AutoSOS - Autonomous Drones Supporting Maritime Search and Rescue
Rescue vessels are the main actors in maritime safety and rescue operations. Aerial drones bring a significant advantage into this scenario. Therefore, AutoSOS develops an autonomous multi-robot search and rescue assistance platform capable of sensor fusion and object detection in embedded devices using novel lightweight AI models. The platform performs reconnaissance missions for initial assessment of the environment using novel adaptive deep learning algorithms that efficiently use the available sensors and computational resources on drones and rescue vessel. When drones find potential objects, they will send their sensor data to the vessel to verity the findings with increased accuracy. The actual rescue and treatment operation are left as the responsibility of the rescue personnel. The drones will autonomously reconfigure their spatial distribution to enable multi-hop communication, when a direct connection between a drone transmitting information and the vessel is unavailable.
The AutoSOS project will base its research exploration on the previous work in the development of the world’s first autonomous ferry (Brighthouse Intelligence Oy), research on hybrid aerial-surface-underwater autonomous systems for rescue operations (Tampere University and Alamarin-Jet Oy, aCOLOR project), and algorithms for drones in the areas of formation control and autonomous cooperation in multi-agent systems (University of Turku). The project will have the support of experts in the area of search and rescue (SAR) robotics (Universidad de Málaga, Spain), and in the fields of artificial intelligence and multi-drone systems (Aristotle University of Thessaloniki, Greece). Moreover, public authorities have shown their interest in the project and field tests will be coordinated with the Alicante Search and Rescue Group (Spain), where maritime search operations are common around the year. The group is in charge of rescue operations in a province with over 1.8M population and over 5M tourists annually.
 Qingqing Li, Paavo Nevalainen , Jorge Peña Queralta , Jukka Heikkonen and Tomi Westerlund, "Localization in Unstructured Environments: Towards Autonomous Robots in Forests with Delaunay Triangulation", Remote Sensing , MDPI (2020) . (View) (Download) (Researchgate) (arXiv)
 Victor Kathan Sarker, Li Qingqing and Tomi Westerlund, "3D Perception with Low-cost 2D LIDAR and Edge Computing for Enhanced Obstacle Detection", IEEE International Conference on Industrial Cyber-Physical Systems (ICPS) , IEEE (2020) . (View) (Download)
 Jorge Pena Queralta, Li Qingqing, Tuan Nguyen Gia, Hong-Linh Truong, Tomi Westerlund, "End-to-End Design for Self-Reconfigurable Heterogeneous Robotic Swarms", arXiv preprint , arXiv (2020) . (View) (Download) (Researchgate) (arXiv)
 Jorge Peña Queralta and Tomi Westerlund, "Blockchain-Powered Collaboration in Heterogeneous Swarms of Robots", Preprint (2019) . Presented at the 2019 Symposium on Blockchain for Robotics and AI Systems, MIT Media Lab. (View) (Download) (Researchgate) (Google Scholar) (arXiv)
 Jorge Peña Queralta, Carmen Martínez Almansa, Fabrizio Schiano, Dario Floreano, Tomi Westerlund, "UWB-based System for UAV Localization in GNSS-Denied Environments: Characterization and Dataset", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , IEEE (2020) . (View) (Download) (arXiv)
 Jorge Peña Queralta, Li Qingqing, Zhuo Zou, and Tomi Westerlund, "Enhancing Autonomy with Blockchain and Multi-Access Edge Computing in Distributed Robotic Systems", FMEC 2020: International Conference on Fog and Mobile Edge Computing , IEEE (2020) . (View) (Download) (Researchgate)