# DRQN-based 3D Obstacle Avoidance with a Limited Field of View

arXiv: https://arxiv.org/abs/2108.05962 · HTML: https://mlsanity.com/paper/2108.05962/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2021.

## Built on by (most cited first, 3 of 3)

- [Reinforcement Learning for Robot Navigation with Adaptive Forward Simulation Time (AFST) in a Semi-Markov Model](https://mlsanity.com/paper/2108.06161/) (2021)
- [Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System](https://mlsanity.com/paper/2501.13727/) (2025)
- [Collision-Aware Object-Goal Visual Navigation via Two-Stage Deep Reinforcement Learning](https://mlsanity.com/paper/2502.13498/) (2025)

## Abstract (arXiv)

In this paper, we propose a map-based end-to-end DRL approach for three-dimensional (3D) obstacle avoidance in a partially observed environment, which is applied to achieve autonomous navigation for an indoor mobile robot using a depth camera with a narrow field of view. We first train a neural network with LSTM units in a 3D simulator of mobile robots to approximate the Q-value function in double DRQN. We also use a curriculum learning strategy to accelerate and stabilize the training process. Then we deploy the trained model to a real robot to perform 3D obstacle avoidance in its navigation. We evaluate the proposed approach both in the simulated environment and on a robot in the real world. The experimental results show that the approach is efficient and easy to be deployed, and it performs well for 3D obstacle avoidance with a narrow observation angle, which outperforms other existing DRL-based models by 15.5% on success rate.