CN112651456A - Unmanned vehicle control method based on RBF neural network - Google Patents
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Abstract
The invention relates to the technical field of vehicle control, in particular to an unmanned vehicle control method based on a RBF neural network, which comprises the following steps: s100: acquiring barrier data and preprocessing the barrier data; s200: establishing a control model based on an RBF neural network model; s300: constructing a sample training sample set, and training a control model; s400: and inputting the preprocessed barrier data into a control model for processing, and outputting control parameters. According to the unmanned vehicle control method based on the RBF neural network, the control quantity of speed and angle can be generated according to the distance and angle information of the obstacle, and intelligent control is further realized; the processing logic and complexity of the sensor data in the obstacle avoidance control process can be simplified; when the sensor changes, other changes to the algorithm of the control logic are not needed, the universality is strong, and the expansion and maintenance are easy.
Description
Technical Field
The invention relates to the technical field of vehicle control, in particular to an unmanned vehicle control method based on a RBF neural network.
Background
With the development of the internet of things and internet technology, intelligent robots or intelligent vehicles are widely applied to scenes such as exhibition hall navigation, greeting and answering, workshop management, warehousing management, freight logistics, intelligent home and the like.
The mobile control is one of the core technologies of the intelligent vehicle, and in the prior art, the movement and the path of the intelligent vehicle are mainly controlled by setting a mark in a fixed line or a scene for identification and the like. The method mainly comprises the steps of moving based on an obstacle avoidance algorithm aiming at an application environment without a preset scene, detecting the distribution of obstacles around a vehicle through sensors, and judging whether the moving direction needs to be adjusted according to data of each sensor.
Disclosure of Invention
The invention aims to provide an unmanned vehicle control method based on a RBF neural network, which can fully utilize environmental data through a neural network model, analyze and output walking control parameters based on the environmental data, has high response speed and is suitable for scenes with fast environmental change and fast vehicle speed.
The application provides the following technical scheme:
an unmanned vehicle control method based on an RBF neural network comprises the following steps:
s100: acquiring barrier data and preprocessing the barrier data;
s200: establishing a control model based on an RBF neural network model;
s300: constructing a sample training sample set, and training a control model;
s400: and inputting the preprocessed barrier data into a control model for processing, and outputting control parameters.
Further, the preprocessing in S100 includes:
s101: acquiring data of each sensor;
s102: carrying out filtering processing on data of each sensor, wherein the filtering processing adopts a Kalman filtering algorithm;
s103: and carrying out data fusion on the data of each sensor to obtain the data of the obstacle.
Further, in S200, the control model includes an input layer, a hidden layer, and an output layer, where the number of neurons in the input layer corresponds to the number of sensors; the hidden layer adopts a Gaussian radial basis function as an activation function; the output layer includes two neurons that output control target amounts for the vehicle speed and the angular speed, respectively.
Further, S300 includes:
s301: initializing neural network parameters, and configuring learning rate and iteration precision;
s302: calculating a root mean square error value output by the network, if the root mean square error value is less than or equal to the iteration precision, ending the training, otherwise executing S303;
s303: the weight parameter, the center parameter, and the width parameter of the neural network model are iteratively trained using a gradient descent method, and then S302 is performed.
Further, in S303, the weight parameter, the center parameter, and the width parameter are adjusted according to the following formulas:
wherein, ω isji(t) weight parameters between the jth output layer neuron and the ith hidden layer neuron at the time of the t iterative computation; c. Cik(t) central parameters for the ith hidden layer neuron to the kth input layer neuron at the tth iteration; dikIs and center cik(t) width corresponding toA parameter; eta is a learning factor;
i is an integer of 1 to ni，niNumber of hidden layer neurons; j is 1, 2; k is an integer of 1 to nk，nkThe number of neurons in the input layer; 0<η<1；
E is the cost function of the RBF neural network,
Oijfor the j output layer neuron's expected value at the i hidden layer neuron input sample; y isijThe output value of the jth output neuron when the sample is input to the ith hidden layer neuron.
Further, unmanned car includes two drive wheels, unmanned car controls through the speed difference of two drive wheels and turns to the angle, still includes:
s500: acquiring the speed and angular speed of a current vehicle;
s600: the control of the two drive wheels is performed based on the vehicle speed, the target amount of angular velocity, the current vehicle speed, and the angular velocity of the output layer.
