CN109213174B - Sewage treatment plant intelligent patrol obstacle avoidance method based on fuzzy neural network - Google Patents
Sewage treatment plant intelligent patrol obstacle avoidance method based on fuzzy neural network Download PDFInfo
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- 238000007689 inspection Methods 0.000 claims abstract description 64
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
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- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0227—Control of position or course in two dimensions specially adapted to land vehicles using mechanical sensing means, e.g. for sensing treated area
- G05D1/0229—Control of position or course in two dimensions specially adapted to land vehicles using mechanical sensing means, e.g. for sensing treated area in combination with fixed guiding means
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
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Abstract
An intelligent inspection obstacle avoidance method for a sewage treatment plant based on a fuzzy neural network belongs to the technical field of intelligent robots. Aiming at the characteristics of movable obstacles, complex and changeable inspection environment and the like in a sewage treatment plant, the intelligent obstacle avoidance control method utilizes environmental information acquired by a front ultrasonic sensor, a rear ultrasonic sensor and a collision switch, judges and makes decisions on the surrounding environment of the inspection robot through a fuzzy neural network, realizes intelligent control on obstacle avoidance of the inspection robot, and improves the operation safety and stability of the inspection robot; the problem that the traditional robot obstacle avoidance method cannot avoid obstacles in advance and the obstacle avoidance effect is easy to interfere is solved. Experimental results show that the method has faster response capability and stronger self-adaptive capability to complex inspection environments, intelligent obstacle avoidance control of the inspection robot is achieved, and obstacle avoidance effectiveness and instantaneity are improved.
Description
Technical Field
The invention utilizes the intelligent patrol and obstacle avoidance method based on the fuzzy neural network to realize the intelligent obstacle avoidance control of the patrol robot of the sewage treatment plant, and the obstacle avoidance control of the patrol robot is a key technology for completing the patrol task; the intelligent inspection obstacle avoidance method based on the fuzzy neural network is applied to the inspection robot obstacle avoidance process, so that the inspection task can be completed safely and efficiently; an intelligent inspection obstacle avoidance method for a sewage treatment plant belongs to the technical field of intelligent robots;
background
Along with the increasing urbanization level of China, the discharge amount of urban sewage rises year by year, various large and medium-sized sewage treatment plants are newly built or expanded around the country, and along with the expansion of the area of the factory of the sewage treatment plant, the uninterrupted inspection of the factory becomes more important, but the traditional manual inspection is difficult to realize the uninterrupted inspection, and larger inspection areas also put higher requirements on inspection personnel; therefore, the inspection robot is increasingly used in daily inspection work of a sewage treatment plant;
in a sewage treatment plant, movable obstacles such as the flow of personnel and vehicles, the movement of equipment and the like and complex routing inspection environments are always difficult problems faced by a routing inspection robot in the routing inspection process; the inspection robot which needs to work in an unmanned environment is difficult to be widely applied, so the inspection robot with the obstacle avoidance function has more practical value in an actual working environment; however, the traditional inspection robot with the obstacle avoidance function performs obstacle avoidance operations such as retreating and steering after touching the obstacle, and is difficult to perform inspection work under the current increasingly complex and variable working environment; therefore, the development trend of the inspection robot for avoiding the obstacle is to distinguish the types of the obstacles, autonomously decide the obstacle avoiding mode and quickly and accurately execute the obstacle avoiding action; the intelligent patrol obstacle avoidance method for the sewage treatment plant is researched, patrol environment information is collected through various sensors, and the intelligent patrol obstacle avoidance of a patrol robot is realized by utilizing an artificial neural network autonomous learning and autonomous decision obstacle avoidance mode, so that the efficient and stable operation of the patrol process is ensured, and the urgent need of safe and stable operation of the sewage treatment plant is met;
the intelligent inspection obstacle avoidance method mainly realizes autonomous intelligent obstacle avoidance actions under different obstacle types and different environmental conditions by controlling the rotation direction of a servo motor of the inspection robot, avoids collision with obstacles, acquires more abundant environmental information by using different sensors, and improves the accuracy of intelligent obstacle avoidance decisions; compared with the traditional inspection robot for touch obstacle avoidance control, the intelligent inspection obstacle avoidance method has more advantages in the aspects of the accuracy, stability, anti-interference performance and the like of the obstacle avoidance control;
the invention designs an intelligent inspection obstacle avoidance method for a sewage treatment plant based on a fuzzy neural network, which mainly controls a control target through a fuzzy neural network controller to realize intelligent inspection obstacle avoidance of an inspection robot.
