Unpublished conference/Abstract (Scientific congresses, symposiums and conference proceedings)
Laser-Based Reactive Navigation for Multirotor Aerial Robots using Deep Reinforcement Learning
Sampedro, Carlos; BAVLE, Hriday; Rodriguez-Ramos, Alejandro et al.
2018IEEE/RSJ International Conference on Intelligent Robots and Systems
 

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Keywords :
Model Learning for Control; Reactive and Sensor-Bas
Abstract :
[en] Navigation in unknown indoor environments with fast collision avoidance capabilities is an ongoing research topic. Traditional motion planning algorithms rely on precise maps of the environment, where re-adapting a generated path can be highly demanding in terms of computational cost. In this paper, we present a fast reactive navigation algorithm using Deep Reinforcement Learning applied to multi rotor aerial robots. Taking as input the 2D-laser range measurements and the relative position of the aerial robot with respect to the desired goal, the proposed algorithm is successfully trained in a Gazebo-based simulation scenario by adopting an artificial potential field formulation. A thorough evaluation of the trained agent has been carried out both in simulated and real indoor scenarios, showing the appropriate reactive navigation behavior of the agent in the presence of static and dynamic obstacles.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Sampedro, Carlos
BAVLE, Hriday  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Automation
Rodriguez-Ramos, Alejandro
De La Puente, Paloma
Campoy, Pascual
External co-authors :
yes
Language :
English
Title :
Laser-Based Reactive Navigation for Multirotor Aerial Robots using Deep Reinforcement Learning
Publication date :
2018
Event name :
IEEE/RSJ International Conference on Intelligent Robots and Systems
Event date :
1-10-2018 to 5-10-2018
Commentary :
9781538680940
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since 19 May 2021

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