#6 Unmanned

Until just a few years ago, defense systems were primarily designed to target a single UAV or a limited number of platforms. Today, swarm scenarios involving dozens or even hundreds of drones launching simultaneous attacks present a new challenge for defense planning. In such situations, as the number of attacking platforms increases, determining which defense platform should engage which target becomes an extremely complex computational problem. Indeed, the coordination of drone swarms and countermeasures have become one of the fastest-growing areas in unmanned systems research in recent years.

A new academic study titled “Building Large-Scale Drone Defenses from Small-Team Strategies,” prepared by researchers from the University of Adelaide and Saab Australia, proposes a different approach to this problem. Rather than attempting to compute defense strategies directly for very large drone swarms, the research focuses on scaling up tactics developed by small teams. According to the study, as the number of drones increases, the number of possible defense combinations grows exponentially, rendering classical optimization methods practically unusable. Therefore, the researchers first identify which behaviors are effective in small defense teams, then develop a system that modularly adapts these behaviors to larger swarm structures.

The model proposed in the study combines two distinct computational approaches. In the first phase, genetic algorithms are used to simulate and test various defense behaviors in a virtual environment. This method allows the identification of the most successful strategies through a process analogous to natural selection. Subsequently, dynamic programming is applied to distribute tactics proven effective in small teams across larger drone groups. This enables the rapid calculation of which defense drones should engage which attack groups.

In simulation tests, defense drones are assumed to operate according to specific behavioral rules. These include preemptive maneuvers to intercept attacking drones at an early stage, establishing defensive formations around targets, and prioritizing threats based on their assessed level.

Other approaches to defending against drone swarms are also being explored. Artificial intelligence methods such as multi-agent reinforcement learning and genetic algorithms are frequently used to develop decision-making mechanisms that enable coordinated drone movement. However, the computational cost of these methods increases rapidly with swarm size, making scalability a major concern.

This study proposes a model that incrementally develops defense strategies to overcome this issue. The researchers demonstrate that by transferring results from small-scale conflict scenarios to larger swarms, high success rates can still be achieved even in simulations involving dozens of drones. This approach is being evaluated as a novel method for defense planning in modern combat environments where unmanned systems are increasingly deployed in high density.

In modern combat scenarios involving dozens or even hundreds of UAVs operating simultaneously, individually controlling each platform is no longer a practical approach. Consequently, U.S. defense institutions have recently been developing new command-and-control methods to manage drone swarms more rapidly and simply.

According to Bloomberg, OpenAI is among the companies contributing to the development of voice-command technology for a drone swarm software competition sponsored by the U.S. Department of Defense. Under this project, OpenAI’s AI tools will translate spoken commands from battlefield commanders into digital instructions to assist in coordinating drone swarms. This system will not directly control drone flight or weapon use but will serve as an interface that translates human commands into understandable directives for platforms within the swarm.

This initiative is part of a broader program launched by the Pentagon to simplify the management of unmanned systems. Led by the Defense Innovation Unit, this effort incentivizes companies with a roughly $100 million prize program to develop software capable of directing large numbers of autonomous systems using natural language.

This development is only one component of OpenAI’s increasingly deep collaboration with U.S. military institutions. In 2025, the company signed a contract worth approximately $200 million with the then-U.S. Department of Defense, marking a significant entry into the national security domain. However, the trajectory of this relationship changed dramatically following a crisis that shook the AI sector in early 2026. The U.S. administration banned rival company Anthropic from federal networks, declaring it a national security and supply chain risk for refusing to remove ethical boundaries around mass surveillance and fully autonomous lethal weapons. Just hours after this decision, OpenAI stepped in and signed an agreement to integrate its AI models into the Pentagon’s classified military networks. The Pentagon aims for these multi-layered efforts to play a critical role not only in active combat operations such as drone swarm coordination but also in cybersecurity and big data analytics.

The management of drone swarms has become one of the fastest-growing topics in defense technology in recent years. The primary challenge in operations involving multiple unmanned air, land, and sea vehicles is ensuring their coordinated movement. The Pentagon’s new competition is precisely focused on this issue: developing software capable of understanding human intent through natural language and distributing those commands across numerous autonomous systems.

According to experts, such command-and-control solutions are critically important for operational efficiency in an era when the number of unmanned systems is rapidly increasing. Therefore, AI-assisted command systems are emerging as one of the foundational technologies determining the effectiveness of unmanned platforms on the battlefield.

The Pentagon’s approach in this area also signals a significant shift. In traditional military systems, operators must control each platform individually. In contrast, next-generation swarm architectures aim to distribute a single command to multiple platforms. Voice-command technologies are emerging as key tools in this transformation.

A research report published in 2026 reveals that the UAV countermeasure systems sector is poised for substantial growth over the next decade. According to the report, the global market for UAV countermeasure systems was approximately $4.9 billion in 2025 and could exceed $36 billion by 2035.

