Intelligent Fastening Tool Tracking Systems Using Hybrid Remote Sensing Technologies
MetadataShow full item record
This research focuses on the development of intelligent fastening tool tracking systems for the automotive industry to identify the fastened bolts. In order to accomplish such a task, the position of the tool tip must be identified because the tool tip position coincides with the head of the fastened bolt while the tool fastens the bolt. The proposed systems utilize an inertial measurement unit (IMU) and another sensor to track the position and orientation of the tool tip. To minimize the position and orientation calculation error, an IMU needs to be calibrated as accurately as possible. This research presents a novel triaxial accelerometer calibration technique that offers a high accuracy. The simulation and experimental results of the accelerometer calibration are presented. To identify the fastening action, an expert system is developed based on the sensor measurements. When a fastening action is identified, the system identifies the fastened bolt by using an expert system based on the position and orientation of the tool tip and the position and orientation of the bolt. Since each fastening procedure needs different accuracies and requirements, three different systems are proposed. The first system utilizes a triaxial magnetometer and an IMU to identify the fastened bolt. This system calculates the position and orientation by using an IMU. An expert system is used to identify the initial position, stationary state, and the fastened bolt. When the tool fastens a bolt, the proposed expert system detects the fastening action by triaxial accelerometer and triaxial magnetometer measurements. When the fastening action is detected, the system corrects the velocity and position error using zero velocity update (ZUPT). By using the corrected tool tip position and orientation, the system can identify the fastened bolts. Then, with the fastened bolt position, the position of the IMU is corrected. When the tool is stationary, the system corrects linear velocity error and reduces the position error. The experimental results demonstrate that the proposed system can identify fastened bolts if the angles of the bolts are different or the bolts are not closely placed. This low cost system does not require a line of sight, but has limited position accuracy. The second system utilizes an intelligent system that incorporates Kalman filters (KFs) and a fuzzy expert system to track the tip of a fastening tool and to identify the fastened bolt. This system employs one IMU and one encoder-based position sensor to determine the orientation and the centre of mass location of the tool. When the KF is used, the orientation error increases over time due to the integration step. Therefore, a fuzzy expert system is developed to correct the tilt angle error and orientation error. When the tool fastens a bolt, the system identifies the fastened bolt by applying the fuzzy expert system. When the fastened bolt is identified, the 3D orientation error of the tool is corrected by using the location and the orientation of the fastened bolt and the position sensor outputs. This orientation correction method results in improved reliability in determining the tool tip location. The fastening tool tracking system was experimentally tested in a lab environment, and the results indicate that such a system can successfully identify the fastened bolts. This system not only has a low computational cost but also provides good position and orientation accuracy. The system can be used for most applications because it provides a high accuracy. The third system presents a novel position/orientation tracking methodology by hybridizing one position sensor and one factory calibrated IMU with the combination of a particle filter (PF) and a KF. In addition, an expert system is used to correct the angular velocity measurement errors. The experimental results indicate that the orientation errors of this method are significantly reduced compared to the orientation errors obtained from an EKF approach. The improved orientation estimation using the proposed method leads to a better position estimation accuracy. The experimental results of this system show that the orientation of the proposed method converges to the correct orientation even when the initial orientation is completely unknown. This new method was applied to the fastening tool tracking system. This system provides good orientation accuracy even when the gyroscopes (gyros hereafter) include a small error. In addition, since the orientation error of this system does not grow over time, the tool tip position drift is limited. This system can be applied to the applications where the bolts are closely placed. The position error comparison results of the second system and the third system are presented in this thesis. The comparison results indicate that the position accuracy of the third system is better than that of the second system because the orientation error does not increase over time. The advantages and limitations of all three systems are compared in this thesis. In addition, possible future work on fastening tool tracking system is described as well as applications that can be expanded by using the KF/PF combination method.
Cite this work
Peter Won (2010). Intelligent Fastening Tool Tracking Systems Using Hybrid Remote Sensing Technologies. UWSpace. http://hdl.handle.net/10012/5230