Disclosure of Invention
The invention obtains the intelligent patrol obstacle avoidance method for the sewage treatment plant based on the fuzzy neural network, and the intelligent patrol obstacle avoidance method based on the fuzzy neural network is used for carrying out obstacle avoidance control on the patrol robot of the sewage treatment plant, so that the intelligent patrol obstacle avoidance of the patrol robot is realized, and the operation safety and stability of the patrol robot are improved;
the invention adopts the following technical scheme and implementation steps:
1. a sewage treatment plant intelligent patrol obstacle avoidance method based on a fuzzy neural network is characterized in that the method controls the motion state of a sewage treatment plant patrol robot, takes the rotation direction of a servo motor as a control quantity, and takes the motion state of the robot as a controlled quantity;
the method comprises the following steps:
(1) the method comprises the following steps of designing a fuzzy neural network for controlling the rotation direction of a servo motor, wherein the fuzzy neural network is divided into four layers: an input layer, a membership function layer, a rule layer and an output layer; the method specifically comprises the following steps:
inputting a layer: this layer consists of 3 input neurons:
X(t)＝[x1(t),x2(t),x3(t)]T (1)
wherein X (t) represents the input of the fuzzy neural network, x1(t) is the distance between the robot and the obstacle measured by the front ultrasonic sensor at time t, x2(t) is the distance between the robot and the obstacle measured by the front ultrasonic sensor at time t, x3(T) is a switching signal of a collision switch at the moment T, and T is the transposition of a matrix;
membership function layer: the layer has 3 × M membership function neurons, each representing a gaussian membership function, represented as follows:
wherein M is the number of neurons in the regular layer, 1<M≤20；uij(t) the output of the jth membership function neuron for the ith input at time t, 0<uij(t)≤1；mij(t) the j-th membership function neuron central value of the ith input at the t moment, 0<mij(t)；σij(t) width value of jth membership function neuron of ith input at time t; i represents the input number of the fuzzy neural network, j represents the number of membership function neurons of the fuzzy neural network, and i is 1, 2 and 3; j ═ 1, 2, …, M;
third, rule layer: the layer has M regular neurons, the output of each neuron being:
wherein，fj(t) is the output value of the jth regular neuron at time t, 0<fj(t)≤1；
Output layer: this layer has 1 neuron, whose output is expressed as:
wherein h isj(t) is the output value of the jth back-part neuron at the time t, j is 1, 2, …, M, the number of back-part neurons is equal to the number of membership function neurons, wij(t) is the weight coefficient of the ith input to the jth back-part neuron at time t, bj(t) is the bias of the jth posterior neuron at time t, yu(t) is a fuzzy neural network output value at the time t, and represents a rotation direction control signal of the servo motor;
(2) training the fuzzy neural network specifically as follows:
firstly, training a fuzzy neural network by using a training sample and a self-adaptive second-order algorithm; the objective function is defined as:
e(t)＝yd(t)-yu(t) (6)
wherein e (t) is the error between the expected value and the actual value of the rotating direction at the time t, yd(t) is a rotation direction expected value at the moment t;
updating parameters of the fuzzy neural network, wherein the parameter updating formula is as follows:
wherein J (t) is a Jacobian vector containing the partial derivatives of the objective function to each parameter at time t, H (t) is a pseudo-Hessian matrix at time t, Ge(t) is a gradient vector at time t, lambda (t) is an adaptive learning rate at time t, and lambda (t-1) is adaptive learning at time t-1The learning rate is I, I is an identity matrix, phi (t) is a parameter vector containing each parameter value at the time t, and phi (t +1) is a parameter vector containing each parameter value at the time t + 1;
(3) the obstacle avoidance method for intelligent inspection is designed, and specifically comprises the following steps:
calculating the output of the fuzzy neural network according to a formula (5);
judging the size of a target function of the intelligent obstacle avoidance control signal of the inspection robot at the current moment, and if e (t) is greater than 0.01, turning to the step three; if e (t) is less than or equal to 0.01, transferring to the step (iv);
solving the updated value of each parameter according to the formula (7), and turning to the step I;
fourthly, calculating a control signal u at the time tc(t)
uc(t)＝yu(t) (8)
Control signal u at t momentc(t) actual input quantity of the inspection robot driving system;
(4) using the solved t-time control signal uc(t) controlling a servo motor of a drive system of the inspection robot, and controlling a signal u at the time of tcWhen the t is 1, the servo motor of the driving system rotates forwards; control signal u at time tcWhen the t is 0, stopping the servo motor of the driving system; control signal u at the present momentcAnd when the (t) is equal to-1, the servo motor of the driving system reverses.
