Humanoid robot navigation method based on hybrid fuzzy embedded PID (Proportion Integration Differentiation) control algorithm

Abstract

The invention discloses a humanoid robot navigation method based on a hybrid fuzzy embedded PID (Proportion Integration Differentiation) control algorithm. The method comprises the following steps: measuring a distance; measuring the distance between the humanoid robot and the forward obstacle, the distance between the humanoid robot and the right obstacle and the distance between the humanoid robot and the left obstacle by using an infrared sensor, defining fuzzy control variables and determining a value range; according to the method, a fuzzy control algorithm and a PID controller are innovatively combined, a fuzzy controller is utilized, a fuzzy control variable is defined as a forward obstacle distance, a right obstacle distance, a left obstacle distance and an optimized turning angle, a value range is determined, and a fuzzy membership function is generated. The forward obstacle distance value, the right obstacle distance value and the left obstacle distance value are input, the unique optimized turning angle is correspondingly obtained, the angle and torque of the robot joints can be adjusted in real time, navigation control over the humanoid robot is achieved, and the navigation precision is improved. The defects that an existing navigation method replacing a GPS is difficult to adapt to dynamic environment changes and low in control precision and calculation efficiency are overcome.

Description

Technical Field

[0001] The invention belongs to the technical field of robots, and in particular relates to a humanoid robot navigation method based on a hybrid fuzzy embedded PID control algorithm. Background Art

[0002] Humanoid robots are an important product in the field of modern robotics. They can imitate humans in many aspects, such as moving and walking from a source to any destination. With the development of society and the advancement of technology, the research on humanoid robots has gradually emerged, and navigation issues are also one of the focuses in the research on humanoid robots.

[0003] Normally, the global positioning system (GPS) can be used to navigate a humanoid robot, that is, the robot's absolute position is determined by satellite signals, and path planning and navigation are performed in combination with map data. This method is very mature and low-cost, but it is only applicable to scenarios where accuracy is not required. In addition, in some more complex environments, GPS cannot provide accurate navigation, such as indoors, underground, or in severe weather.

[0004] However, humanoid robots are also widely used in environments with high precision requirements and GPS-denied environments. In these scenarios, traditional GPS navigation methods cannot meet the needs, so other high-precision and reliable navigation methods are needed. Existing alternative navigation methods to GPS, such as path enhancement method, genetic potential field method, pseudobacterial potential field method and bacterial potential field method, generally have the defects of being difficult to adapt to dynamic environmental changes, low control accuracy and computational efficiency. Therefore, it is necessary to propose a navigation method with strong adaptability, high control accuracy and computational efficiency in dynamic environments to better adapt to complex and dynamic navigation environments. Summary of the invention

[0005] The technical problem to be solved by the present invention is: in view of the common shortcomings of the prior art, such as difficulty in adapting to dynamic environmental changes, low control accuracy and computational efficiency, a humanoid robot navigation method based on a hybrid fuzzy embedded PID control algorithm is proposed to better adapt to complex and dynamic navigation environments. The method first uses an infrared sensor to measure the forward obstacle distance, the right obstacle distance and the left obstacle distance; then defines the fuzzy control variables as the forward obstacle distance, the right obstacle distance, the left obstacle distance and the optimized turning angle, and determines the value range; then generates a fuzzy membership function; then formulates fuzzy rules, activates the fuzzy controller, inputs the forward obstacle distance, the right obstacle distance and the left obstacle distance values ​​into the fuzzy controller, performs fuzzy reasoning, and obtains the optimized turning angle. Finally, the obtained optimized turning angle is input into the PID controller, and a control signal is generated to adjust the angle and torque of the robot joint to realize the navigation control of the humanoid robot.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a humanoid robot navigation method based on a hybrid fuzzy embedded PID control algorithm, comprising the following steps:

[0007] Step S1: measuring distance;

[0008] Infrared sensors are used to measure the distances between the humanoid robot and the front obstacle, right obstacle, and left obstacle.

