返回
Data Alignment Design for Robotic Programming by Demonstration Based on IMU and Optical Tracker
DOI:10.1109/TIM.2023.3244796.png)
摘要
En 中文
This study proposes a data alignment design for sensor fusion of an optical target (OT) and an inertial measurement unit (IMU) to track the trajectory of a handheld tool for robot programming by demonstration (PbD). A novel method is proposed for coordinate and temporal alignment between the OT and the IMU sensor to increase tracking accuracy. The IMU measurements are aligned with the corresponding angular velocities and accelerations derived from the OT to calibrate the IMU-OT relationship. Global and local optimizations are combined to reduce the computation time for the temporal alignment and coordinate calibration. Experimental results show that the proposed method provides improved calibration results with shorter calculation time compared with the two existing methods. The sensor fusion experiments verify the accuracy of trajectory tracking of 1.3 mm using the calibration result, an improvement from 1.4 mm of the data alignment using ruler measurement. Integrated PbD experiments using a 6-DOF manipulator were carried out to evaluate the proposed method. The manipulator imitated the hand motion of a bottle transfer task with a trajectory error of 0.66 mm. Practical robotic experiments demonstrate that the method is suitable for robot programming applications.
Keyword:
Robot sensing systems
Trajectory
Calibration
Optical filters
Cameras
Sensor fusion
Optical sensors
Inertial measurement unit (IMU)
programming by demonstration (PbD)
relative pose calibration
sensor fusion
sensory data alignment
期刊
IF:
5.9
论文数:
2.0W
被引数:
5.8W
机构
引用论文
Label-Free Protein-RNA Interactome Analysis Identifies Khsrp Signaling Downstream of the p38/Mk2 Kinase Complex as a Critical Modulator of Cell Cycle Progression
PLOS ONE
IF0
G4-QuadScreen: A Computational Tool for Identifying Multi-Target-Directed Anticancer Leads against G-Quadruplex DNA
Cancers
IF0
Tulsa 1000: a naturalistic study protocol for multilevel assessment and outcome prediction in a large psychiatric sample
BMJ Open
IF0

