Intelligent control method for climbing control of hanging rail type inspection robot

Abstract

The invention provides an intelligent control method for climbing control of a rail-mounted inspection robot, which comprises the following steps: when the rail-mounted inspection robot climbs, the rail-mounted robot determines the numerical value of clamping force applied to front and rear wheels by using an intelligent algorithm according to the current gradient information, and applies the clamping force with the numerical value to the front and rear wheels. Therefore, optimal distribution of clamping force of front and rear wheels is achieved, climbing efficiency is improved, energy loss is reduced, and the service life of driving wheels is prolonged.

Description

Technical Field

[0001] The invention relates to the field of inspection robot control, and in particular to an intelligent control method for climbing control of a rail-mounted inspection robot. Background Art

[0002] Inspection is an indispensable part of industries such as electricity, coal, steel, and sewage treatment, and is generally completed manually. However, most of the equipment in these industries works in harsh environments or environments that are difficult for humans to reach, which poses a great challenge to manual inspection. Therefore, inspection robots have gradually replaced manual inspection and become the main means of inspection.

[0003] The rail-mounted inspection robot has the advantages of precise control, simple structure, and high safety. It is very suitable for occasions with a single route and complex environment. When the rail-mounted inspection robot is climbing a slope, the clamping mechanism will provide a clamping force to prevent slipping. Most clamping mechanisms provide the same clamping force to the front and rear wheels, which brings a problem. When climbing a slope, the front wheel bears more of the gravity component, and the clamping force applied to the front wheel is too large for the rear wheel, which leads to the possibility of the robot failing to climb the slope, and also causes energy waste and increases the wear of the drive wheel. Summary of the invention

[0004] The main purpose of the present invention is to provide an intelligent control method for the climbing control of a rail-mounted inspection robot to solve the problems in the above-mentioned background technology.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: an intelligent control method for the climbing control of a rail-mounted inspection robot, comprising the following steps: the rail-mounted robot determines the clamping force value applied to the front and rear wheels according to the current slope information using an intelligent algorithm, and applies the clamping force of this value to the front and rear wheels.

[0006] Furthermore, the intelligent algorithm is an adaptive algorithm that uses slope information as an independent variable input and adaptively adjusts the value of the clamping force applied to the front and rear wheels.

[0007] Furthermore, in the intelligent control method for the climbing control of the rail-mounted inspection robot, the steps of implementing the adaptive algorithm are as follows:

[0008] S1. According to the slope information, combined with the fixed parameters such as the center of gravity height, weight and track friction coefficient of the rail-mounted inspection robot, the force balance and torque of the rail-mounted inspection robot are analyzed to calculate the value range of the front and rear wheel clamping force corresponding to the slope;

[0009] S2. multiplying the minimum value of the corresponding front and rear wheel clamping force by the redundancy coefficient as the actual clamping force applied to the front and rear wheels;

[0010] S3. Compare the minimum value of the corresponding front and rear wheel clamping force obtained in real time in S1 with the actual clamping force values ​​of the front and rear wheels. When the minimum value of the corresponding front wheel clamping force is greater than the actual clamping force value of the front wheel, multiply the minimum value of the corresponding front wheel clamping force by the redundancy coefficient to obtain the actual clamping force value applied to the front wheel. When the minimum value of the corresponding rear wheel clamping force is greater than the actual clamping force value of the rear wheel, multiply the minimum value of the corresponding rear wheel clamping force by the redundancy coefficient to obtain the actual clamping force value applied to the rear wheel.

[0011] S4. Repeat S3;

[0012] The above steps enable precise clamping force control.

[0013] Furthermore, in the intelligent control method for the climbing control of the rail-mounted inspection robot, the redundancy coefficient is 1.05 to 1.4.

[0014] Preferably, in the intelligent control method for climbing control of the rail-mounted inspection robot, the redundancy coefficient is 1.1.

[0015] Furthermore, in the intelligent control method for the climbing control of the rail-mounted inspection robot, in the step S1, when climbing, the force analysis of the rail-mounted inspection robot is as follows, and the balance formula of the robot's gravity on the front and rear wheels is listed:

[0016]

[0017] Where G is the weight of the robot, F GF 、F GR are the forces exerted by the ground on the front and rear wheels to overcome the gravity of the robot, LF and LR are the distances from the center of gravity to the front and rear wheels in the direction of the line connecting the front and rear wheels, H is the height of the center of gravity, and θ is the slope of the track. The balance equation of the front and rear wheel components can be processed to obtain:

[0018]

[0019] Then we can obtain:

[0020]

[0021] To apply the clamping force so that the front and rear drive wheels do not slip, the following conditions must be met:

[0022]

[0023] Where μ is the rail friction coefficient, F NF 、F NR They correspond to the clamping force of the front and rear wheels respectively.