Further, still include:
s700: recording data of each sensor and corresponding vehicle speed and angular velocity to form a data set;
s800: screening abnormal data in the data set according to a data screening rule;
s900: correcting the abnormal data, and constructing a corrected data set according to the abnormal data correction result;
s1000: and performing iterative training on the control model by correcting the data set.
The technical scheme of the invention has the beneficial effects that:
in the technical scheme of the invention, the barrier data are analyzed by adopting the control model based on the RBF neural network model, and the control quantity of speed and angle can be generated according to the distance and angle information of the barrier, so that intelligent control is realized. The environment data can be fully utilized through the neural network model, the walking control parameters are analyzed and output based on the environment data, the response speed is high, and the method is suitable for scenes with fast environment change and fast vehicle speed.
In the technical scheme of the invention, the data of each sensor is used as input, and the control result is output through the control model, so that the processing logic and complexity of the sensor data in the obstacle avoidance control process can be simplified; when the sensor changes, such as when the sensor is added or removed, the number of neurons in the input layer is adjusted to train again, other changes to the algorithm of the control logic are not needed, the expansion and maintenance are easy, the universality is strong, and the method can be applied to intelligent vehicles with different hardware.
According to the technical scheme, sensor data and vehicle speed and angular speed data in the operation process are recorded, a correction data set is constructed by screening and correcting abnormal data, and the control model is subjected to iterative training through the correction data set, so that the control model can be continuously iterated in the use process, and further the control is more accurate.
Drawings
FIG. 1 is a control model structure diagram in an embodiment of the unmanned vehicle control method based on an RBF neural network;
fig. 2 is a schematic simulation operation diagram in the embodiment of the unmanned vehicle control method based on the RBF neural network.
Detailed Description
The technical scheme of the application is further explained in detail through the following specific implementation modes:
example one
The unmanned vehicle control method based on the RBF neural network disclosed by the embodiment comprises the following steps:
s100: sensor data is acquired and pre-processed.
S200: and establishing a control model based on the RBF neural network model.
S300: and constructing a sample training sample set, and training the control model.
S400: and inputting the preprocessed data into a control model for processing, and outputting control parameters.
In the embodiment, the unmanned vehicle comprises a vehicle body, a chassis of the vehicle body is provided with a left driving wheel and a right driving wheel, and the unmanned vehicle controls the steering angle through the speed difference of the two driving wheels, so that the driving wheels are used for driving and steering; on the vehicle body, a laser radar is arranged as a main sensor. The device is provided with five laser radars, the angle difference between every two laser radars is 45 degrees, and the barrier distances in the right left direction, the left front direction, the right front direction and the right direction of the trolley are respectively detected. The vehicle body is also provided with 32-bit ARM core processor, motor, GPS positioning module and other circuit modules or devices.
In this embodiment, the preprocessing in S100 includes:
s101: acquiring data of each sensor, namely acquiring data of five laser radar sensors;
s102: filtering data of each sensor, wherein the filtering is performed by adopting a Kalman filtering algorithm in the embodiment to eliminate illumination influence and Gaussian noise influence;
s103: and performing data fusion on the data of each sensor.
In S200, as shown in fig. 1, the control model includes an input layer, a hidden layer, and an output layer, where the number of neurons in the input layer corresponds to the number of sensors; in other words, in the present application, the input layer is composed of five neurons, data detected by five lidar signals is used as a signal source node, and transmitted information is a distance and an angle of an environmental obstacle.
The hidden layer adopts a Gaussian radial basis function
As an activation function; the laser radar data processing system is composed of six neurons and is used for carrying out nonlinear change on input laser radar data.
The output layer comprises two neurons for performing linear weighted output on the information output by the hidden layer neurons, and the two neurons respectively output control quantities of the vehicle speed and the angular speed.
