Redundancy-based visual tool center point pose estimation for long-reach manipulators
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020 |
| Publisher | IEEE |
| Pages | 1387-1393 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781728167947 |
| ISBN (Print) | 978-1-7281-6795-4 |
| DOIs | |
| Publication status | Published - 2020 |
| Publication type | A4 Article in a conference publication |
| Event | IEEE/ASME International Conference on Advanced Intelligent Mechatronics - Boston, United States Duration: 6 Jul 2020 → 9 Jul 2020 |
Publication series
| Name | IEEE/ASME International Conference on Advanced Intelligent Mechatronics |
|---|---|
| ISSN (Print) | 2159-6247 |
| ISSN (Electronic) | 2159-6255 |
Conference
| Conference | IEEE/ASME International Conference on Advanced Intelligent Mechatronics |
|---|---|
| Country | United States |
| City | Boston |
| Period | 6/07/20 → 9/07/20 |
Abstract
In this paper, we study a visual sensing scheme for 6 degree-of-freedom (DOF) tool center point (TCP) pose estimation of large-scale, long-reach manipulators. A sensor system is proposed, designed especially for mining manipulators, comprising a stereo camera running a simultaneous localization and mapping (SLAM) algorithm near the TCP and multiple cameras that track a fiducial marker attached near the stereo camera. In essence, the TCP pose is formulated using two different routes in a co-operative (eye-in-hand/eye-to-hand) manner using data fusion, with the goal of increasing the system's fault tolerance and robustness via sensor redundancy. The system is studied in offline data analysis based on real-world measurements recorded using a hydraulic 6 DOF robotic manipulator with a 5 m reach. The SLAM pose trajectory is obtained using the open source ORB-SLAM2 Stereo algorithm, whereas marker-based tracking is realized with a high-end motion capture system. For reference measurements, the pose trajectory is also formulated using joint encoders and a kinematic model of the manipulator. Results of the 6 DOF pose estimation using the proposed sensor system are presented, with future work and key challenges also highlighted.