Several fundamental factors drive this growth. First, the decreasing cost and widespread availability of unmanned systems now enable both state and non-state actors to utilize these platforms. Even low-cost commercial drones can be employed for reconnaissance, sabotage, or attack, creating a new risk domain for security agencies. Consequently, demand is rapidly rising for multi-layered defense systems that integrate diverse technologies such as radar, radio frequency detection, electronic jamming, and AI-assisted target recognition.

This growth is not limited to military needs. Civilian sectors including airports, energy facilities, major sporting events, and public gatherings are also turning to these technologies to protect against unauthorized drone activity. Studies show that detection and disruption systems developed for critical infrastructure and airspace security constitute a significant portion of this market.

In the defense industry, this domain is increasingly becoming a competitive landscape, much like the UAV sector itself. Numerous large defense companies based in the U.S. and Europe, along with new technology startups, are competing to develop UAV countermeasure systems. Companies such as Raytheon, Northrop Grumman, Dedrone, and DroneShield are emerging as key players by developing a range of products from radar-based detection systems to electronic jamming devices.

The global security environment is further increasing interest in these technologies. The intensive use of small, low-cost drones in the Ukraine war served as a critical lesson for many nations. The widespread deployment of drones for reconnaissance, attack, and target designation on the battlefield has made the development of effective countermeasures an imperative. As a result, UAV countermeasure solutions are no longer merely a new technological field but have become one of the fundamental elements of modern warfare.

Market analyses and academic studies show that demand for countermeasure systems is rapidly increasing alongside the proliferation of unmanned aerial vehicles. Thanks to technologies such as sensor fusion, AI-assisted target recognition, and autonomous intervention systems, UAV defense is becoming more integrated and scalable. In other words, the challenge is no longer developing a single system but establishing a comprehensive defense architecture capable of continuously monitoring airspace and responding swiftly to diverse threats.

As modern warfare increasingly takes place in narrow streets, buildings, and complex urban environments, new technologies are gaining greater importance in military planning. In a modern battlefield undergoing fundamental transformation through digitalization and autonomous systems, the focus of Chinese military research institutions has shifted to AI-enabled drone swarms.

An analysis published in The Diplomat reveals that China is developing a new approach to urban warfare based on autonomous systems. Research linked to the Chinese People’s Liberation Army suggests that autonomous drone swarms can perform reconnaissance, target identification, and attack missions in urban settings. This approach is particularly evaluated for potential operations in densely populated areas such as a Taiwan scenario.

The primary reason for growing interest in unmanned systems in urban warfare is the nature of such operations. Buildings, narrow alleys, and complex infrastructure networks can severely limit the mobility of conventional military vehicles. Small, low-cost drones, however, offer significant advantages in these environments by enabling reconnaissance, target designation, and real-time information delivery to ground units. Consequently, many military analyses assess that autonomous drone swarms are likely to become widespread in future urban conflicts.

China’s efforts in this area are not merely theoretical. In recent military exercises, scenarios involving drone swarms, robotic ground vehicles, and human-machine collaboration have been tested. Some exercises reportedly simulated autonomous drones conducting reconnaissance while robotic ground vehicles provided support to troops in narrow urban areas.

However, the issue of autonomous weapon systems remains controversial internationally. China has publicly stated its opposition to fully autonomous systems without human intervention in discussions at the United Nations.

Military research indicates that autonomous systems may play a larger role in urban warfare. A war model in which drone swarms handle reconnaissance and target designation, robotic vehicles advance in confined spaces, and AI supports operational decision-making is increasingly debated both in China and among other major military powers. Therefore, the future of urban warfare will depend not only on new platforms but also on the advancement of autonomous decision-making systems.

A new report by the U.S.-based think tank Center for a New American Security (CNAS) argues that Taiwan must establish a broad defense network composed of numerous unmanned systems to deter a potential Chinese invasion. The report defines this approach as a defense zone heavily reliant on dense deployments of drones and other autonomous platforms.

The report proposes a model based on lower-cost but numerically superior systems in defense planning. It contends that establishing a dense network of integrated air, sea, and underwater drones could significantly complicate China’s amphibious invasion plans.

At the heart of the report is the idea of transforming the Taiwan Strait into an extremely hazardous operational area for invasion forces. In this approach, long-range aerial drones, surface unmanned vehicles, and underwater platforms would be deployed simultaneously to slow the advance of the Chinese navy. The goal is to make it more difficult for landing forces to reach the island and to raise the cost of the operation sufficiently to deter the attack.

The model proposed in the report is built on investment in mobile, distributed, and numerous systems rather than larger, more expensive platforms. The impact of commercial and low-cost drones in the Ukraine war is cited as a key example supporting this approach. Given that the Chinese military holds significant advantages in terms of ships, aircraft, and personnel, Taiwan must seek to offset this disparity using cheaper and more flexible technologies. Consequently, drone swarms and autonomous systems are finding an increasingly central role in Taiwan’s defense planning.

A future potential amphibious assault would become a multi-layered war of attrition, forcing traditional navies to confront thousands of cheap, autonomous, and expendable systems simultaneously from air, sea, and underwater. This scenario strengthens the argument that unmanned systems have moved far beyond being mere auxiliary elements in modern warfare.