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Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities

Guangyuan Liu, Nguyen Van Huynh, Hongyang Du, Dinh Thai Hoang, Dusit Niyato, Kun Zhu, Jiawen Kang, Zehui Xiong, Abbas Jamalipour, Dong In Kim

TL;DR

Unmanned vehicle swarms enable scalable, robust operations across aerial, ground, surface, and underwater domains. The paper surveys Generative AI techniques—GANs, VAEs, diffusion models, Transformers, and normalizing flows—and maps their capabilities to core swarm challenges: state estimation, environmental perception, autonomy, task allocation, networking, security, and safety. It highlights concrete GAI-enabled applications and discusses open issues and future directions, including scalability, adaptive GAI, AI-native UV networks, 3-D interference control, and privacy/security. The work aims to guide researchers and practitioners toward leveraging GAI to achieve resilient, efficient, and autonomous UV swarms with practical impact for defense, industry, and civil applications.

Abstract

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions.

Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities

TL;DR

Unmanned vehicle swarms enable scalable, robust operations across aerial, ground, surface, and underwater domains. The paper surveys Generative AI techniques—GANs, VAEs, diffusion models, Transformers, and normalizing flows—and maps their capabilities to core swarm challenges: state estimation, environmental perception, autonomy, task allocation, networking, security, and safety. It highlights concrete GAI-enabled applications and discusses open issues and future directions, including scalability, adaptive GAI, AI-native UV networks, 3-D interference control, and privacy/security. The work aims to guide researchers and practitioners toward leveraging GAI to achieve resilient, efficient, and autonomous UV swarms with practical impact for defense, industry, and civil applications.

Abstract

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions.
Paper Structure (29 sections, 6 figures, 8 tables)

This paper contains 29 sections, 6 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Unmanned Vehicle swarms
  3. Unmanned Aerial Vehicles
  4. Unmanned Ground Vehicles
  5. Unmanned Surface Vehicles
  6. Unmanned Underwater Vehicles
  7. Generative AI
  8. An Overview
  9. Typical GAI Models
  10. GAN
  11. VAE
  12. GDM
  13. Transformer
  14. Normalizing flow
  15. Solutions to Unmanned Vehicle swarms utilizing GAI
  16. ...and 14 more sections

Figures (6)

  • Figure 1: The overall structure of this paper.
  • Figure 2: Infrastructure of UV systems and their applications.
  • Figure 3: Generative AI vs Discriminative AI
  • Figure 4: Overviews of Generative Models: Common Abilities and Structural Overviews of GANs, VAEs, GDMs, Normalizing Flows, and Transformers
  • Figure 5: Exploring the Spectrum of Innovation: This illustration presents 12 groundbreaking model structures, featuring two distinct approaches per aspect, to demonstrate the diverse applications of GAI in enhancing the performance and addressing challenges in UV swarms. Each model encapsulates unique strategies and solutions, offering a comprehensive overview of the technological advancements in this field.
  • ...and 1 more figures