trajectory coefficients. In other words, as the complexity of the trajectory increases, the number of trajectory coefficients may increase.

The number of neural networks used by the processor 200 to predict a trajectory may vary according to the number of trajectory coefficients. As in the example of FIG. 5 , the number of neural networks may be the same as the number of trajectory coefficients.

In other words, when there are two trajectory coefficients to be extracted, two neural networks of FIG. 3 may be required, and when there are three trajectory coefficients, three neural networks may be needed.

The processor 200 may predict the trajectory of the projectile by using the plurality of extracted trajectory coefficients. After the trajectory coefficient is determined through learning, the processor 200 may predict the trajectory of the projectile by inputting only inertia data.

The example of FIG. 6 shows an example of the trajectory of the projection predicted by the trajectory prediction apparatus 10 . The trajectory prediction apparatus 10 may predict the flight trajectory of the projectile by using only the inertial sensor as an input when using a neural network that has been trained.

Since the trajectory prediction apparatus 10 uses only the inertial sensor, it is not restricted by the surrounding environment, and can be miniaturized and implemented as a low-cost platform.

When measuring equipment using a camera, such as a bicon or motion capture, is used to measure the flight trajectory of a projectile, the flight trajectory can be estimated only in a special environment in which a plurality of cameras are installed, but the trajectory prediction device 10 has completed learning By predicting the trajectory using the neural network, it is possible to predict the trajectory of a projectile equipped with only an inertial sensor without restrictions on the surrounding environment.

FIG. 7 shows an operation sequence of the trajectory prediction apparatus shown in FIG. 1 .

The sensor 100 may detect inertial data of the projectile ( 710 ).

The processor 200 may obtain an initial firing condition of the projectile based on the inertia data ( 730 ). The initial firing conditions may include an attitude of the projectile, a speed of the projectile, a rotation speed of the projectile, and an initial position of the projectile.

The processor 200 may obtain a reference trajectory of the projectile. The processor 200 may obtain a function corresponding to the reference trajectory based on the position of the projectile. Functions can contain polynomials.

The processor 200 may predict the trajectory of the projectile by inputting the initial launch condition and the reference trajectory to at least one neural network. The number of at least one neural network may be determined based on the degree of the polynomial.

The processor 200 may extract a trajectory coefficient representing the trajectory based on the reference trajectory. The processor 200 may predict the trajectory based on the initial launch condition and the trajectory coefficient.

The processor 200 may train at least one neural network based on the initial launch condition and the reference trajectory. The processor 200 may train at least one neural network to output a trajectory coefficient based on a reference trajectory by inputting an initial launch condition.

The embodiments described above may be implemented by a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the apparatus, methods, and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA) array), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions, may be implemented using a general purpose computer or special purpose computer. The processing device may execute an operating system (OS) and a software application running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that can include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

Software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or apparatus, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in a computer-readable recording medium.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or in combination, and the program instructions recorded on the medium are specially designed and configured for the embodiment, or are known and available to those skilled in the art of computer software. may be Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like.

The hardware devices described above may be configured to operate as one or a plurality of software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with reference to the limited drawings, a person skilled in the art may apply various technical modifications and variations based thereon. For example, the described techniques are performed in an order different from the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (17)

detecting inertial data of the projectile;
obtaining an initial firing condition of the projectile based on the inertial data;
obtaining a reference trajectory of the projectile; and
predicting the trajectory of the projectile by inputting the initial firing condition and the reference trajectory into at least one neural network;
A trajectory prediction method comprising
According to claim 1,
The initial firing conditions are:
The posture of the projectile, the speed of the projectile, the rotational speed of the projectile and the initial position of the projectile
A trajectory prediction method comprising
The method of claim 1,
The step of obtaining the reference trajectory is
obtaining a function corresponding to the reference trajectory based on the position of the projectile;
A trajectory prediction method comprising
4. The method of claim 3,
The function contains a polynomial
Trajectory prediction method.
5. The method of claim 4,
The number of the at least one neural network is,
determined based on the degree of the polynomial
A trajectory prediction method comprising
According to claim 1,
The predicting step is
extracting a trajectory coefficient representing the trajectory based on the reference trajectory; and
predicting the trajectory based on the initial launch condition and the trajectory coefficient
A trajectory prediction method comprising
According to claim 1,
learning the at least one neural network based on the initial firing condition and the reference trajectory;
A trajectory prediction method further comprising a.
8. The method of claim 7,
The learning step is
training the at least one neural network to input the initial firing condition and output a trajectory coefficient based on the reference trajectory;
A trajectory prediction method comprising
A computer program stored in a medium for executing the method of any one of claims 1 to 8 in combination with hardware.
a sensor that detects inertial data of the projectile; and
obtaining an initial firing condition of the projectile based on the inertia data, obtaining a reference trajectory of the projectile, and inputting the initial firing condition and the reference trajectory into at least one neural network to determine the trajectory of the projectile predictive processor
A trajectory prediction device comprising a.
11. The method of claim 10,
The initial firing conditions are:
The posture of the projectile, the speed of the projectile, the rotational speed of the projectile and the initial position of the projectile
A trajectory prediction device comprising a.
11. The method of claim 10,
The processor is
to obtain a function corresponding to the reference trajectory based on the position of the projectile
Trajectory prediction device.
13. The method of claim 12,
The function contains a polynomial
Trajectory prediction device.
14. The method of claim 13,
The number of the at least one neural network is,
determined based on the degree of the polynomial
Trajectory prediction device.
11. The method of claim 10,
The processor is
extracting a trajectory coefficient representing the trajectory based on the reference trajectory,
predicting the trajectory based on the initial launch condition and the trajectory coefficient
Trajectory prediction device.
11. The method of claim 10,
The processor is
learning the at least one neural network based on the initial firing condition and the reference trajectory.
Trajectory prediction device.
17. The method of claim 16,
The processor is
learning the at least one neural network to input the initial firing condition and output a trajectory coefficient based on the reference trajectory.
Trajectory prediction device.

Images