Planning swift maneuvers of quadcopter using motion primitives explored by reinforcement learning

TY - GEN

T1 - Planning swift maneuvers of quadcopter using motion primitives explored by reinforcement learning

AU - Camci, Efe

AU - Kayacan, Erdal

PY - 2019

Y1 - 2019

N2 - In this work, we propose a novel, learning-based approach for swift maneuver planning of unmanned aerial vehicles using motion primitives. Our approach is composed of two main stages: learning a set of motion primitives during offline training first, and utilization of them for online planning of fast maneuvers thereafter. We propose a compact disposition of motion primitives which consists of roll, pitch, and yaw motions to build up a simple yet effective representation for learning. Thanks to this compact representation, our method retains an easily transferable, reproducible, and referable knowledge which caters for real-time swift maneuver planning. We compare our approach with the current state-of-the-art methods for planning and control, and show improved navigation time performance up to 25 % in challenging obstacle courses. We validate our approach through software-in-the-loop Gazebo simulations and real flight tests with Diatone FPV250 Quadcopter equipped with PX4 FMU.

AB - In this work, we propose a novel, learning-based approach for swift maneuver planning of unmanned aerial vehicles using motion primitives. Our approach is composed of two main stages: learning a set of motion primitives during offline training first, and utilization of them for online planning of fast maneuvers thereafter. We propose a compact disposition of motion primitives which consists of roll, pitch, and yaw motions to build up a simple yet effective representation for learning. Thanks to this compact representation, our method retains an easily transferable, reproducible, and referable knowledge which caters for real-time swift maneuver planning. We compare our approach with the current state-of-the-art methods for planning and control, and show improved navigation time performance up to 25 % in challenging obstacle courses. We validate our approach through software-in-the-loop Gazebo simulations and real flight tests with Diatone FPV250 Quadcopter equipped with PX4 FMU.

UR - http://www.scopus.com/inward/record.url?scp=85072277478&partnerID=8YFLogxK

M3 - Article in proceedings

AN - SCOPUS:85072277478

SP - 279

EP - 285

BT - Proceedings of the American Control Conference

PB - IEEE

T2 - 2019 American Control Conference, ACC 2019

Y2 - 10 July 2019 through 12 July 2019

ER -