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Reinforcement learning and model predictive control for robust embedded quadrotor guidance and control

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)1681-1693
Number of pages13
JournalAutonomous Robots
Volume43
Issue number7
Early online date12 Jan 2019
DOIs
DateAccepted/In press - 2 Jan 2019
DateE-pub ahead of print - 12 Jan 2019
DatePublished (current) - 15 Oct 2019

Abstract

A new method for enabling a quadrotor micro air vehicle (MAV) to navigate unknown environments using reinforcement learning (RL) and model predictive control (MPC) is developed. An efficient implementation of MPC provides vehicle control and obstacle avoidance. RL is used to guide the MAV through complex environments where dead-end corridors may be encountered and backtracking is necessary. All of the presented algorithms were deployed on embedded hardware using automatic code generation from Simulink. Results are given for flight tests, demonstrating that the algorithms perform well with modest computing requirements and robust navigation.

    Research areas

  • Exploration, Micro air vehicle, Model predictive control, Reinforcement learning

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    Rights statement: This is the final published version of the article (version of record). It first appeared online via Springer at https://link.springer.com/article/10.1007/s10514-019-09829-4#aboutcontent . Please refer to any applicable terms of use of the publisher.

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    Licence: CC BY

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