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.
 Jorge Peña Queralta, Li Qingqing, Fabrizio Schiano, Tomi Westerlund, "VIO-UWB-Based Collaborative Localization and Dense Scene Reconstruction within Heterogeneous Multi-Robot Systems", arXiv Preprint (2020) . (View) (Download) (arXiv)
 Li Qingqing, Jussi Taipalmaa, Jorge Peña Queralta, Tuan Nguyen Gia, Moncef Gabbouj, Hannu Tenhunen, Jenni Raitoharju, Tomi Westerlund, "Towards Active Vision with UAVs in Marine Search and Rescue: Analyzing Human Detection at Variable Altitudes", IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) , IEEE (2020) . (View) (Download) (Researchgate)
 Jorge Peña Queralta, Jussi Taipalmaa, Bilge Can Pullinen, Victor Kathan Sarker, Tuan Nguyen Gia, Hannu Tenhunen, Moncef Gabbouj, Jenni Raitoharju, Tomi Westerlund, "Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception and Active Vision", IEEE Access , IEEE (2020) . (View) (Download) (Google Scholar) (arXiv)
 Jorge Peña Queralta, Li Qingqing, Tuan Nguyen Gia, Hong-Linh Truong, Tomi Westerlund, "End-to-End Design for Self-Reconfigurable Heterogeneous Robotic Swarms", International Conference on Distributed Computing in Sensor Systems (DCOSS) , IEEE (2020) . (View) (Download) (Researchgate) (arXiv)
 Jorge Peña Queralta and Tomi Westerlund, "Blockchain-Powered Collaboration in Heterogeneous Swarms of Robots", Preprint (2020) . 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)