Full-Body Control of an Aerial Manipulator for Advance Physical Interaction Using Fuzzy Reinforcement Learning: A Road Map

Mohsen Zahmatkesh

doi:10.20944/preprints202302.0210.v1

Preprint Brief Report Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 11 February 2023 / Approved: 13 February 2023 / Online: 13 February 2023 (09:09:53 CET)

A detailed literature review is performed in this study to address solutions for the full-body design and control of an aerial manipulator. Deep Reinforcement Learning methods are growing to be utilized recently to cope with various uncertainties. The pros and cons of these theories will be explained as well as introducing the advantages of Fuzzy Reinforcement Learning methods. State-of-the-Art, possible challenges, potential approaches, and a summary of desired precision devices are discussed in this study.

Aerial Manipulator, Deep Deterministic Policy Gradient, Fuzzy Reinforcement Learning, Sensors

Engineering, Control and Systems Engineering

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Full-Body Control of an Aerial Manipulator for Advance Physical Interaction Using Fuzzy Reinforcement Learning: A Road Map

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