Controller design for quadrotor UAVs using reinforcement learning

By Haitham Bou-Ammar, Holger Voos, Wolfgang Ertel. 2010

100 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Theory and Mathematics, Control and Systems Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. David Feil-Seifer
2. Bo Li
3. Hung Manh La
4. Chaowei Xiao
5. Huy Xuan Pham
6. Luan Van Nguyen
7. Shaoshuai Shi
8. Hongsheng Li

In Computational Theory and Mathematics

1. Dawn Song
2. Bo Li
3. Tadayoshi Kohno
4. Keith B. Frikken
5. Benny Pinkas

In Control and Systems Engineering

1. David Feil-Seifer
2. Bo Li
3. Hung Manh La
4. Huy Xuan Pham
5. Luan Van Nguyen

Citations in the corpus

• Robust Physical-World Attacks on Deep Learning Models (2017, background): “I. INTRODUCTION Deep Neural Networks (DNNs) are being applied with great success to a variety of areas ranging from speech processing [6] to medical diagnostics [4] and are increasingly being used to control physical objects such as cars [14], UAVs [2], and robots [30].”
• Robust Physical-World Attacks on Deep Learning Visual Classification (2018, background): “Based on ∗ These authors contributed equally. these successes, they are increasingly being used as part of control pipelines in physical systems such as cars [8,17], UAVs [4,24], and robots [40].”
• Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage (2018, methodology): “Previous papers focus on applying RL algorithm into UAV control to achieve desired trajectory tracking/following [16], or discussion of using RL to improve the performance in UAV application [17].”