The invention is mainly characterized in that:
(1) aiming at the characteristics that the inspection environment of the inspection robot of the sewage treatment plant is an open space, the inspection robot has various movable obstacles, the environment is complex and changeable and the like, the inspection robot needs to take obstacle avoidance measures in advance aiming at different types of obstacles so as to ensure the safety of the robot and complete the inspection process, and the conventional touch type obstacle avoidance method cannot meet the obstacle avoidance requirement under the inspection environment condition; the environment information is acquired by utilizing various sensors, and the intelligent inspection obstacle avoidance method based on the fuzzy neural network is adopted to realize the intelligent inspection obstacle avoidance of the inspection robot, so that the intelligent inspection robot has the advantages of timely obstacle avoidance, strong adaptability, strong interference resistance and the like;
(2) the invention adopts the intelligent patrol and obstacle avoidance method of the sewage treatment plant based on the fuzzy neural network to carry out obstacle avoidance control on the patrol process of the patrol robot, the obstacle avoidance method fully utilizes the self-learning capability of the artificial neural network, and good obstacle avoidance effect can be realized aiming at different patrol environments; the problems that the inspection robot can timely and accurately avoid obstacles and safely complete inspection tasks in a complex and variable sewage treatment plant production environment are solved;
particular attention is paid to: the invention is only for convenient description, adopts intelligent obstacle avoidance control on the inspection robot of the sewage treatment plant, is also applicable to intelligent inspection obstacle avoidance and the like of the inspection robot of the power plant, and belongs to the scope of the invention as long as the principle of the invention is adopted for control.
Drawings
FIG. 1 is a control structure diagram of the present invention
FIG. 2 is a diagram of a fuzzy neural network architecture of the present invention
FIG. 3 is a diagram of the obstacle avoidance result of the inspection robot
FIG. 4 is an error diagram of the obstacle avoidance result of the inspection robot
Detailed Description
The invention obtains the intelligent patrol obstacle avoidance method for the sewage treatment plant based on the fuzzy neural network, and the intelligent patrol obstacle avoidance method based on the fuzzy neural network is used for carrying out obstacle avoidance control on the patrol robot of the sewage treatment plant, so that the intelligent control of the patrol robot obstacle avoidance is realized, and the operation safety and the stability of the patrol robot are improved;
1. an intelligent inspection obstacle avoidance method for a sewage treatment plant based on a fuzzy neural network,
the motion state of the inspection robot of the sewage treatment plant is controlled, the rotation direction of a servo motor is used as a control quantity, the motion state of the robot is a controlled quantity, and the control structure is shown in figure 1;
the method is characterized by comprising the following steps:
(1) the method comprises the following steps of designing a fuzzy neural network for controlling the rotation direction of a servo motor, wherein the fuzzy neural network is divided into four layers: an input layer, a membership function layer, a rule layer and an output layer; the structure of the fuzzy neural network is shown in fig. 2, which specifically comprises:
inputting a layer: this layer consists of 3 input neurons:
X(t)＝[x1(t),x2(t),x3(t)]T (1)
wherein X (t) represents the input of the fuzzy neural network, x1(t) is the distance between the robot and the obstacle measured by the front ultrasonic sensor at time t, x2(t) is the distance between the robot and the obstacle measured by the front ultrasonic sensor at time t, x3(T) is a switching signal of a collision switch at the moment T, and T is the transposition of a matrix;
