Payload Transporting With Two Quadrotors by Centralized Reinforcement Learning Method

Nowadays, quadrotors find applications in automation and artificial intelligence. Among diverse quadrotor studies, payload transport stands out, posing implementation challenges. Using multiple quadrotors reduces per-quadrotor load while increasing system complexity. Inspired by model-free reinforce...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems Vol. 60; no. 1; pp. 239 - 251
Main Authors: Lin, Dasheng, Han, Jianda, Li, Kun, Zhang, Jianlei, Zhang, Chunyan
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Angular velocity
Artificial intelligence
Continuous action space
continuous state space
Machine learning
Mathematical models
Nonlinear control
Payloads
Quadrotors
Reinforcement learning
reinforcement learning (RL)
Rotary wing aircraft
Symbols
Torque
twin delayed deep deterministic policy gradient (TD3)
two-quadrotor transporting payload system
ISSN:0018-9251, 1557-9603
Online Access:Get full text
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Summary:Nowadays, quadrotors find applications in automation and artificial intelligence. Among diverse quadrotor studies, payload transport stands out, posing implementation challenges. Using multiple quadrotors reduces per-quadrotor load while increasing system complexity. Inspired by model-free reinforcement learning, we apply it to position control in a nonlinear two-quadrotor payload system. Our approach employs a reinforcement learning agent guided by the twin delay deep deterministic policy gradient (TD3) algorithm. Its goal is accurate cable-suspended payload delivery and system stabilization. We test the method's robustness by adding noise. Simulation results show that TD3 excels in ideal conditions and handles noise during training and testing, highlighting its effectiveness. This article's scope can be expanded to encompass scenarios involving three or more quadrotors, providing valuable insights for future endeavors.
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ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2023.3321260