[0024] Furthermore, the intelligent control method for the climbing control of the rail-mounted inspection robot calculates and processes the non-slip condition to obtain the value range of the corresponding front and rear wheel clamping force:

[0025]

[0026] Furthermore, in the intelligent control method for the climbing control of the rail-mounted inspection robot, in the step S2, according to the value range of the front and rear wheel clamping force, the value of the actual clamping force applied to the front and rear wheels is determined as:

[0027]

[0028] It can be seen from the above formula that, except for the slope, other parameters are inherent parameters of the robot or track itself.

[0029] Furthermore, in the intelligent control method for climbing control of the rail-mounted inspection robot, in step S3, the slope at the current moment is θ 1 , the corresponding minimum value of the front wheel clamping force is F NF1 The corresponding minimum rear wheel clamping force value is F NR1 , compare the corresponding front wheel clamping force value with the actual front wheel clamping force value. When the corresponding front wheel clamping force value is greater than or equal to the actual front wheel clamping force value, that is:

[0030]

[0031] Update the actual front wheel clamping force value:

[0032]

[0033] Furthermore, in the intelligent control method for climbing control of the rail-mounted inspection robot, in step S3, the slope at the current moment is θ 1 , the corresponding minimum value of the front wheel clamping force is F NF1 The corresponding minimum rear wheel clamping force value is F NR1 , compare the corresponding rear wheel clamping force value with the actual rear wheel clamping force value. When the corresponding rear wheel clamping force value is greater than or equal to the actual rear wheel clamping force value, that is:

[0034]

[0035] Update the actual clamping force value of the rear wheel:

[0036] BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0038] Figure 1 is a control algorithm flow chart of the present invention;

[0039] Figure 2 This is a force analysis diagram of the rail-mounted inspection robot of the present invention when climbing a slope. DETAILED DESCRIPTION

[0040] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0041] like Figure 1 As shown, the intelligent control method for climbing control of the rail-mounted inspection robot includes the following steps: the rail-mounted robot determines the clamping force value applied to the front and rear wheels using an intelligent algorithm based on the current slope information, and applies the clamping force of this value to the front and rear wheels.

[0042] The intelligent algorithm is an adaptive algorithm that takes the slope as an independent variable. The adaptive algorithm can adaptively adjust the value of the clamping force applied to the front and rear wheels.

[0043] The steps of the adaptive algorithm are as follows:

[0044] S1. Figure 2 As shown in the figure, when climbing a slope, the force analysis of the rail-mounted inspection robot is as follows, and the balance formula of the robot's gravity on the front and rear wheels is listed:

[0045]

[0046] Where G is the weight of the robot, F GF 、F GR are the forces exerted by the ground wire on the front and rear wheels to overcome the robot's gravity, L F , L R are the distances from the center of gravity to the front and rear wheels in the direction of the line connecting the front and rear wheels, and θ is the track slope. By processing the balance equation of the front and rear wheel components, we can get:

[0047]

[0048] Then we can obtain:

[0049]

[0050] Applying clamping force to prevent the front and rear drive wheels from slipping requires the following conditions to be met:

[0051]

[0052] Where μ is the rail friction coefficient, F NF 、F NR They correspond to the clamping force of the front and rear wheels respectively.

[0053] By calculating the above non-slip conditions, the corresponding range of front and rear wheel clamping forces can be obtained:

[0054]

[0055] S2. According to the range of values ​​of the front and rear wheel clamping force obtained in step 1, multiply the minimum value by the redundancy coefficient. The range of the redundancy coefficient is 1.05 to 1.4, and 1.1 is the best. The actual clamping force applied to the front and rear wheels is determined as follows:

[0056]

[0057] It can be seen from the above formula that, except for the slope, other parameters are fixed parameters of the robot or track itself.

[0058] S3. Let the slope at the current moment be θ 1 , the corresponding minimum value of the front wheel clamping force is F NF1 The corresponding minimum rear wheel clamping force value is F NR1 Compare the corresponding front wheel clamping force value with the actual front wheel clamping force value. When the corresponding front wheel clamping force value is greater than or equal to the actual front wheel clamping force value, that is:

[0059]

[0060] Update the actual front wheel clamping force value:

[0061]

[0062] When the corresponding rear wheel clamping force value is greater than or equal to the actual rear wheel clamping force value, that is:

[0063]

[0064] Update the actual clamping force value of the rear wheel:

[0065]

[0066] Repeating the above steps can achieve precise adaptive control of clamping force.

