Abstract: This paper proposes a method for solving robot inverse kinematics based on a BP neural network algorithm, and provides the specific steps for solving robot inverse kinematics based on neural networks and related considerations for designing neural networks. Simulation results on a KLD-600 six-degree-of-freedom robot show that the algorithm is simple and reliable. Keywords: BP neural network; six degrees of freedom; robot; inverse kinematics 1 Introduction The robot inverse kinematics problem is to solve for the values ​​of joint variables given the robot's hand pose. It is a key link in robot trajectory planning and motion control, and also a hot topic in robot research. Traditional methods for solving inverse kinematics include the inverse transformation method proposed by Paul et al., the geometric method proposed by Lee and Ziegler, and Pieper's solution. These methods often have certain limitations, are difficult to solve, and involve problems such as multiple solutions and singularities. Neural network control technology, which emerged in the 1980s, is an important branch of neural networks. It has the structural characteristics of parallel processing, distributed storage, and fault tolerance, and has self-learning, self-organizing, and adaptive capabilities, as well as strong nonlinear mapping capabilities. Since the late 1980s, it has been applied to robot control. This paper uses the BP neural network algorithm to directly solve the inverse kinematics problem of the KLD-600 six-DOF robot. For details, please click to download: Inverse Kinematics Analysis of Robots Based on Neural Networks.