AI-Driven Swarm Intelligence for Multi-Domain Unmanned Vehicles: Advances in Sensing, Perception, and Communication

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About this Research Topic

Submission deadlines

  1. Manuscript Submission Deadline 10 February 2026

  2. This Research Topic is currently accepting articles.

Background

The advancement of technology in unmanned vehicles, including aerial, surface, and underwater platforms, has led to widespread applications such as surveying, delivery, and tracking. Each type of unmanned vehicle has its strengths and limitations, making the combination of multiple autonomous vehicles highly beneficial in accuracy, efficiency, and operational time. However, integrating these systems has significant challenges, including real-time data fusion, robust inter-vehicle communication, swarm coordination, and energy-efficient operation in dynamic and unpredictable environments. Recent advancements in AI, autonomous systems, and communication technologies have driven the development of multi-domain unmanned vehicle swarms, allowing them to operate collaboratively across air, sea, and underwater environments. Three key challenges in this field include collaborative sensing, perception across different vehicles, and inter-vehicle communication, which is crucial for enabling high levels of autonomy, adaptability, and coordination.

This research topic aims to address key challenges in developing collaborative unmanned vehicles, focusing on sensing, perception, and communication capabilities. Recent advances in AI-driven sensor fusion and vision-based technologies have significantly improved measurement accuracy, allowing for more precise environmental understanding and situational awareness. Perception of unmanned vehicles, such as identifying safe landing zones in drone delivery or classifying objects, is crucial for autonomous decision-making. Additionally, real-time communication is critical in coordinating and collaborating with multi-vehicle swarms, ensuring efficient and safe operations in complex environments. Despite these advancements, integrating these capabilities across multi-domain unmanned vehicle systems remains challenging. This special issue invites contributions that address these fundamental issues, propose novel methodologies, and showcase practical implementations that enhance collaborative autonomy, paving the way for the future of intelligent, resilient, and coordinated unmanned systems.

Some specific themes that potential contributors can address may include, but are not limited to:
• Swarm Intelligence and Multi-Vehicle Coordination
• Real-Time Communication and Networking in Multi-Domain Systems
• Energy-Efficient Operation in Collaborative Unmanned Vehicle Swarms
• Applications of Collaborative Drones and AI-Driven Sensor Fusion
• Swarm Intelligence Algorithms
• AI-Driven Sensing and Perception
• Robustness and Fault Tolerance in Swarms
• Scalability of Swarm Systems

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

  • Brief Research Report
  • Data Report
  • Editorial
  • FAIR² Data
  • General Commentary
  • Hypothesis and Theory
  • Methods
  • Mini Review
  • Opinion

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Keywords: Drone, UAV, ROV, VTOL, Machine learning, AI, Swarm drone, Collaborative drone, Computer vision, Robot Operating System (ROS), Reinforcement Learning, Monitoring, Tracking, Aerial Manipulation, Surface Drone

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Topic editors

Manuscripts can be submitted to this Research Topic via the main journal or any other participating journal.

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