Welding gun space position and posture self-adaptive adjustment system and method of wheel type welding robot

[0039] In order to make the purpose, technical solutions and advantages of the present invention clearer, below in conjunction with specific embodiments (not limited to this example), and with reference to the accompanying drawings, the present invention is further elaborated:

[0040] The basic idea of ​​the present invention is to provide a self-adaptive real-time adjustment strategy of the welding torch space position and posture of an autonomous mobile wheeled welding robot based on a rotating arc sensor, as shown in the attached figure 2 and attached image 3 Schematic diagram of the assembly of the welding robot. The vertical arm and the horizontal arm installed on the roof plate of the Pioneer3DX autonomous mobile trolley are fixed to the α angle adjuster on the moving slider of the horizontal sliding arm. The β angle adjuster is connected with the α angle adjuster through the hollow arm, and the β angle The regulator is connected with the welding torch fine-tuning arm through bakelite, and the welding gun fine-tuning arm is composed of the welding gun based on the rotating arc sensor connected with the bakelite.

[0041] The adjustment feature of the system is that: the welding current change generated by the rotating arc is collected by the current Hall sensor to obtain the welding torch spatial position and attitude information, and the information processing is performed by the welding torch attitude coordinator in the control system to obtain the current height, left and right and inclination deviation of the welding torch. The control quantities of the mechanical adjustment arm and the wheel are obtained through the graded threshold controller, and output through D/A conversion to control the servo motor to drive the wheel and the rolling screw supported by the fixed bearings I at both ends of the vertical arm driven by the stepping motor I to rotate. , drive the horizontal arm fixed on the ball sliding table to move vertically along the lead screw and the rolling screw supported by the fixed bearings II at both ends of the horizontal arm driven by the stepping motor II to rotate, drive the hollow arm fixed on the ball sliding table horizontally Move and jointly adjust the height deviation of the welding torch; the α angle adjuster driven by the hollow rotary slide I to rotate the hollow arm and the β angle adjuster driven by the hollow rotary slide II to drive the rotation of the welding torch fine-tuning arm, jointly adjust the welding torch inclination deviation; The arm is driven by the stepper motor III to rotate the rolling screw supported by the fixed bearings III at both ends of the arm, and drives the rotating arc welding torch fixed on the ball sliding table to perform left and right fine-tuning and real-time adjustment, so as to realize the self-adaptive welding torch space attitude correction for welding seam tracking.

[0042] Welding torch attitude coordination strategy: Using the rotating arc sensor to input the signal into the welding torch attitude coordinator under the premise of knowing the three-dimensional attitude information of the welding torch, decompose the welding torch attitude deviation into high and low, left and right and inclination deviation, according to the adjustment control obtained by the hierarchical threshold controller According to the priority, the welding robot can weld in the desired position and posture, and the welding quality can be improved.

[0043] Hierarchical control strategy: the highest level of optimal regulation (O) includes four criteria-level influencing factors B 1 : Shortest adjustment time, B 2 : the smoothest trajectory, B 3 : Minimum energy consumption, B 4 : The change of the driving torque is the smallest, and a pairwise comparison is performed. If B = (b ij ) as a special case of the dynamic judgment matrix, then the O~B judgment matrix is

[0044]

[0045]

[0046] Consistency check: for n=4, λ max = 4.0739, RI = 0.90, then by Thus, CR=0.0274<0.1.

[0047] In the same way, we get: judgment matrix B 1 ~P's key data:

[0048] W 1 = [0.2467 0.0784 0.0538 0.5052 0.1158] T

[0049] λ 1 m a x = 5.1152 , CR 1 = 0.0257 0.1

[0050] Judgment Matrix B 2 ~P's key data:

[0051] W 2 =[0.2267 0.0744 0.0547 0.5072 0.1371] T

[0052] λ 2 m a x = 5.1377 , CR 2 = 0.0307 0.1

[0053] Judgment Matrix B 3 ~P's key data:

[0054] W 3 = [0.2222 0.0722 0.0472 0.5019 0.1565] T

[0055] λ 3 m a x = 5.1289 , CR 3 = 0.0288 0.1

[0056] Judgment Matrix B 4 ~P's key data:

[0057] W 4 =[0.1213 0.2300 0.0722 0.0592 0.5173] T

[0058] λ 4 m a x = 5.2177 , CR 4 = 0.0486 0.1

[0059] Grading Total Sort Calculation:

[0060]

[0061] Hierarchical Total Sort Consistency Ratio:

[0062] meet the requirements.

[0063] Therefore, the total ranking of the bottom level P for the top level optimal adjustment (O) is: P 5P 1P 2P 3P 4 , that is, priority ordering: wheel adjustment amount>α deviation adjustment amount>β deviation adjustment amount>left and right deviation adjustment amount>high and low deviation adjustment amount.