# Performance Analysis of Hybrid Kinematic Mechanism for Fusion Reactor Maintenance
## 1 Introduction
[PERSON] et al. [19] built the stiffness model of a novel five-DOF hybrid robot consisting of a Tricept parallel mechanism and a two-DOF wrist. Figure 1: DEMO concept design (**a**); the process of HKM lifting the BBs (**b**). [PERSON] et al. [20; 21] proposed a hybrid mechanism consisting of two 3 UPU mechanisms that can perform translational and yaw motions separately. [PERSON] [22] investigated the complete kinematics of a hybrid robot composed of two (3 SPS-UP) parallel robots connected in series. [PERSON] et al. [23] optimized the workspace of the Tricept using a genetic algorithm. In recent years, there has been an increasing number of studies on the accuracy analysis of parallel mechanisms considering geometric errors, joint errors, etc. [24]. Since the light link structure has unavoidable elastic deformation under large loads, which further causes large position errors, the position accuracy should be studied [25]. [PERSON] et al. [26; 27] simultaneously considered linkage elasticity, spherical joint, and universal joint clearance to evaluate the accuracy of Tricept under load, and the small changes in the link length caused by the clearance and deformation were simulated by a hyperbolic function. [PERSON] et al. [28] proposed a nonlinear kinematic model for the flexible axis parallel mechanism. [PERSON] et al. [29] identified geometric parameters according to the product of the exponentials (POE) formula and compensated for residual errors caused by non-geometric parameters using a multilayer perceptron neural network (MLPNN). [PERSON] et al. [30; 31] proposed a modeling method for flexible parallel robots considering nonlinear friction characteristics, and used the least-squares method to complete parameter identification. [PERSON] et al. [32; 33] proposed a sampling point selection method based on a spatial grid for static identification of kinematic parameters and joint stiffness. [PERSON] et al. [34] established an error prediction model for the Tricept robot using a positional error decomposition strategy and a BP neural network (BPNN) to achieve high-precision real-time compensation. [PERSON] et al. [35] proposed an iterative learning approach to compensate for the external uncertain dynamic loads of a 5-DOF hybrid-connected robot. In this paper, based on the new HKM mechanism in a BB remote maintenance system, forward and inverse kinematic modeling is built. The trajectory planning of the HKM in Cartesian space and joint space is designed according to the remote maintenance process of the BBs. Due to the high self-weight of BBs and the fact that grease lubrication is not allowed in the vacuum vessel environment, the Tricept spherical joint in the HKM will degrade into a universal joint, causing position errors. Next, we focus on analyzing the possible errors after the degradation of the HKM and assessing the feasibility of engineering applications. To overcome the possible transmission error, control error, and rigid-flexible coupling deformation error, etc., an open-loop variable parameter error compensation algorithm is proposed to meet the actual needs of engineering. This paper is organized as follows. Section 2 analyzes the forward and inverse kinematic properties of the HKM. Section 3 provides trajectory planning for the process of the HKM lifting BBs. Section 4 analyzes the effect of degradation of the Tricept spherical joint on the position accuracy of the HKM. Section 5 proposes an open-loop variable parameter error compensation method. Finally, Section 6 concludes this paper.