TY - GEN
T1 - Pallet detection and docking strategy for autonomous pallet truck AGV operation
AU - Tsiogas, Efthimios
AU - Kleitsiotis, Ioannis
AU - Kostavelis, Ioannis
AU - Kargakos, Andreas
AU - Giakoumis, Dimitris
AU - Bosch-Jorge, Marc
AU - Ros, Raquel Julia
AU - Tarazon, R. L.
AU - Likothanassis, Spyridon
AU - Tzovaras, Dimitrios
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Automated guided vehicles operation in human populated factory environments is a challenging task, especially when there is a demand to operate without following fixed paths defined by guide wires, magnetic tape, magnets, or transponders embedded in the floor. The paper at hand introduces a vision-based method enabling safe and autonomous operation of pallet moving vehicles that accommodate pallet detection, pose estimation, docking control and pallet pick up in such industrial environments. A dedicated perception topology relying on monocular vision and laser-based measurements has been applied and installed on-board a novel robotic pallet truck. Pallet detection and pose estimation are performed in two steps. Firstly, a deep neural network is used for the fast isolation of pallets' regions of interest and, secondly, model-based geometrical pattern matching on point cloud data is applied to extract the pallet pose. Robot alignment with candidate pallet is performed with a dedicated visual servoing controller. The developed method has been extensively evaluated both in simulated and real industrial environments with the pallet truck and proved to have real-time performance achieving increased accuracy in navigation, pallet detection and pick-up.
AB - Automated guided vehicles operation in human populated factory environments is a challenging task, especially when there is a demand to operate without following fixed paths defined by guide wires, magnetic tape, magnets, or transponders embedded in the floor. The paper at hand introduces a vision-based method enabling safe and autonomous operation of pallet moving vehicles that accommodate pallet detection, pose estimation, docking control and pallet pick up in such industrial environments. A dedicated perception topology relying on monocular vision and laser-based measurements has been applied and installed on-board a novel robotic pallet truck. Pallet detection and pose estimation are performed in two steps. Firstly, a deep neural network is used for the fast isolation of pallets' regions of interest and, secondly, model-based geometrical pattern matching on point cloud data is applied to extract the pallet pose. Robot alignment with candidate pallet is performed with a dedicated visual servoing controller. The developed method has been extensively evaluated both in simulated and real industrial environments with the pallet truck and proved to have real-time performance achieving increased accuracy in navigation, pallet detection and pick-up.
UR - http://www.scopus.com/inward/record.url?scp=85124372060&partnerID=8YFLogxK
U2 - 10.1109/IROS51168.2021.9636270
DO - 10.1109/IROS51168.2021.9636270
M3 - Conference contribution
AN - SCOPUS:85124372060
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 3444
EP - 3451
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
Y2 - 27 September 2021 through 1 October 2021
ER -