TY - JOUR
T1 - Cooperative Transportation of Cable Suspended Payloads with MAVs Using Monocular Vision and Inertial Sensing
AU - Li, Guanrui
AU - Ge, Rundong
AU - Loianno, Giuseppe
N1 - Funding Information:
Manuscript received October 15, 2020; accepted February 16, 2021. Date of publication March 11, 2021; date of current version May 4, 2021. This letter was recommended for publication by Associate Editor M. Garratt and Editor P. Pounds upon evaluation of the reviewers’ comments. This work was supported in part by Qualcomm Research, the Technology Innovation Institute, Nokia, NYU Wireless, and in part by Young Researchers Program “Rita Levi di Montalcini” 2017 Grant PGR17W9W4N. (Corresponding author: Guanrui Li.) The authors are with the Tandon School of Engineering, New York University, New York, NY 11201 USA (e-mail: [email protected]; [email protected]; [email protected]).
Publisher Copyright:
© 2016 IEEE.
PY - 2021/7
Y1 - 2021/7
N2 - Micro Aerial Vehicles (MAVs) have the great potential to be deployed in commercial or health care services such as e-commerce package delivery, transportation of medicines, same-day food delivery, and other time-sensitive transportation tasks. A team of MAVs can cooperatively transport objects to overcome the physical limitations of a single vehicle, while concurrently increasing the system's resilience to vehicles' failures. In this letter, we address the state estimation, control and trajectory tracking problems of cooperative transportation of cable suspended rigid body payloads with MAVs using monocular vision and inertial sensing. The key contributions are (a) a distributed vision-based coordinated control of the cable-suspended rigid body payload on SE(3), (b) a distributed estimation approach that allows each agent to estimate its cable direction and velocity independently, and (c) a new cooperative estimation scheme that can infer the payload's full 6-DoF states. This is obtained by sharing the robots' local position estimates and their relative position with respect to their corresponding attachment points on the payload. It allows to infer the payload's 6-DoF state when it is not directly measurable by each vehicle. The proposed solution runs in real-time on board and is validated through experimental results with multiple quadrotors transporting a rigid body payload via cables.
AB - Micro Aerial Vehicles (MAVs) have the great potential to be deployed in commercial or health care services such as e-commerce package delivery, transportation of medicines, same-day food delivery, and other time-sensitive transportation tasks. A team of MAVs can cooperatively transport objects to overcome the physical limitations of a single vehicle, while concurrently increasing the system's resilience to vehicles' failures. In this letter, we address the state estimation, control and trajectory tracking problems of cooperative transportation of cable suspended rigid body payloads with MAVs using monocular vision and inertial sensing. The key contributions are (a) a distributed vision-based coordinated control of the cable-suspended rigid body payload on SE(3), (b) a distributed estimation approach that allows each agent to estimate its cable direction and velocity independently, and (c) a new cooperative estimation scheme that can infer the payload's full 6-DoF states. This is obtained by sharing the robots' local position estimates and their relative position with respect to their corresponding attachment points on the payload. It allows to infer the payload's 6-DoF state when it is not directly measurable by each vehicle. The proposed solution runs in real-time on board and is validated through experimental results with multiple quadrotors transporting a rigid body payload via cables.
KW - Aerial systems
KW - aerial systems
KW - applications
KW - perception and autonomy
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U2 - 10.1109/LRA.2021.3065286
DO - 10.1109/LRA.2021.3065286
M3 - Article
AN - SCOPUS:85102696958
SN - 2377-3766
VL - 6
SP - 5316
EP - 5323
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 3
M1 - 9376103
ER -