Precision spherical joint is a spherical motion pair that can realize rotation with three degrees of freedom. This joint is widely used in robots, parallel mechanisms, and high-end medical equipment, as well as in aerospace and other fields. However, the rotation orientation and angle cannot be determined when the joint is in passive motion. The real-time determination of the rotation orientation and angle is crucial to the improvement of the motion control accuracy of the equipment where the joint is installed in. In this study, a new measurement method that utilizes eddy current sensors is proposed to identify the special features of the joint ball and realize angle measurements indirectly. The basic idea is to manufacture the specific shape features on the ball without affecting its movement accuracy and mechanical performance. An eddy current sensor array is distributed in the ball socket. When the ball head rotates, the features on the ball opposite to the sensor, as well as the output signal of every eddy current sensor, change. The measurement model that establishes the relationship between the output signal of the eddy current sensor array and the rotation direction and angle of the ball head is constructed by learning and training an artificial neural network. A prototype is developed using the proposed scheme, and the model simulation and feasibility experiment are subsequently performed. Results show that the root mean square angular error of a single axis within a range of ±14° is approximately 20 min, which suggests the feasibility of the proposed method.

Articulated arm coordinate measuring machines (AACMMs) have been developed and applied in industrial measurement fields for more than 30 years. Manual operation is typically required during measurement, which introduces uncertain influences, such as fluctuation of measurement force, speed, and acceleration, and leads to poor reliability and reproducibility. In this paper, a novel self-driven joint model is proposed to realize automatic measurement for AACMMs. A self-driven joint is designed by combining the joint of an AACMM with a robotic arm to realize automatic rotation. A self-driven AACMM is designed using three rolling joints and three pitching joints with assigned parameters. A virtual prototype of the self-driven AACMM is established using the Adams software to simulate the driving moment of each joint. The simulation results demonstrate that the designed mechanical structure and selected devices can meet the preset requirements. Additionally, based on the proposed model, a single physical joint is developed and assembled for performance testing. Experimental results demonstrate that the model can achieve a repeatability of 1.39″ (k = 2) when the rotational velocity is less than 1.53 rad/s. Therefore, the proposed design is suitable for use in AACMMs.

Nowadays, integrated joint modules are increasingly adopted in manipulators for their advantages of high integration, miniaturization and high repeatability positioning accuracy. The problem of generally low absolute positioning accuracy (namely angle measurement accuracy) must be solved before they can be introduced into the self-driven articulated arm coordinate measuring machine which is under study in our laboratory. In this study, the sources of joint module’s angle error were analyzed and the error model based on harmonic analysis was established. Two integrated joint modules were calibrated on the self-designed calibration platform and the model parameters were deduced, respectively. The angle error was then compensated in the experiments and the results demonstrated that the angle error of the joint modules was reduced by 82.03% on average. The established angle error model can be effectively applied into the self-driven articulated arm coordinated measuring machine.