AeroPolis - Sustainable and autonomous carbon-neutral aerial ecosystems and energy solutions for future metropolises

Solving the challenges of future transportation requires a solid understanding of the megatrends in urbanization, digitalization and energy. Automated vehicles in particular create new zero-carbon services when combined with renewable energy and urban design and attractive opportunities are found in the development of autonomous aerial vehicles, addressing greener last-mile delivery services. Several challenges in this sector are also shared throughout other fields, ideally making the related automation and energy solutions universal. AeroPolis proposes a new take on the aerial logistics ecosystem by defining how autonomous urban-embedded micro-airports and open logistics-ready drones can redefine and advance the current technological possibilities. This is then combined with a digital platform that leverages distributed ledger technologies (DLTs), advanced aerial autonomy and ground-to-air coordination approaches, and integrated hybrid renewable solar+fuel cell energy solutions.

The multidisciplinary nature and the ambitious targets set by the project requires collaboration between partners having strong know-how and complementary expertise. AeroPolis brings together five research groups from four Finnish universities: Tampere University (TAU - urban design and urban informatics, industrial informatics and digital systems), the University of Turku (UTU - intelligent robotics, embedded and distributed systems), the University of Oulu (UOU - robotics, AI and cybersecurity) and Aalto University (thermophotonics and renewable energy solutions). We also partner with Forum Virium, the City of Helsinki's innovation unit to study the problem of urban integration of aerial logistics, while an experimental proof of concept is planned with our industrial partner Würth Finland Oy.

Funded by: Academy of Finland (#348480)

Related Publications

[1] Salma Salimi, Jorge Peña Queralta, Tomi Westerlund, "Towards Managing Industrial Robot Fleets with Hyperledger Fabric Blockchain and ROS 2", arXiv preprint (2022) . (View) (Download) (arXiv)

[2] Li Qingqing, Yu Xianjia, Jorge Peña Queralta and Tomi Westerlund, "Multi-Modal Lidar Dataset for Benchmarking General-Purpose Localization and Mapping Algorithms", , arXiv (2022) . (View) (Download) (arXiv)