membership function layer: the layer has 3 × M membership function neurons, each representing a gaussian membership function, represented as follows:
wherein M is the number of neurons in the regular layer, 1<M≤20；uij(t) the output of the jth membership function neuron for the ith input at time t, 0<uij(t)≤1；mij(t) the j-th membership function neuron central value of the ith input at the t moment, 0<mij(t)；σij(t) width value of jth membership function neuron of ith input at time t; i represents the input number of the fuzzy neural network, j represents the number of membership function neurons of the fuzzy neural network, and i is 1, 2 and 3; j ═ 1, 2, …, M;
third, rule layer: the layer has M regular neurons, the output of each neuron being:
wherein f isj(t) is the output value of the jth regular neuron at time t, 0<fj(t)≤1；
Output layer: this layer has 1 neuron, whose output is expressed as:
wherein h isj(t) is the output value of the jth back-part neuron at the time t, j is 1, 2, …, M, the number of back-part neurons is equal to the number of membership function neurons, wij(t) is the weight coefficient of the ith input to the jth back-part neuron at time t, bj(t) is the bias of the jth posterior neuron at time t, yu(t) is the output of the fuzzy neural network at the time t, and represents the rotation direction control signal of the servo motor;
(2) training the fuzzy neural network specifically as follows:
firstly, training a fuzzy neural network by using a training sample and a self-adaptive second-order algorithm; the objective function is defined as:
e(t)＝yd(t)-yu(t) (6)
wherein e (t) is the error between the expected value and the actual value of the rotating direction at the time t, yd(t) is a rotation direction expected value at the moment t;
updating parameters of the fuzzy neural network, wherein the parameter updating formula is as follows:
wherein J (t) is a Jacobian vector containing the partial derivatives of the objective function to each parameter at time t, H (t) is a pseudo-Hessian matrix at time t, Ge(t) is a gradient vector at the time t, lambda (t) is an adaptive learning rate at the time t, lambda (t-1) is an adaptive learning rate at the time t-1, I is an identity matrix, phi (t) is a parameter vector containing each parameter value at the time t, and phi (t +1) is a parameter vector containing each parameter value at the time t + 1;
(3) the obstacle avoidance method for intelligent inspection is designed, and specifically comprises the following steps:
calculating the output of the fuzzy neural network according to a formula (5);
judging the size of a target function of the intelligent obstacle avoidance control signal of the inspection robot at the current moment, and if e (t) is greater than 0.01, turning to the step three; if e (t) is less than or equal to 0.01, transferring to the step (iv);
solving the updated value of each parameter according to the formula (7), and turning to the step I;
fourthly, calculating a control signal u at the time tc(t)
uc(t)＝yu(t) (8)
Control signal u at t momentc(t) actual input quantity of the inspection robot driving system;
(4) using the solved t-time control signal uc(t) controlling the rotation direction of the servo motor of the drive system, and controlling the signal u at the time of tcWhen the t is 1, the servo motor of the driving system rotates forwards; control signal u at time tcWhen the t is 0, stopping the servo motor of the driving system; control signal u at time tcWhen the (t) is-1, the servo motor of the driving system reverses; fig. 3 shows that the intelligent obstacle avoidance result of the inspection robot, X axis: time, in units of 5 seconds/sample, Y-axis: the rotation direction of the servo motor is unitless, a black dotted line is an expected rotation direction, and a solid line is an actual rotation direction; fig. 4 shows that the intelligent obstacle avoidance result error of the inspection robot, X axis: time, in units of 5 seconds/sample, Y-axis: error value of rotation direction, no unit; the experimental results prove the effectiveness of the method.