[0009] Step S2: define fuzzy control variables and determine the value range;

[0010] The fuzzy control variables are defined as the forward obstacle distance, the right obstacle distance, the left obstacle distance and the optimized turning angle, and the value range is determined; specifically: the forward obstacle distance is 30-84, the right obstacle distance is 30-84, the left obstacle distance is 30-84, and the optimized turning angle is -35-35.

[0011] Step S3: generating a fuzzy membership function;

[0012] Generate a fuzzy membership function, select the Gaussian membership function, define the membership range, and divide the input variables forward obstacle distance, right obstacle distance and left obstacle distance into eight levels, namely 30-42, 36-48, 42-54, 48-60, 54-66, 60-72, 66-78, 72-84, and the eight levels are correspondingly expressed as {extremely close, very close, very close, near, far, very far, very far, extremely far}. Divide the output variable optimized turning angle into eight levels, namely -35--25, -35--15, -25--5, -15-5, -5-15, 5-25, 15-35, 25-35, and the eight levels are correspondingly expressed as {extreme left turn, very left turn, very left turn, left turn, right turn, very right turn, very right turn, extreme right turn}.

[0013] Step S4: perform fuzzy operation;

[0014] A fuzzy control system is constructed according to fuzzy rules, the fuzzy controller is activated and run, the forward obstacle distance, right obstacle distance and left obstacle distance values ​​are input into the fuzzy controller, the fuzzy controller is run for fuzzy reasoning, and the centroid method is used for defuzzification to obtain the corresponding optimized turning angle as the output of the fuzzy controller.

[0015] Step S5: Controlling the humanoid robot using a PID controller;

[0016] The optimized turning angle is input into the PID controller to generate control signals to adjust the angle and torque of the robot joints, thus realizing navigation control of the humanoid robot.

[0017] The PID controller adjusts the system output to achieve the desired set point by combining three basic control strategies - proportional control, integral control and differential control, which can be expressed by the following equation:

[0018]

[0019] in:

[0020] e(t) is the error between the set point and the process variable.

[0021] U(t) is the controller output;

[0022] K p is the proportional gain;

[0023] K i is the integral gain;

[0024] K d is the differential gain;

[0025] In this paper, the optimized turning angle is input into the PID controller to generate control signals to adjust the angle and torque of the robot joints, so as to realize the navigation control of the humanoid robot.

[0026] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0027] 1. The present invention innovatively combines the fuzzy control algorithm and the PID controller, uses the fuzzy controller, inputs the forward obstacle distance, the right obstacle distance and the left obstacle distance values, and obtains a unique optimized turning angle. The angle and torque of the robot joint can be adjusted in real time to realize the navigation control of the humanoid robot, solving the defects of the existing navigation method that replaces GPS, such as difficulty in adapting to dynamic environmental changes, low control accuracy and low calculation efficiency.

[0028] 2. The present invention innovatively uses fuzzy algorithms to associate the forward obstacle distance, the right obstacle distance and the left obstacle distance with the optimized turning angle of the humanoid robot, laying a foundation for the subsequent introduction of other influencing factors, the establishment of a hybrid fuzzy neural network and PID controller model, or the application in gait control of humanoid robots on uneven ground. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0030] Figure 1 This is a flow chart of the humanoid robot navigation method based on the hybrid fuzzy embedded PID control algorithm.

[0031] Figure 2 It is the membership function diagram of the forward obstacle distance;

[0032] Figure 3 is the right obstacle distance membership function diagram;

[0033] Figure 4 It is the membership function diagram of the left obstacle distance;

[0034] Figure 5 This is the optimized turning angle membership function diagram. DETAILED DESCRIPTION

[0035] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Example 1

[0037] See also Figure 1 As shown, the present invention provides a humanoid robot navigation method based on a hybrid fuzzy embedded PID control algorithm, comprising the following steps:

[0038] Step S1: When the humanoid robot moves forward, the infrared sensor can be used to measure the distance between the humanoid robot and the front obstacle, the right obstacle and the left obstacle.