S300 comprises the following steps:
s301: initializing neural network parameters, and configuring a learning rate eta and an iteration precision epsilon;
the initialization process is as follows:
a. determining an input vector X: x ═ X1,x2,x3,x4,x5,x6]T；
b. Determining an output vector Y and a desired output vector O: y ═ Y1,y2]T,O＝[o1,o2]T；
c. Initializing weights from a hidden layer to an output layer: wij＝[ωi1,ωi2]T,(i＝1,2,3,4,5,6)；
d. Initializing central parameters of each neuron of the hidden layer: ck＝[ci1,ci2,ci3,ci4,ci5,ci6]；
e. Initializing a width vector: dk＝[dk1,dk2,dk3,dk4,dk5,dk6]。
After initialization is finished, calculating the output value of each neuron of the hidden layer and calculating the output of each neuron of the output layer;
s302: calculating the value of the root mean square error RMS output by the network, if the value of the root mean square error is less than or equal to the iteration precision, namely the RMS is less than or equal to epsilon, ending the training, otherwise executing S303;
wherein: o isijThe expected value of the jth output neuron at the ith input sample; y isijThe net output value of the jth output neuron at the ith input sample is obtained.
S303: the weight parameter, the center parameter, and the width parameter of the neural network model are iteratively trained using a gradient descent method, and then S302 is performed.
In S303, the weight parameter, the center parameter, and the width parameter are adjusted according to the following formulas:
wherein, ω isji(t) weight parameters between the jth output layer neuron and the ith hidden layer neuron at the time of the t iterative computation; c. Cik(t) central parameters for the ith hidden layer neuron to the kth input layer neuron at the tth iteration; dikIs and center cik(t) corresponding width parameters; eta is a learning factor;
i is an integer of 1 to ni，niNumber of hidden layer neurons; j is 1, 2; k is an integer of 1 to nk，nkThe number of neurons in the input layer; 0<η<1; in this example, ni＝6，i＝1,2,3,4,5,6；nk＝5，k＝1,2,3,4,5。
E is the cost function of the RBF neural network,
Oijfor the j output layer neuron's expected value at the i hidden layer neuron input sample; y isijThe output value of the jth output neuron when the sample is input to the ith hidden layer neuron.
When the technical scheme of the embodiment is operated, as shown in fig. 2, the distance of the obstacle is acquired through five laser radar sensors, the control model based on the RBF neural network model is adopted, data of the laser radar sensors are input, and control quantity of speed and angle can be generated according to the distance and angle information of the obstacle, so that intelligent control is realized, and processing logic and complexity of sensor data in the obstacle avoidance control process can be simplified.
Example two
The present embodiment is different from the first embodiment in that, in the present embodiment, the two neurons respectively output control target amounts for a vehicle speed and an angular velocity, and the present embodiment further includes:
s500: acquiring the speed and angular speed of a current vehicle;
s600: the control of the two drive wheels is performed based on the target amounts of the vehicle speed and the angular velocity of the output layer and the current speed and the angular velocity of the vehicle.
EXAMPLE III
The difference between this embodiment and the second embodiment is that, in this embodiment, the method further includes:
s700: recording data of each sensor and corresponding vehicle speed and angular velocity to form a data set;
s800: screening abnormal data in the data set according to a data screening rule;
s900: correcting the abnormal data, and constructing a corrected data set according to the abnormal data correction result;
s1000: and performing iterative training on the control model by correcting the data set.
In the technical scheme of the embodiment, the place where the current control model is inaccurate can be judged by screening abnormal data, and then iterative training is carried out by constructing a correction data set, so that the training precision is improved.
Example four
The difference between this embodiment and the third embodiment is that, in this embodiment, the method further includes:
s1100: constructing a sensor data inference model based on an LSTM neural network model;
s1200: constructing a training data set to train the sensor data inference model;
s1300: inputting data of an existing data set and a sample training set into a sensor data inference model, and inputting the position and the number of sensors to be predicted;
s1400: the sensor data inference model predicts detection data corresponding to other sensor positions according to data of an existing data set sample training set;
s1500: constructing data training sets corresponding to the number of other sensors according to the prediction result and the existing data set and sample training set;
s1600: and training the control model according to the data training set obtained in the step S1500.
In the technical scheme of the embodiment, by constructing the sensor data inference model, data corresponding to positions of other sensors can be pushed on the basis of an existing data set sample training set, and training sets and control models of different numbers of sensors are constructed, for example, training data sets of five sensors are currently available.
EXAMPLE five
The difference between this embodiment and the third embodiment lies in that, in this embodiment, a high accuracy control model and a low accuracy control model are provided, and the analysis dimension number related to the high accuracy control model and the low accuracy control model, that is, the number of input layer neurons and hidden layer neurons is different, and the method further includes:
s1700: the driving is controlled through a low-precision control model, and the storage module stores the current driving path data;
s1800: optimizing and analyzing the path data through a high-precision control model to generate an optimized driving path;
s1900: and when the condition that the vehicle runs on the same path again is detected, calling the optimized driving path and controlling the vehicle running according to the optimized driving path.
