Three-Dimensional Position-Measuring Instrument using Two Manipulators
In Japan, the number of sewer culverts with a service life of 50 years has increased, and aging facilities have become apparent. In recent years, therefore, robots have come to be used for pipe inspections. Thus, the robot must grasp the situation and perform actions to prevent tipping over in the pipe. Consequently, we have decided to explore a software approach to prevent tipping using driving control and aim to realize highly accurate selfposition estimation, which is necessary for this approach. Although we have established a basic localization method based on our previous research, there is a small estimation error due to the influence of tire shape. Therefore, we propose a highly accurate localization method using a neural network to compensate for this estimation error. However, a large amount of teacher data was required to achieve this. For this reason, this research develops a three-dimensional position-measuring instrument that can quickly perform many measurements with high accuracy using two commercially available manipulators. This paper states a three-dimensional position-measuring machine that can quickly perform high-precision and highvolume measurements using two commercially available manipulators. The paper shows how to connect two manipulators utilising a ball joint and proposes a highly accurate measurement theory using an IMU. Furthermore, we confirm that the accuracy of the contact-type three-dimensional position-measuring is within 1 mm on average for translation error, within 1 deg on average for angular error, within 1 mm for the standard deviation of translation error, and 1 deg for the standard deviation of angular error except for 0 γp3, even in the initial state without calibration. This result shows that the method can be used in a neural network to correct estimation errors.
Authors
- Prof. Hirofumi Maeda
Publication Details
- Journal
- International Journal of Recent Technology and Engineering (IJRTE)
- Published
- 2025-05-03
- DOI
- https://doi.org/10.35940/ijrte.f8214.14010525
- Primary Topic
- Robot Manipulation and Learning
- Type
- article
- Field-Weighted Citation Impact
- 0.00