# Autonomous quadrotor obstacle avoidance based on dueling double deep recurrent Q-learning with monocular vision

arXiv: https://arxiv.org/abs/2002.03510 · HTML: https://mlsanity.com/paper/2002.03510/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [Vision Transformers for End-to-End Vision-Based Quadrotor Obstacle Avoidance](https://mlsanity.com/paper/2405.10391/) (2024)
- [Approximate information state based convergence analysis of recurrent Q-learning](https://mlsanity.com/paper/2306.05991/) (2023)
- [Dealing with uncertainty: balancing exploration and exploitation in deep recurrent reinforcement learning](https://mlsanity.com/paper/2310.08331/) (2023)
- [UAV Trajectory Optimization via Improved Noisy Deep Q-Network](https://mlsanity.com/paper/2602.05644/) (2026)

## Abstract (arXiv)

The rapid development of unmanned aerial vehicles (UAV) puts forward a higher requirement for autonomous obstacle avoidance. Due to the limited payload and power supply, small UAVs such as quadrotors usually carry simple sensors and computation units, which makes traditional methods more challenging to implement. In this paper, a novel framework is demonstrated to control a quadrotor flying through crowded environments autonomously with monocular vision. The framework adopts a two-stage architecture, consisting of a sensing module and a decision module. The sensing module is based on an unsupervised deep learning method. And the decision module uses dueling double deep recurrent Q-learning to eliminate the adverse effects of limited observation capacity of an on-board monocular camera. The framework enables the quadrotor to realize autonomous obstacle avoidance without any prior environment information or labeled datasets for training. The trained model shows a high success rate in the simulation and a good generalization ability for transformed scenarios.