Claims (1)
1. A sewage treatment plant intelligent patrol obstacle avoidance method based on a fuzzy neural network is characterized in that the method controls the motion state of a sewage treatment plant patrol robot, takes the rotation direction of a servo motor as a control quantity, and takes the motion state of the robot as a controlled quantity;
the method comprises the following steps:
(1) designing a fuzzy neural network for generating a servo motor rotation direction control signal, wherein the fuzzy neural network is divided into four layers: an input layer, a membership function layer, a rule layer and an output layer; the method specifically comprises the following steps:
inputting a layer: this layer consists of 3 input neurons:
X(t)＝[x1(t),x2(t),x3(t)]T (1)
wherein X (t) represents the input of the fuzzy neural network, x1(t) is the distance between the robot and the obstacle measured by the front ultrasonic sensor at time t, x2(t) is the distance between the robot and the obstacle measured by the front ultrasonic sensor at time t, x3(T) is a switching signal of a collision switch at the moment T, and T is the transposition of a matrix;
membership function layer: the layer has 3 × M membership function neurons, each representing a gaussian membership function, represented as follows:
wherein M is the number of neurons in the regular layer, 1<M≤20；uij(t) the output of the jth membership function neuron for the ith input at time t, 0<uij(t)≤1；mij(t) the j-th membership function neuron central value of the ith input at the time t, mij(t)>0；σij(t) width value of jth membership function neuron of ith input at time t; i represents the input number of the fuzzy neural network, j represents the number of membership function neurons of the fuzzy neural network, and i is 1, 2 and 3; j ═ 1, 2, …, M;
third, rule layer: the layer has M regular neurons, the output of each neuron being:
wherein f isj(t) is the output value of the jth regular neuron at time t, 0<fj(t)≤1；
Output layer: this layer has 1 neuron, whose output is expressed as:
wherein h isj(t) is the output value of the jth back-part neuron at the time t, j is 1, 2, …, M, the number of back-part neurons is equal to the number of membership function neurons, wij(t) is the weight coefficient of the ith input to the jth back-part neuron at time t, bj(t) is the bias of the jth posterior neuron at time t, bj(t) is the bias of the jth posterior neuron at time t, yu(t) is the output of the fuzzy neural network at the time t, and represents the rotation direction control signal of the servo motor;
(2) training a fuzzy neural network, specifically:
firstly, training a fuzzy neural network by using a training sample and a self-adaptive second-order algorithm; the objective function is defined as:
e(t)＝yd(t)-yu(t) (6)
wherein e (t) is the error between the expected value of the rotation direction and the network output value at the time t, yd(t) is a rotation direction expected value at the moment t;
updating parameters of the fuzzy neural network, wherein the parameter updating formula is as follows:
H(t)＝JT(t)J(t),Ge(t)＝JT(t)e(t),
Φ(t)＝[mij(t),σij(t),wij(t),bj(t)]
Φ(t+1)＝Φ(t)+(H(t)+λ(t)I)-1Ge(t)
wherein J (t) is Jacobian vector containing the partial derivatives of the objective function to each parameter at the time t, H (t) is a pseudo-Hessian matrix at the time t, Ge(t) is a gradient vector at the time t, lambda (t) is an adaptive learning rate at the time t, lambda (t-1) is the adaptive learning rate at the time t-1, I is an identity matrix, phi (t) is a parameter vector containing each parameter value at the time t, and phi (t +1) is a parameter vector containing each parameter value at the time t + 1;
(3) the obstacle avoidance method for intelligent inspection is designed, and specifically comprises the following steps:
calculating the output of the fuzzy neural network according to a formula (5);
judging the size of a target function of the intelligent patrol obstacle-avoiding control signal of the patrol robot at the current moment, and if e (t) is greater than 0.01, turning to the step three; if e (t) is less than or equal to 0.01, transferring to the step (iv);
solving the updated value of each parameter according to the formula (7), and turning to the step I;
fourthly, calculating a control signal u at the time tc(t)
uc(t)＝yu(t) (8)
Control signal u at t momentc(t) actual input quantity of the inspection robot driving system;
(4) using the solved t-time control signal uc(t) controlling the rotation direction of a servo motor of a drive system of the inspection robot, and controlling a signal u at the time of tcWhen the t is 1, the servo motor of the driving system rotates forwards; control signal u at time tcWhen the t is 0, stopping the servo motor of the driving system; control signal u at time tcAnd when the (t) is equal to-1, the servo motor of the driving system reverses.
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