# Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms

arXiv: https://arxiv.org/abs/2212.11201 · HTML: https://mlsanity.com/paper/2212.11201/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 21 Dec 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Distributed Deep Neural Networks over the Cloud, the Edge and End Devices](https://mlsanity.com/paper/1709.01921/) (2017)
- [Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks](https://mlsanity.com/paper/2012.03158/) (2020)
- [Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learning](https://mlsanity.com/paper/2001.10268/) (2020)
- [A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence](https://mlsanity.com/paper/2010.09317/) (2020)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Deep Reinforcement Learning Attitude Control of Fixed-Wing UAVs Using Proximal Policy Optimization](https://mlsanity.com/paper/1911.05478/) (2019)
- [Cellular-Connected UAVs over 5G: Deep Reinforcement Learning for Interference Management](https://mlsanity.com/paper/1801.05500/) (2018)
- [Offloading Optimization in Edge Computing for Deep Learning Enabled Target Tracking by Internet-of-UAVs](https://mlsanity.com/paper/2008.08001/) (2020)
- [Distributed Deep Convolutional Neural Networks for the Internet-of-Things](https://mlsanity.com/paper/1908.01656/) (2019)
- [Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique](https://mlsanity.com/paper/1901.10832/) (2019)
- [Distributed CNN Inference on Resource-Constrained UAVs for Surveillance Systems: Design and Optimization](https://mlsanity.com/paper/2105.11013/) (2021)
- [3D UAV Trajectory and Data Collection Optimisation via Deep Reinforcement Learning](https://mlsanity.com/paper/2106.03129/) (2021)
- [Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2107.11015/) (2021)
- [Mobile Edge Computing via a UAV-Mounted Cloudlet: Optimization of Bit Allocation and Path Planning](https://mlsanity.com/paper/1609.05362/) (2016)
- [Joint Ground and Aerial Package Delivery Services: A Stochastic Optimization Approach](https://mlsanity.com/paper/1808.04617/) (2018)
- [Efficient Real-Time Image Recognition Using Collaborative Swarm of UAVs and Convolutional Networks](https://mlsanity.com/paper/2107.04648/) (2021)
- [Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning](https://mlsanity.com/paper/2102.02078/) (2021)

## Built on by (most cited first, 6 of 6)

- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [Q-Learning based system for path planning with unmanned aerial vehicles swarms in obstacle environments](https://mlsanity.com/paper/2303.17655/) (2023)
- [Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms](https://mlsanity.com/paper/2401.11118/) (2024)
- [GraphEdge: Dynamic Graph Partition and Task Scheduling for GNNs Computing in Edge Network](https://mlsanity.com/paper/2504.15905/) (2025)
- [Multi-UAV Multi-RIS QoS-Aware Aerial Communication Systems using DRL and PSO](https://mlsanity.com/paper/2406.16934/) (2024)
- [Adversarial Reinforcement Learning for Robust Control of Fixed-Wing Aircraft under Model Uncertainty](https://mlsanity.com/paper/2510.16650/) (2025)

## Abstract (arXiv)

The deployment flexibility and maneuverability of Unmanned Aerial Vehicles (UAVs) increased their adoption in various applications, such as wildfire tracking, border monitoring, etc. In many critical applications, UAVs capture images and other sensory data and then send the captured data to remote servers for inference and data processing tasks. However, this approach is not always practical in real-time applications due to the connection instability, limited bandwidth, and end-to-end latency. One promising solution is to divide the inference requests into multiple parts (layers or segments), with each part being executed in a different UAV based on the available resources. Furthermore, some applications require the UAVs to traverse certain areas and capture incidents; thus, planning their paths becomes critical particularly, to reduce the latency of making the collaborative inference process. Specifically, planning the UAVs trajectory can reduce the data transmission latency by communicating with devices in the same proximity while mitigating the transmission interference. This work aims to design a model for distributed collaborative inference requests and path planning in a UAV swarm while respecting the resource constraints due to the computational load and memory usage of the inference requests. The model is formulated as an optimization problem and aims to minimize latency. The formulated problem is NP-hard so finding the optimal solution is quite complex; thus, this paper introduces a real-time and dynamic solution for online applications using deep reinforcement learning. We conduct extensive simulations and compare our results to the-state-of-the-art studies demonstrating that our model outperforms the competing models.