SWARMER

Unmanned Aerial Systems (UAS) have become essential assets for situational awareness and tactical support in modern defence operations. The current generation of UAS relies heavily on Global Navigation Satellite Systems (GNSS) for positioning and mission execution. However, in contested environments, adversaries try to take advantage of such vulnerability by employing electronic warfare tactics, including jamming and spoofing, to deny satellite access. Such “non-permissive environments” can render standard remotely piloted systems ineffective, limiting the operational capability of defence forces to locate targets or survey areas of interest safely. There is a pressing need for systems that can operate autonomously and reliably without external navigation signals or continuous data links to a ground station.

The SWARMER project addresses this capability gap by developing a robust framework for autonomous drone swarms designed specifically for GNSS-denied environments. Unlike traditional systems that run on a single UAS or process data centralized on swarm, SWARMER emphasizes distributed and decentralized ways along with multi-sensor fusion (LiDAR, visual, thermal and inertial sensors) to be used in deep learning methods in order to achieve autonomous navigation, mission completion and decision-making. The goal is to deliver a scalable system where drones operate as a cohesive flock, utilizing decentralized strategies to share tasks and situational awareness. The main objectives of the SWARMER project include:

• To design and develop a robust autonomous navigation system that fuses data from heterogeneous sensors (Visual, LiDAR, Inertial) to operate without GNSS.
• To design and integrate high-performance Edge AI computing payloads capable of supporting heavy computational loads for navigation and AI processing within strict Size, Weight, and Power (SWaP) constraints.
• To develop decentralized swarming behaviors and Vehicle-to-Vehicle (V2V) communication protocols, allowing the swarm to self-organize, heal from dropouts, and allocate tasks without a central controller.
• To implement Cooperative Simultaneous Localization and Mapping (C-SLAM), enabling the swarm to collectively build a 3D map of the battlefield and localize themselves relative to one another.
• To employ advanced AI/ML algorithms for real-time object detection, threat identification, and dynamic path planning in cluttered environments.
• To design a comprehensive system architecture for a resilient UAS swarm capable of robust operation in GNSS-denied and electronically contested environments.
• To validate the integrated system through participation in four annual technological challenges, testing the swarm in realistic, increasing complex scenarios (indoor, outdoor, and hybrid) to ensure operational readiness.

CERTH holds a central technical role in the SWARMER consortium, leading the work package on “Scalability and Optimization.” CERTH is responsible for the design and development of the core autonomous navigation and perception algorithms that form the brain of the swarm. This includes pioneering research in robust visual and LiDAR-based SLAM for localization in GPS-denied spaces, developing AI-driven dynamic path planning and obstacle avoidance systems, and optimizing models to take advantage of sensor fusion frameworks for decentralized swarm intelligence. Through these efforts, CERTH ensures the SWARMER system achieves the high levels of autonomy, resilience, and scalability required for successful deployment in non-permissive environments.

Consortium

IKNOWHOW SA, Greece
DEUTSCHES ZENTRUM FUR LUFT – UND RAUMFAHRT EV, Germany
APPLIED INTELLIGENCE ANALYTICS LIMITED, Ireland
ETHNIKO KENTRO EREVNAS KAI TECHNOLOGIKIS ANAPTYXIS, Greece
ETRA INVESTIGACION Y DESARROLLO SA, Spain
SIGNALGENERIX LIMITED, Cyprus
STOWARZYSZENIE POLSKA PLATFORMA BEZPIECZENSTWA WEWNETRZNEGO, Poland
ICARUS SWARMS, France
THALES, France