In this embodiment, the high-accuracy control model may be stored in the server or may be set in the vehicle control system, and by using the low-accuracy control model and the high-accuracy control model in sequence, the processing overhead of the vehicle may be reduced, but the optimal path is ensured to be achieved by optimizing the path of the vehicle traveling for many times.
The above are merely examples of the present invention, and the present invention is not limited to the field related to this embodiment, and the common general knowledge of the known specific structures and characteristics in the schemes is not described herein too much, and those skilled in the art can know all the common technical knowledge in the technical field before the application date or the priority date, can know all the prior art in this field, and have the ability to apply the conventional experimental means before this date, and those skilled in the art can combine their own ability to perfect and implement the scheme, and some typical known structures or known methods should not become barriers to the implementation of the present invention by those skilled in the art in light of the teaching provided in the present application. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (7)
1. An unmanned vehicle control method based on an RBF neural network is characterized in that: the method comprises the following steps:
s100: acquiring barrier data and preprocessing the barrier data;
s200: establishing a control model based on an RBF neural network model;
s300: constructing a sample training sample set, and training a control model;
s400: and inputting the preprocessed barrier data into a control model for processing, and outputting control parameters.
2. The unmanned vehicle control method based on RBF neural network of claim 1, wherein: the preprocessing in the S100 includes:
s101: acquiring data of each sensor;
s102: carrying out filtering processing on data of each sensor, wherein the filtering processing adopts a Kalman filtering algorithm;
s103: and carrying out data fusion on the data of each sensor to obtain the data of the obstacle.
3. The unmanned vehicle control method based on RBF neural network of claim 2, wherein: in S200, the control model includes an input layer, a hidden layer, and an output layer, where the number of neurons in the input layer corresponds to the number of sensors; the hidden layer adopts a Gaussian radial basis function as an activation function; the output layer includes two neurons that output control target amounts for the vehicle speed and the angular speed, respectively.
4. The unmanned vehicle control method based on RBF neural network of claim 3, wherein: s300 comprises the following steps:
s301: initializing neural network parameters, and configuring learning rate and iteration precision;
s302: calculating a root mean square error value output by the network, if the root mean square error value is less than or equal to the iteration precision, ending the training, otherwise executing S303;
s303: the weight parameter, the center parameter, and the width parameter of the neural network model are iteratively trained using a gradient descent method, and then S302 is performed.
5. The unmanned vehicle control method based on RBF neural network of claim 4, wherein: in S303, the weight parameter, the center parameter, and the width parameter are adjusted according to the following formulas:
wherein, ω isji(t) weight parameters between the jth output layer neuron and the ith hidden layer neuron at the time of the t iterative computation; c. Cik(t) central parameters for the ith hidden layer neuron to the kth input layer neuron at the tth iteration; dikIs and center cik(t) corresponding width parameters; eta is a learning factor;
i is an integer of 1 to ni，niNumber of hidden layer neurons; j is 1, 2; k is an integer of 1 to nk，nkThe number of neurons in the input layer; eta is more than 0 and less than 1;
e is the cost function of the RBF neural network,
Oijfor the j output layer neuron's expected value at the i hidden layer neuron input sample; y isijThe output value of the jth output neuron when the sample is input to the ith hidden layer neuron.
6. The unmanned vehicle control method based on RBF neural network of claim 5, wherein: unmanned car includes two drive wheels, unmanned car still includes through the speed difference control steering angle of two drive wheels:
s500: acquiring the speed and angular speed of a current vehicle;
s600: the control of the two drive wheels is performed based on the vehicle speed, the target amount of angular velocity, the current vehicle speed, and the angular velocity of the output layer.
7. The unmanned vehicle control method based on RBF neural network of claim 6, wherein: further comprising:
s700: recording data of each sensor and corresponding vehicle speed and angular velocity to form a data set;
s800: screening abnormal data in the data set according to a data screening rule;
s900: correcting the abnormal data, and constructing a corrected data set according to the abnormal data correction result;
s1000: and performing iterative training on the control model by correcting the data set.
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