Autonomous Navigation of UAVs in Large-Scale Complex Environments: A Deep Reinforcement Learning Approach

By Chao Wang, Jian Wang, Yuan Shen, Xudong Zhang. IEEE Transactions on Vehicular Technology, 2019

355 citations recorded by the source corpus.

In this paper, we propose a deep reinforcement learning (DRL)-based method that allows unmanned aerial vehicles (UAVs) to execute navigation tasks in large-scale complex environments. This technique is important for many applications such as goods delivery and remote surveillance. The problem is formulated as a partially observable Markov decision process (POMDP) and solved by a novel online DRL algorithm designed based on two strictly proved policy gradient theorems within the actor-critic framework. In contrast to conventional simultaneous localization and mapping-based or sensing and avoidance-based approaches, our method directly maps UAVs’ raw sensory measurements into control signals for navigation. Experiment results demonstrate that our method can enable UAVs to autonomously perform navigation in a virtual large-scale complex environment and can be generalized to more complex, larger-scale, and three-dimensional environments. Besides, the proposed online DRL algorithm addressing POMDPs outperforms the state-of-the-art.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/a2766fd8abb5610240ff57b1295bfac5f1c289d2

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. Bander Al-Zahrani
2. Ahmed Barnawi
3. Kai Zhu
4. Tao Zhang
5. Omar Sami Oubbati
6. Daniyal Mohammed Nouraddin Alghazzawi
7. Mohammed Atiquzzaman

In Artificial Intelligence

1. Bander Al-Zahrani
2. Kai Zhu
3. Tao Zhang

In Computer Vision and Pattern Recognition

1. Kai Zhu
2. Tao Zhang

Citations in the corpus

• Deep reinforcement learning based mobile robot navigation: A review (2021, methodology): “The authors then extended their work to the 3D environment [27] .”
• UAV assistance paradigm: State-of-the-art in applications and challenges (2020, methodology): “To handle these issues, UAVs can be quickly deployed to assist VANETs as relays and forward data packets among vehicles when the communication between vehicles on the ground is not possible [131], as shown in Fig.”