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2023 IEEE 17th International Symposium on Applied Computational Intelligence and Informatics (SACI) / 23-26 May 2023

Safe trajectory design for indoor drones using reinforcement-learning-based methods

This paper proposes a design method for achieving safe trajectory of indoor drones. The trajectory design with a reinforcement-learning-based (RL) agent is facilitated, which can result in efficient and collision-free motion. The method is developed for motion in indoor area with moving mobile robots, and thus, the collision with these obstacles must be avoided. Through RL-based design the fast motion of the drones can be achieved, which must perform a mission between workstations in a manufacturing system. The effectiveness of the design process through a simulation example on a real laboratory environment is illustrated.

Url
https://doi.org/10.1109/SACI58269.2023.10158591
Authors
Tompos, D.
Németh, B.
Areas of application

Autonomous Aerial Vehicles

Institutes

Kapcsolat

Prof. Dr. Péter Gáspár

H-1111 Budapest, Kende u. 13-17.

+36 1 279 6000

autonom@nemzetilabor.hu

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