TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Redundancy-based visual tool center point pose estimation for long-reach manipulators

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020
KustantajaIEEE
Sivut1387-1393
Sivumäärä7
ISBN (elektroninen)9781728167947
ISBN (painettu)978-1-7281-6795-4
DOI - pysyväislinkit
TilaJulkaistu - 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE/ASME International Conference on Advanced Intelligent Mechatronics - Boston, Yhdysvallat
Kesto: 6 heinäkuuta 20209 heinäkuuta 2020

Julkaisusarja

NimiIEEE/ASME International Conference on Advanced Intelligent Mechatronics
ISSN (painettu)2159-6247
ISSN (elektroninen)2159-6255

Conference

ConferenceIEEE/ASME International Conference on Advanced Intelligent Mechatronics
MaaYhdysvallat
KaupunkiBoston
Ajanjakso6/07/209/07/20

Tiivistelmä

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.