# Dynamic Parameters Identification of Serial Robot Based on Dual Quaternion
## 2 Related Works  
Robot dynamic parameters are very important factors that affect the movement performance of the robot. Dynamic parameter identification involves estimating variables such as mass, center of mass, and inertia tensors of robotic components, which are essential for predicting the robot's dynamic behavior under varying operating conditions. According to [PERSON] [9], it can be useful to use a generic model to obtain attitude information and calculate attitude transformation relationships of delta-like robots, and analyze the data information of omitting negligible or non-excited inertia parameters to obtain the inertial parameters of the robot. In publication [10], [PERSON] studied the recursive algorithm to obtain the derivatives of the estimated inertial matrix and obtained identification results using the gradient-based optimizer. In publications [11; 12; 13], the dynamic model of load can be identified separately from the robot body or combined with the robot linkages to obtain effective data. In publications [14; 15; 16], the researchers obtained joint and base force and torque data through force sensors and established the dynamic model of the robot system in order to construct an inertia parameter identification algorithm scheme using the idea of combination. Otherwise, the dynamic parameters are important attributes of most robot systems, with dual-arm robots as an example, and these parameters can be useful to describe the dynamic response characteristics of the system. Moreover, the dynamic parameters of different models are also different. Because there are many theories to construct the complex dynamic models, and the unknown parameters that are not easy to model as the complex factors of the dynamic system. However, the dynamic parameters could be simplified, and in order to simplify the dynamic model, a combination of parameters can be used; several different derivation methods have been proposed by researchers. For example, in publications [17; 18; 19], [PERSON] provided a recursive formula for determining a set of minimum inertial parameters. [PERSON] used numerical and symbolic deduction to solve the set of minimum parameters. Otherwise, Lie algebra and Lie group theory can be used to establish robot kinematic models. This theory uses vector operators to represent the spatial position and attitude of the end-effector of the robot and obtains the spatial position vector through vector operators. Although the geometric meaning of parameters using Lie algebra to construct a model of robot system motion is not intuitive enough, the advantage is that as the number of degrees of freedom of the robot continues to increase, the calculation process of this method will become more efficient relatively, which can be discovered in publications [20; 21; 22]. In publications [23; 24; 25], [PERSON] first studied quaternions, and [PERSON] combined even numbers with quaternions to form algebraic operation rules for a dual quaternion gradually. Furthermore, dual quaternion algebra has been used to explain the pose transformation of a rigid body in space. The dual quaternion method that combines the concepts of even numbers and quaternions is a typical method belonging to the Lie group and Lie algebra. It has the unified representation of translation and rotation and the ability to avoid singularities found in the Denavit-Hartenberg-based method, and it can be more computationally efficient than the radial basis function neural network (RBFNN). This means the dual quaternion has the advantage of representing the rotation and translation of a rigid body while avoiding the singularity in the process of establishing a robotic model. Otherwise, there remains a lack of a unified method that integrates the dynamic identification of loads and linkages using dual quaternion algebra while maintaining low computational cost and practical feasibility.