IEEE Robotics & Automation Magazine - December 2018 - 76
ROBOTS: ©iSTOckphOTO.cOm/LeOnid eRemeychuk; BAckGROund: imAGe LicenSed By inGRAm puBLiShinG
Cooperation in the Air
A Learning-Based Approach for the Efficient
Motion Planning of Aerial Manipulators
By Hyoin Kim, Hoseong Seo, Clark Youngdong Son,
Hyeonbeom Lee, Suseong Kim, and H. Jin Kim
C
ooperation among multiple aerial vehicles could
provide a solution for overcoming limited
payload restrictions. Multiple aerial vehicles can
carry a heavy or large object safely by sharing the
load, alleviating the risk involved in exerting
considerable inertia on a single vehicle.
In reality, however, deploying multiple aerial manipulators
in a cooperative manner is not easy. For example, when aerial
manipulators hold an object jointly, the movements of one
agent affect other agents. Because aerial systems are inherently unstable, such undesirable motion can lead to the failure of
all agents.
Digital Object Identifier 10.1109/MRA.2018.2866766
Date of publication: 8 October 2018
76
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IEEE ROBOTICS & AUTOMATION MAGAZINE
*
december 2018
In this article, we employ a powerful motion-planning
approach to generate trajectories that avoid obstacles and
achieve a safe pose of manipulation while satisfying dynamic/kinematic constraints. This approach allows the incorporation of important factors such as a proper distribution of
payload to maintain overall stability as well as time and/or
energy efficiency. By learning the relationship between
motion parameters and the environmental configuration
from optimal (offline) demonstrations, the safe and efficient
trajectory corresponding to the actual environment with
unknown obstacles can be quickly computed (online) without further optimization. This article also presents successful
experiments of cooperative aerial transportation combined
with avoidance of unknown obstacles. The results demonstrate that our fast online replanning algorithm can be an
attractive solution for aerial cooperation.
1070-9932/18©2018IEEE
http://www.iSTOckphOTO.cOm/LeOnid
IEEE Robotics & Automation Magazine - December 2018
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