Swarming Technology for Reliable and Energy-aware Aerial Missions

We are a joint research project between Lappeenranta-Lahti University of Technology (LUT) and Tampere University (TAU) funded by Jane and Aatos Erkko Foundation that aims at breaking through the current scalability limits in autonomous aerial swarming for collaborative mission-aware applications.


Project Coordinator: Assoc. Prof. Pedro Nardelli, LUT

Co-Principal Investigator: Assoc. Prof. Sergey Andreev, TAU

Co-Principal Investigator: Prof. Pertti Silventoinen, LUT


Unmanned Aerial Vehicles (UAVs) are reusable aircrafts designed to operate without on-board pilot nor passengers, which can be either piloted remotely or programmed to fly autonomously. UAVs may also be deployed to work collaboratively where multiple UAVs form a fleet named swarm to achieve a collective objective. In this case, the UAVs constituting an autonomous swarm maintain and adjust their formation to engage in collaborative missions by sharing not only their goals but also individual energy and data resources. We specifically emphasize missions related to rapid deployment and easy maintenance of flexible and on-demand radio network infrastructures that comprise multiple drone-cells and (multi-hop) UAV relays, which require efficient co-design of communication and navigation/control functionalities. In stark contrast to existing knowledge, this project will focus on understanding intelligent energy management in large UAV swarms for mission-aware communication purposes based on collaborative data acquisition with context-inference capabilities. In other words, this project seeks to integrate intelligent UAVs into an effective and robust autonomous swarm for large-scale and agile operations related to the contexts of (i) intra-swarm communication and (ii) on-demand network service provisioning. The key ambition of this project is to leap beyond the state-of-the-art in several fields of science and technology in order to reach our ultimate objective: break through the current scalability limits in autonomous aerial swarming for collaborative mission-aware applications. To achieve this trendsetting target the following scientific objectives need to be reached in concert: (a) Energy-centric collaborative UAV management in mission-specific intelligent swarming operations; (b) Communication technology co-design with collaborative flight control and UAV fleet management; (c) AI-driven data acquisition and fusion method for aerial context inference and situational awareness. Our approach will employ mathematical modeling and analysis, agent-based computer modeling and multi-agent simulations, artificial and collective intelligence methods, and a proof-of-concept testbed for experimenting with UAV swarms.


  1. Dick Carrillo, Konstantin Mikhaylov, Pedro J. Nardelli, Sergey Andreev, Daniel da Costa: Understanding UAV-Based WPCN-Aided Capabilities for Offshore Monitoring Applications. IEEE Wireless Communications Magazine 28(2): 114-120 (2021)

  2. Margarita Gapeyenko, Dmitri Moltchanov, Sergey Andreev, Robert W. Heath Jr.: Line-of-Sight Probability for mmWave-based UAV Communications in 3D Urban Grid Deployments. IEEE Transactions on Wireless Communications 20(10): 6566-6579 (2021)

  3. Miguel Calvo-Fullana, Alexander Pyattaev, Daniel Mox, Sergey Andreev, Alejandro Ribeiro: Communications and Robotics Simulation in UAVs: A Case Study on Aerial Synthetic Aperture Antennas. IEEE Communications Magazine 59(1): 22-27 (2021)

  4. Iuliia Tropkina, Alexander Pyattaev, Yekaterina Sadovaya, and Sergey Andreev: Modeling of SHF/EHF Radio-Wave Scattering for Curved Surfaces with Voxel Cone Tracing. IEEE Antennas and Wireless Propagation Letters, revision completed (2021)

  5. Melgarejo, Dick Carrillo, et al. "Optimizing Flying Base Station Connectivity by RAN Slicing and Reinforcement Learning." IEEE Access (2022).

  6. Zarini, Hosein, et al. "Xavier-Enabled Extreme Reservoir Machine for Millimeter-Wave Beamspace Channel Tracking." 2022 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2022.