A reinforcement learning approach towards autonomous suspended load manipulation using aerial robots

Abstract:

In this paper, we present a problem where a suspended load, carried by a rotorcraft aerial robot, performs trajectory tracking. We want to accomplish this by specifying the reference trajectory for the suspended load only. The aerial robot needs to discover/learn its own trajectory which ensures that the suspended load tracks the reference trajectory. As a solution, we propose a method based on least-square policy iteration (LSPI) which is a type of reinforcement learning algorithm. The proposed method is verified through simulation and experiments. © 2013 IEEE.

Año de publicación:

2013

Keywords:

• Quadrotor control
• motion planning and control
• Trajectory tracking
• reinforcement learning
• Aerial robotics
• aerial load transportation
• Machine learning

Fuente:

scopus

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