article · Journal of Engineering and Applied Science
Unmanned aerial vehicle swarms employ collaborative autonomy to expand operational capabilities across both civilian and defence sectors. Key elements of swarm infrastructure include coordinated path planning, task assignment, formation control, and dedicated security measures. Integrating artificial intelligence and machine learning strengthens decision-making and adaptability across varied environments. In civilian sectors, these systems can be applied to entertainment, infrastructure inspection, and delivery services, while military uses include surveillance, logistics, and combat support. Realising these capabilities involves addressing technical difficulties, regulatory constraints, and ethical considerations. Ongoing development aims to improve system scalability, robustness, and overall societal integration to support wider adoption.
Drone swarms offer collaborative, autonomous alternatives to single aerial vehicles, enabling coordinated operations across large areas. Understanding the technical, ethical, and regulatory hurdles allows organisations, operators, and developers to responsibly implement multi-agent robotic systems in civilian delivery, structural monitoring, and security settings.
The review outlines immediate applications for infrastructure inspection companies, entertainment providers, and commercial delivery services, as well as military logistics and surveillance organisations. Because this work reviews existing advancements, challenges, and future research directions rather than testing a specific product, the broad technology appears to range from applied development to early-stage research depending on the specific sector and regulatory context.
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Abstract Unmanned Aerial Vehicle (UAV) swarms represent a transformative advancement in aerial robotics, leveraging collaborative autonomy to enhance operational capabilities. This paper provides a comprehensive exploration of UAV swarm infrastructure, recent research advancements, and diverse applications. Key areas such as coordinated path planning, task assignment, formation control, and security considerations are examined, highlighting how Artificial Intelligence (AI) and Machine Learning (ML) are integrated to improve decision-making and adaptability. Applications span civilian sectors, including entertainment, infrastructure inspection, and delivery services, as well as military applications in surveillance, combat support, and logistics. The paper addresses technical challenges, regulatory constraints, and ethical considerations, while outlining future directions focused on scalability, robustness, and societal integration. This review consolidates the evolving landscape of UAV swarms, identifying critical challenges and guiding future research endeavors.
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DOI: 10.1186/s44147-025-00582-3
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