# Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV with Limited Environment Knowledge

arXiv: https://arxiv.org/abs/1811.03307 · HTML: https://mlsanity.com/paper/1811.03307/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Nov 2018.

## Built on by (most cited first, 26 of 26)

- [Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey](https://mlsanity.com/paper/2211.04324/) (2022)
- [NavRL: Learning Safe Flight in Dynamic Environments](https://mlsanity.com/paper/2409.15634/) (2024)
- [Learning Perception-Aware Agile Flight in Cluttered Environments](https://mlsanity.com/paper/2210.01841/) (2022)
- [MAVRL: Learn to Fly in Cluttered Environments with Varying Speed](https://mlsanity.com/paper/2402.08381/) (2024)
- [UAVs Meet Agentic AI: A Multidomain Survey of Autonomous Aerial Intelligence and Agentic UAVs](https://mlsanity.com/paper/2506.08045/) (2025)
- [AeroDuo: Aerial Duo for UAV-based Vision and Language Navigation](https://mlsanity.com/paper/2508.15232/) (2025)
- [Flying on Point Clouds with Reinforcement Learning](https://mlsanity.com/paper/2503.00496/) (2025)
- [Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review](https://mlsanity.com/paper/2208.12328/) (2022)
- [Navigation in a simplified Urban Flow through Deep Reinforcement Learning](https://mlsanity.com/paper/2409.17922/) (2024)
- [Asynchronous Curriculum Experience Replay: A Deep Reinforcement Learning Approach for UAV Autonomous Motion Control in Unknown Dynamic Environments](https://mlsanity.com/paper/2207.01251/) (2022)
- [Reliable and Efficient Data Collection in UAV-based IoT Networks](https://mlsanity.com/paper/2311.05303/) (2023)
- [Interpretable UAV Collision Avoidance using Deep Reinforcement Learning](https://mlsanity.com/paper/2105.12254/) (2021)
- [On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods](https://mlsanity.com/paper/2111.04865/) (2021)
- [Flocking and Collision Avoidance for a Dynamic Squad of Fixed-Wing UAVs Using Deep Reinforcement Learning](https://mlsanity.com/paper/2101.08074/) (2021)
- [Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation](https://mlsanity.com/paper/2209.11094/) (2022)
- [Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning](https://mlsanity.com/paper/2210.06377/) (2022)
- [Robust Recurrent Reinforcement Learning under Evolving Hidden Disturbances with Application to Rover Wheel Slip](https://mlsanity.com/paper/2307.15931/) (2023)
- [Self-Clustering Hierarchical Multi-Agent Reinforcement Learning with Extensible Cooperation Graph](https://mlsanity.com/paper/2403.18056/) (2024)
- [Towards Task-Oriented Flying: Framework, Infrastructure, and Principles](https://mlsanity.com/paper/2504.15129/) (2025)
- [NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation](https://mlsanity.com/paper/2605.15559/) (2026)
- [HiMA: A Fast and Scalable History-based Memory Access Engine for Differentiable Neural Computer](https://mlsanity.com/paper/2202.07275/) (2022)
- [Dynamic Q-planning for Online UAV Path Planning in Unknown and Complex Environments](https://mlsanity.com/paper/2402.06297/) (2024)
- [SHM-Traffic: DRL and Transfer learning based UAV Control for Structural Health Monitoring of Bridges with Traffic](https://mlsanity.com/paper/2402.14757/) (2024)
- [Zero-Shot UAV Navigation in Forests via Relightable 3D Gaussian Splatting](https://mlsanity.com/paper/2602.07101/) (2026)
- [A Cross-Layered Multi-Drone Coordination for Medical Supply Delivery during Disaster Response Management](https://mlsanity.com/paper/2605.09342/) (2026)
- [Vision-Guided Outdoor Flight and Obstacle Evasion via Reinforcement Learning](https://mlsanity.com/paper/2605.24449/) (2026)

## Abstract (arXiv)

This paper presents our method for enabling a UAV quadrotor, equipped with a monocular camera, to autonomously avoid collisions with obstacles in unstructured and unknown indoor environments. When compared to obstacle avoidance in ground vehicular robots, UAV navigation brings in additional challenges because the UAV motion is no more constrained to a well-defined indoor ground or street environment. Horizontal structures in indoor and outdoor environments like decorative items, furnishings, ceiling fans, sign-boards, tree branches etc., also become relevant obstacles unlike those for ground vehicular robots. Thus, methods of obstacle avoidance developed for ground robots are clearly inadequate for UAV navigation. Current control methods using monocular images for UAV obstacle avoidance are heavily dependent on environment information. These controllers do not fully retain and utilize the extensively available information about the ambient environment for decision making. We propose a deep reinforcement learning based method for UAV obstacle avoidance (OA) and autonomous exploration which is capable of doing exactly the same. The crucial idea in our method is the concept of partial observability and how UAVs can retain relevant information about the environment structure to make better future navigation decisions. Our OA technique uses recurrent neural networks with temporal attention and provides better results compared to prior works in terms of distance covered during navigation without collisions. In addition, our technique has a high inference rate (a key factor in robotic applications) and is energy-efficient as it minimizes oscillatory motion of UAV and reduces power wastage.