Autonomy refers to the capability of systems to perceive environmental data, analyze it, and make decisions and take actions based on this analysis without the need for continuous human intervention.
Autonomous systems perform critical functions such as sensor data processing, situational assessment, mission planning, pathfinding, and obstacle avoidance internally and in real time. This capability sets them apart from conventional automation solutions and enables them to maintain operational performance even in uncertain environments. Autonomy, which encompasses sensing, analysis, learning, and decision-making processes, represents a transformative evolution not only in the technological domain but also across diverse sectors including defense, industry, logistics, and transportation.
The fundamental building blocks of autonomous platforms include multi-sensor integration, high-capacity computing hardware, and advanced algorithms. Massive volumes of data originating from sources such as LiDAR, sonar, radar, and electro-optical sensors are fused through sensor fusion algorithms to create environmental awareness. This awareness is further processed through deep learning, artificial intelligence, behavior modeling, and predictive analytics. Decision support processes encompass mission prioritization, route optimization, dynamic task reallocation, and obstacle avoidance strategies.
Given the criticality of security in autonomous systems, fault-tolerant architectures, redundant hardware designs, and cyber resilience mechanisms hold paramount importance. Examples of autonomy in action include UAVs that plan their own routes, underwater vehicles maneuvering to avoid obstacles, or ground robots autonomously identifying and tracking targets. Thus, autonomous platforms offer significant advantages such as speed, precision, flexibility, and security, extending beyond mere physical mobility to encompass advanced decision-making capabilities.
Globally, autonomy has emerged as a crucial factor for operational efficiency and rapid response, particularly in defense industries. Asymmetric threats and complex operational environments are pushing the limits of manned platforms, elevating operational risks. Autonomous air, land, and naval platforms enhance mission continuity, keep human operators away from hazardous environments, and deliver rapid reaction times while providing cost-effective solutions.
Today, autonomous capabilities are no longer limited to platform maneuvering; command and control architectures and decision support systems are increasingly operating through algorithmic processes. The future trend lies in developing systems capable of higher levels of collaboration, swarm architecture coordination, and adaptability to dynamic environmental conditions.
At STM, we regard autonomous technologies not merely as a trend but as a fundamental pillar of the future of defense and security. In a strategic field like defense, we place significant emphasis on developing autonomous systems that enhance operational capabilities, save time, and minimize human risk. Our R&D efforts in this domain combine sensor fusion, AI-based decision support mechanisms, and platform-level integration capabilities, contributing to Türkiye’s technological independence.
STM’s Autonomous Unmanned Underwater Vehicle (AUV) STM NETA stands as a prominent example of Türkiye’s defense capabilities in the field of autonomy.
The STM NETA family is capable of operating at depths ranging from 100 to 1000 meters and can be produced in different configurations tailored to shallow, medium, and deep-water operational scenarios.
The platform is equipped with sonar, inertial navigation systems (INS), depth sensors, magnetic sensors, and imaging systems. Thanks to advanced mission planning and obstacle avoidance algorithms, STM NETA can autonomously execute critical missions such as mine detection, pipeline inspection, harbor security, submarine rescue, and reconnaissance operations without human intervention. STM NETA’s autonomy capabilities significantly enhance the speed, safety, and effectiveness of underwater missions.
STM’s Tactical Mini UAV Systems possess autonomous capabilities that provide significant operational superiority in combat environments. Platforms like KARGU successfully execute target recognition, tracking, and autonomous navigation tasks utilizing deep learning-based image processing algorithms. Capabilities such as autonomous route planning, real-time threat analysis, and dynamic task switching enable rapid adaptation to evolving threats in the field.
STM’s systems also offer technical advantages such as low radar signature, rapid deployment, and compatibility with swarm operations, making them highly effective solutions in modern battlefields. These platforms not only strengthen national defense capabilities but also boost Türkiye’s competitive edge in global defense exports.
In swarm systems, assigned tasks are performed autonomously, with swarm intelligence serving as the foundation of autonomy. In other words, all swarm elements collectively execute assigned tasks using shared environmental awareness, resulting in superior mission performance compared to what individual platforms could achieve alone.
Within swarm systems, tasks are autonomously distributed and dynamically reassigned. Capabilities such as swarm splitting, area scanning, formation control, and obstacle avoidance are executed autonomously, and STM continues to optimize these functionalities.
Because swarm structures consist of multiple platforms, there are numerous ways to accomplish a given operation. Inter-platform task allocations must be determined to achieve missions at minimal cost. Even within the same swarm, platforms can be assigned to various missions such as surveillance, reconnaissance, area scanning, target acquisition, or target neutralization. To allocate tasks among swarm elements, STM employs cost functions that assess platform suitability and mission success probabilities. Inputs to these cost functions may include battery levels, distance to mission points, and types of payloads. Outputs from these functions are used to distribute tasks in a way that maximizes mission success while minimizing cost.
Autonomous task reassignment encompasses the transfer of tasks to the most suitable platform in response to changing conditions affecting the originally assigned platform or external environment. It is essential for handling situations during missions that could compromise mission success, such as low battery levels or emergencies. When changes such as the loss of a swarm element are detected, tasks are reallocated from the affected unit to the most cost-efficient alternative, thereby enhancing system resilience.
At STM, we view autonomous systems not simply as a technological domain but as a strategic lever shaping the future of defense. Through our ongoing R&D efforts and innovative solutions, we aim to position Türkiye as a significant player in autonomous systems on the global stage. For us, autonomy is not merely technology—it is the key to operational security and strategic independence.