# 3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2106.03129 · HTML: https://mlsanity.com/paper/2106.03129/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 58 references that are in the map.
- First posted on arXiv 6 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates](https://mlsanity.com/paper/1610.00633/) (2016)
- [Deep-Reinforcement Learning Multiple Access for Heterogeneous Wireless Networks](https://mlsanity.com/paper/1712.00162/) (2017)
- [Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks](https://mlsanity.com/paper/1705.02723/) (2017)
- [Simultaneous Navigation and Radio Mapping for Cellular-Connected UAV with Deep Reinforcement Learning](https://mlsanity.com/paper/2003.07574/) (2020)
- [Radio Map Based 3D Path Planning for Cellular-Connected UAV](https://mlsanity.com/paper/1912.00021/) (2019)
- [Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management](https://mlsanity.com/paper/1801.05500/) (2018)
- [Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach](https://mlsanity.com/paper/1812.07665/) (2018)
- [Reinforcement Learning in Multiple-UAV Networks: Deployment and Movement Design](https://mlsanity.com/paper/1904.05242/) (2019)
- [On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection](https://mlsanity.com/paper/1906.07064/) (2019)
- [Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique](https://mlsanity.com/paper/1901.10832/) (2019)
- [Unmanned Aerial Vehicle with Underlaid Device-to-Device Communications: Performance and Tradeoffs](https://mlsanity.com/paper/1509.01187/) (2015)
- [3D UAV Trajectory and Communication Design for Simultaneous Uplink and Downlink Transmission](https://mlsanity.com/paper/2001.00351/) (2020)
- [Unmanned Aerial Vehicle-Aided Communications: Joint Transmit Power and Trajectory Optimization](https://mlsanity.com/paper/1801.05351/) (2018)

## Built on by (most cited first, 8 of 8)

- [Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms](https://mlsanity.com/paper/2212.11201/) (2022)
- [UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms](https://mlsanity.com/paper/2211.14931/) (2022)
- [Reliable and Efficient Data Collection in UAV-based IoT Networks](https://mlsanity.com/paper/2311.05303/) (2023)
- [RIS-assisted UAV Communications for IoT with Wireless Power Transfer Using Deep Reinforcement Learning](https://mlsanity.com/paper/2108.02889/) (2021)
- [Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications](https://mlsanity.com/paper/2108.02892/) (2021)
- [Large Language Model-Empowered Decision Transformer for UAV-Enabled Data Collection](https://mlsanity.com/paper/2509.13934/) (2025)
- [Optimizing Energy and Data Collection in UAV-aided IoT Networks using Attention-based Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2601.14092/) (2026)
- [Physics-Aware End-to-End Deep Reinforcement Learning for Quadcopter Control with Actuator Dynamics](https://mlsanity.com/paper/2607.25985/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) are now beginning to be deployed for enhancing the network performance and coverage in wireless communication. However, due to the limitation of their on-board power and flight time, it is challenging to obtain an optimal resource allocation scheme for the UAV-assisted Internet of Things (IoT). In this paper, we design a new UAV-assisted IoT systems relying on the shortest flight path of the UAVs while maximising the amount of data collected from IoT devices. Then, a deep reinforcement learning-based technique is conceived for finding the optimal trajectory and throughput in a specific coverage area. After training, the UAV has the ability to autonomously collect all the data from user nodes at a significant total sum-rate improvement while minimising the associated resources used. Numerical results are provided to highlight how our techniques strike a balance between the throughput attained, trajectory, and the time spent. More explicitly, we characterise the attainable performance in terms of the UAV trajectory, the expected reward and the total sum-rate.