[0067] The beneficial effects of the present invention are: different clamping forces are provided to the front and rear wheels according to the change of the slope, so that the distribution of the clamping forces of the front and rear wheels is more reasonable, the climbing efficiency is improved, the energy loss is reduced, and the service life of the driving wheel is extended.

Claims

1. An intelligent control method for climbing control of a rail-mounted inspection robot is applied to the rail-mounted inspection robot, and is characterized by: The method comprises the following steps: the rail-mounted robot determines the clamping force value applied to the front and rear wheels by using an intelligent algorithm according to the current slope information, and applies the clamping force of the value to the front and rear wheels.

2. The intelligent control method for climbing control of a rail-mounted inspection robot according to claim 1 is characterized in that: The intelligent algorithm is an adaptive algorithm that uses slope information as an independent variable input and adaptively adjusts the value of the clamping force applied to the front and rear wheels.

3. The intelligent control method for climbing control of a rail-mounted inspection robot according to claim 2 is characterized in that: The steps of implementing the adaptive algorithm are: S1. According to the slope information, combined with the fixed parameters such as the center of gravity height, weight and track friction coefficient of the rail-mounted inspection robot, the force balance and torque of the rail-mounted inspection robot are analyzed to calculate the value range of the front and rear wheel clamping force corresponding to the slope; S2. multiplying the minimum value of the corresponding front and rear wheel clamping force by the redundancy coefficient as the actual clamping force applied to the front and rear wheels; S3. Compare the minimum value of the corresponding front and rear wheel clamping force obtained in real time in S1 with the actual clamping force values ​​of the front and rear wheels. When the minimum value of the corresponding front wheel clamping force is greater than the actual clamping force value of the front wheel, multiply the minimum value of the corresponding front wheel clamping force by the redundancy coefficient to obtain the actual clamping force value applied to the front wheel. When the minimum value of the corresponding rear wheel clamping force is greater than the actual clamping force value of the rear wheel, multiply the minimum value of the corresponding rear wheel clamping force by the redundancy coefficient to obtain the actual clamping force value applied to the rear wheel. S4. Repeat S3; The above steps enable precise clamping force control.

4. The intelligent control method for climbing control of a rail-mounted inspection robot according to claim 3 is characterized in that: The redundancy coefficient is 1.05 to 1.

4.

5. The intelligent control method for climbing control of a rail-mounted inspection robot according to claim 3 is characterized in that: The redundancy factor is 1.

1.

6. The intelligent control method for climbing control of a rail-mounted inspection robot according to claim 3 is characterized in that: In step S1, when climbing a slope, the force analysis of the rail-mounted inspection robot is as follows, and the balance equation of the robot's gravity on the front and rear wheels is listed: Where G is the weight of the robot, F GF 、F GR are the forces exerted by the ground on the front and rear wheels to overcome the gravity of the robot, LF and LR are the distances from the center of gravity to the front and rear wheels in the direction of the line connecting the front and rear wheels, H is the height of the center of gravity, and θ is the slope of the track. The balance equation of the front and rear wheel components can be processed to obtain: Then we can obtain: To apply the clamping force so that the front and rear drive wheels do not slip, the following conditions must be met: Where μ is the rail friction coefficient, F NF 、F NR They correspond to the clamping force of the front and rear wheels respectively.

7. The intelligent control method for climbing control of a rail-mounted inspection robot according to claim 6, characterized in that: The non-slip condition is calculated and processed to obtain the corresponding range of the front and rear wheel clamping forces:

8. The intelligent control method for climbing control of a rail-mounted inspection robot according to claim 7, characterized in that: In step S2, according to the range of the front and rear wheel clamping forces, the actual clamping force values ​​applied to the front and rear wheels are determined as: It can be seen from the above formula that, except for the slope, other parameters are inherent parameters of the robot or track itself.

9. The intelligent control method for climbing control of a rail-mounted inspection robot according to claim 8, characterized in that: In step S3, the current slope is assumed to be θ1, and the corresponding minimum value of the front wheel clamping force is F NF1 The corresponding minimum rear wheel clamping force value is F NR1 , compare the corresponding front wheel clamping force value with the actual front wheel clamping force value. When the corresponding front wheel clamping force value is greater than or equal to the actual front wheel clamping force value, that is: Update the actual front wheel clamping force value:

10. The intelligent control method for climbing control of a rail-mounted inspection robot according to claim 8, characterized in that: In step S3, the current slope is assumed to be θ1, and the corresponding minimum value of the front wheel clamping force is F NF1 The corresponding minimum rear wheel clamping force value is F NR1 , compare the corresponding rear wheel clamping force value with the actual rear wheel clamping force value. When the corresponding rear wheel clamping force value is greater than or equal to the actual rear wheel clamping force value, that is: Update the actual clamping force value of the rear wheel:

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