A Reinforcement Learning Approach for Autonomous Control and Landing of a Quadrotor

Madhu Babu Vankadari, Kaushik Das, Chinmay Shinde, Swagat Kumar

Research output: Chapter in Book/Report/Conference proceedingChapter

5 Citations (Scopus)

Abstract

— This paper looks into the problem of precise autonomous landing of an Unmanned Aerial Vehicle (UAV) which is considered to be a difficult problem as one has to generate appropriate landing trajectories in presence of dynamic constraints, such as, sudden changes in wind velocities and directions, downwash effects, change in payload etc. The problem is further compounded due to uncertainties arising from inaccurate model information and noisy sensor readings. The problem is partially solved by proposing a Reinforcement Learning (RL) based controller that uses Least Square Policy Iteration (LSPI) to learn the optimal control policies required for generating these trajectories. The efficacy of the approach is demonstrated through both simulation and real-world ex-periments with actual Parrot AR drone 2.0. According to our study, this is the first time such experimental results have been presented using RL based controller for drone landing, making it a novel contribution in this field.
Original languageEnglish
Title of host publication2018 International Conference on Unmanned Aircraft Systems, ICUAS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages676-683
Number of pages8
ISBN (Print)9781538613535
DOIs
Publication statusPublished - 31 Aug 2018
Event2018 International Conference on Unmanned Aircraft Systems, ICUAS 2018 - Dallas, United States
Duration: 12 Jun 201815 Jun 2018

Publication series

Name2018 International Conference on Unmanned Aircraft Systems, ICUAS 2018

Conference

Conference2018 International Conference on Unmanned Aircraft Systems, ICUAS 2018
CountryUnited States
CityDallas
Period12/06/1815/06/18

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