# Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2209.06336 · HTML: https://mlsanity.com/paper/2209.06336/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight](https://mlsanity.com/paper/1902.03701/) (2019)
- [Deep Reinforcement Learning for Mapless Navigation of a Hybrid Aerial Underwater Vehicle with Medium Transition](https://mlsanity.com/paper/2103.12883/) (2021)
- [Double Critic Deep Reinforcement Learning for Mapless 3D Navigation of Unmanned Aerial Vehicles](https://mlsanity.com/paper/2112.13724/) (2021)
- [Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles](https://mlsanity.com/paper/2206.15211/) (2022)

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

- [Reinforcement Learning for Active Perception in Autonomous Navigation](https://mlsanity.com/paper/2602.01266/) (2026)
- [Improving Generalization in Aerial and Terrestrial Mobile Robots Control Through Delayed Policy Learning](https://mlsanity.com/paper/2406.01952/) (2024)
- [Apple: Toward General Active Perception via Reinforcement Learning](https://mlsanity.com/paper/2505.06182/) (2025)

## Abstract (arXiv)

Unmanned Aerial Vehicles (UAV) have been standing out due to the wide range of applications in which they can be used autonomously. However, they need intelligent systems capable of providing a greater understanding of what they perceive to perform several tasks. They become more challenging in complex environments since there is a need to perceive the environment and act under environmental uncertainties to make a decision. In this context, a system that uses active perception can improve performance by seeking the best next view through the recognition of targets while displacement occurs. This work aims to contribute to the active perception of UAVs by tackling the problem of tracking and recognizing water surface structures to perform a dynamic landing. We show that our system with classical image processing techniques and a simple Deep Reinforcement Learning (Deep-RL) agent is capable of perceiving the environment and dealing with uncertainties without making the use of complex Convolutional Neural Networks (CNN) or Contrastive Learning (CL).