# Inverse Kinematics of Large Hydraulic Manipulator Arm Based on ASWO Optimized BP Neural Network
## 1 Introduction  
The rapid development of tunnel construction has dramatically increased the demand for large hydraulic robotic arms. Compared with the electronically controlled mechanical arm, the hydraulic mechanical arm has the advantages of strong power, high durability, and fast response speed. However, the deflection error caused by the huge size of the arm itself makes it difficult to ensure its end positioning accuracy. The end position error of the forward kinematics can be determined through deflection modeling, error compensation modeling, and other methods. Inverse kinematics directly affects the control speed and accuracy of the robotic arm [1]. How to avoid the influence of forward solution errors and improve the positioning accuracy of the inverse solution of large hydraulic robotic arms has become the focus of research. Currently, inverse kinematics methods for robotic arms mainly include geometric, analytical, iterative, and intelligent algorithms. The geometric method [2, 3] depends on the geometric characteristics of the robot, is less generalized, and is not applicable to robotic arms with complex structures or many joints. The analytical method [4] has high solution accuracy but is computationally complex and has the problem of multiple solutions. The iterative method [5, 6] requires a large amount of computation and is difficult to meet real-time control requirements. Intelligent algorithms can be divided into two parts: neural networks and heuristic algorithms, which transform the complex inverse kinematics problem into a data model training and objective optimization problem and have become effective methods for the inverse kinematics solution of robotic arms. Artificial neural networks are widely used to solve complex robotic arm inverse solutions because of their powerful mapping ability, avoiding complex computational processes, and meeting real-time control requirements [7; 8; 9; 10; 11; 12; 13]. [PERSON] et al. [14] established an accurate kinematic model of a rigid-flexible variable-diameter robotic arm by quantitatively analyzing different pressure and load cases underwater and utilized a deep neural network to map the relationship between the position and attitude of the target terminal and the input pressure, achieving high inverse solution accuracy. [PERSON] et al. [15] solved the kinematic inverse problem of a six-degree-of-freedom robot by five different deep learning networks. [PERSON] et al. [16] proposed an excitation function-based improved BP neural network for robot inverse solution. [PERSON] et al. [17] completed the inverse solution of an industrial grade 5 degree of freedom manipulator with path planning through a neural network. [PERSON] et al. [18] completed the inverse solution of a humanoid robotic arm by using Bayesian-optimized deep neural network structure. [PERSON] et al. [19] proposed an artificial neural network with the current joint angle of the robotic arm and the desired position and attitude of the end in the input layer to improve the joint output accuracy. However, the above neural networks are prone to falling into local optimality and and find it difficult to find the global optimal solution when dealing with multi-peak high dimensional problems. Meta-heuristic algorithms are more efficient in solving complex nonlinear problems [20; 21; 22; 23; 24; 25; 26; 27], which can overcome the shortcomings of neural networks that are prone to fall into local optimums. [PERSON] et al. [28] proposed an inverse kinematics solution method for a four-degree-of-freedom hydraulic manipulator by combining the global mapping of a BP neural network with the local search of a gravitational search algorithm, ensuring solution accuracy while avoiding the problem of multiple solutions. [PERSON] et al. [29] proposed to optimize the initial weight bias of BP neural networks with a fruit fly optimization algorithm (FOA) to avoid neural networks from falling into local extremes. [PERSON] et al.