# Towards Robust Multi-UAV Collaboration: MARL with Noise-Resilient Communication and Attention Mechanisms

arXiv: https://arxiv.org/abs/2503.02913 · HTML: https://mlsanity.com/paper/2503.02913/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 23 of 23)

- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Graph Neural Networks for Decentralized Multi-Robot Path Planning](https://mlsanity.com/paper/1912.06095/) (2019)
- [NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models](https://mlsanity.com/paper/2007.00328/) (2020)
- [SCRIMP: Scalable Communication for Reinforcement- and Imitation-Learning-Based Multi-Agent Pathfinding](https://mlsanity.com/paper/2303.00605/) (2023)
- [HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection](https://mlsanity.com/paper/2204.03245/) (2022)
- [Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs](https://mlsanity.com/paper/2007.12640/) (2020)
- [An informative path planning framework for UAV-based terrain monitoring](https://mlsanity.com/paper/1809.03870/) (2018)
- [Deep Reinforcement Learning-based Large-scale Robot Exploration](https://mlsanity.com/paper/2403.10833/) (2024)
- [Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning](https://mlsanity.com/paper/2203.02381/) (2022)
- [Graph Neural Networks for Multi-Robot Active Information Acquisition](https://mlsanity.com/paper/2209.12091/) (2022)
- [Multi-UAV Path Planning for Wireless Data Harvesting with Deep Reinforcement Learning](https://mlsanity.com/paper/2010.12461/) (2020)
- [Learning-based Methods for Adaptive Informative Path Planning](https://mlsanity.com/paper/2404.06940/) (2024)
- [Deep Reinforcement Learning with Dynamic Graphs for Adaptive Informative Path Planning](https://mlsanity.com/paper/2402.04894/) (2024)
- [Informative Path Planning for Mobile Sensing with Reinforcement Learning](https://mlsanity.com/paper/2002.07890/) (2020)
- [Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and Monitoring](https://mlsanity.com/paper/2005.10040/) (2020)
- [Multi-UAV Adaptive Path Planning Using Deep Reinforcement Learning](https://mlsanity.com/paper/2303.01150/) (2023)
- [IR2: Implicit Rendezvous for Robotic Exploration Teams under Sparse Intermittent Connectivity](https://mlsanity.com/paper/2409.04730/) (2024)
- [Unified Data Collection for Visual-Inertial Calibration via Deep Reinforcement Learning](https://mlsanity.com/paper/2109.14974/) (2021)
- [Monitoring and mapping of crop fields with UAV swarms based on information gain](https://mlsanity.com/paper/2203.11766/) (2022)
- [Autonomous localization of multiple ionizing radiation sources using miniature single-layer Compton cameras onboard a group of micro aerial vehicles](https://mlsanity.com/paper/2410.06693/) (2024)
- [A Decentralized Multi-Agent Unmanned Aerial System to Search, Pick Up, and Relocate Objects](https://mlsanity.com/paper/1707.03734/) (2017)
- [Traversing Mars: Cooperative Informative Path Planning to Efficiently Navigate Unknown Scenes](https://mlsanity.com/paper/2406.05313/) (2024)

## Built on by (most cited first, 1 of 1)

- [UAV Trajectory Optimization via Improved Noisy Deep Q-Network](https://mlsanity.com/paper/2602.05644/) (2026)

## Abstract (arXiv)

Efficient path planning for unmanned aerial vehicles (UAVs) is crucial in remote sensing and information collection. As task scales expand, the cooperative deployment of multiple UAVs significantly improves information collection efficiency. However, collaborative communication and decision-making for multiple UAVs remain major challenges in path planning, especially in noisy environments. To efficiently accomplish complex information collection tasks in 3D space and address robust communication issues, we propose a multi-agent reinforcement learning (MARL) framework for UAV path planning based on the Counterfactual Multi-Agent Policy Gradients (COMA) algorithm. The framework incorporates attention mechanism-based UAV communication protocol and training-deployment system, significantly improving communication robustness and individual decision-making capabilities in noisy conditions. Experiments conducted on both synthetic and real-world datasets demonstrate that our method outperforms existing algorithms in terms of path planning efficiency and robustness, especially in noisy environments, achieving a 78% improvement in entropy reduction.