[0039] Step S2: define the fuzzy control variables as the forward obstacle distance, the right obstacle distance, the left obstacle distance and the optimized turning angle, and set the numerical range of the fuzzy control variables. The forward obstacle distance is 30-84, the right obstacle distance is 30-84, the left obstacle distance is 30-84, and the optimized turning angle is -35-35.

[0040] Step S3: Generate a fuzzy membership function, select a Gaussian membership function, define a membership range, and divide the input variables forward obstacle distance, right obstacle distance, and left obstacle distance into eight levels, namely, 30-42, 36-48, 42-54, 48-60, 54-66, 60-72, 66-78, 72-84, and the eight levels are correspondingly represented as {extremely close, very close, very close, near, far, very far, very far, extremely far}; divide the output variable optimized turning angle into eight levels, namely, -35--25, -35--15, -25--5, -15-5, -5-15, 5-25, 15-35, 25-35, and the eight levels are correspondingly represented as {extreme left turn, very left turn, very left turn, left turn, right turn, very right turn, very right turn, extreme right turn}, and generate the following Figure 2 , Attachment Figure 3 and attached Figure 4The forward obstacle distance, right obstacle distance and left obstacle distance membership function diagram shown in the figure and the attached Figure 5 The optimized turning angle membership function diagram is shown.

[0041] Step S4: Create a fuzzy control system according to fuzzy rules, activate and run the fuzzy controller, input the forward obstacle distance, right obstacle distance and left obstacle distance values ​​into the fuzzy controller, run the fuzzy controller for fuzzy reasoning, use the centroid method to defuzzify and obtain the corresponding optimized turning angle as the output of the fuzzy controller.

[0042] Step S5: Input the obtained optimized turning angle into the PID controller to generate a control signal to adjust the angle and torque of the robot joint to achieve navigation control of the humanoid robot.

[0043] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A humanoid robot navigation method based on a hybrid fuzzy embedded PID control algorithm, characterized in that: The following steps are involved: Step S1: measuring distance; Infrared sensors are used to measure the distances between the humanoid robot and the front obstacle, right obstacle, and left obstacle. Step S2: define fuzzy control variables and determine the value range; The fuzzy control variables are defined as the forward obstacle distance, the right obstacle distance, the left obstacle distance and the optimized turning angle, and the value range is determined; specifically: the forward obstacle distance is 30-84, the right obstacle distance is 30-84, the left obstacle distance is 30-84, and the optimized turning angle is -35-35. Step S3: generating a fuzzy membership function; Generate a fuzzy membership function, select the Gaussian membership function, define the membership range, and divide the input variables forward obstacle distance, right obstacle distance and left obstacle distance into eight levels, namely 30-42, 36-48, 42-54, 48-60, 54-66, 60-72, 66-78, 72-84, and the eight levels are correspondingly expressed as {extremely close, very close, very close, near, far, very far, very far, extremely far}. Divide the output variable optimized turning angle into eight levels, namely -35--25, -35--15, -25--5, -15-5, -5-15, 5-25, 15-35, 25-35, and the eight levels are correspondingly expressed as {extreme left turn, very left turn, very left turn, left turn, right turn, very right turn, very right turn, extreme right turn}. Step S4: perform fuzzy operation; A fuzzy control system is constructed according to fuzzy rules, the fuzzy controller is activated and run, the forward obstacle distance, right obstacle distance and left obstacle distance values ​​are input into the fuzzy controller, the fuzzy controller is run for fuzzy reasoning, and the centroid method is used for defuzzification to obtain the corresponding optimized turning angle as the output of the fuzzy controller. Step S5: Controlling the humanoid robot using a PID controller; The optimized turning angle is input into the PID controller to generate control signals to adjust the angle and torque of the robot joints, thus realizing navigation control of the humanoid robot. The PID controller adjusts the system output to achieve the desired set point by combining three basic control strategies - proportional control, integral control and differential control, which can be expressed by the following equation: in: e(t) is the error between the set point and the process variable. U(t) is the controller output; K p is the proportional gain; K i is the integral gain; K d is the differential gain; In this paper, the optimized turning angle is input into the PID controller to generate control signals to adjust the angle and torque of the robot joints, so as to realize the navigation control of the humanoid robot.

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