SeaPASS - Enhanced Situational Awareness for Cargo Boats
The main objective of SeaPASS is to develop a dynamic, smart and scalable platform for assisted waterborne navigation. We aim at developing a platform architecture consisting on a set of algorithms for control, communication and decision making, together with the definition of a set sensors that can be installed onto any ship worldwide. More specifically, we aim at providing a smart and advanced driving assistance platform for large ships that can enhance situational awareness and provide extensive and comprehensive data about the environment around the ship, to complement existing sensor data among the ship itself.
At SeaPASS, we explore the following topics:
(1) Dynamic allocation of computing resources and roles in a changeable and heterogeneous group of semi-autonomous vehicles and robots, exploiting the advantages of hybrid fog and mobile edge architectures. This relies on efficient data fusion, compression and robust communication.
(2) Integration of machine learning techniques for sensor fusion within the edge nodes, analyzing data from different sources: sensors aboard the main ship, companion boats and drones.
(3) Developing new algorithms in the area of swarm coordination and formation control when communication link in unreliable and only external information about the state of other agents is available to the different nodes in the network.
 Jorge Peña Queralta, Tuan Nguyen Gia, Hannu Tenhunen and Tomi Westerlund, "Collaborative Mapping with IoE-based Heterogeneous Vehicles for Enhanced Situational Awareness", IEEE Sensors Applications Symposium 2019 , IEEE (2019) . (View) (Download) (Researchgate)
 Li Qingqing, Jorge Peña Queralta, Tuan Nguyen Gia, Zhuo Zou, Hannu Tenhunen and Tomi Westerlund, "Detecting Water Reflection Symmetries in Point Clouds for Camera Position Calibration in Unmanned Surface Vehicles", The 19th International Symposium on Communications and Information Technologies (ISCIT) , IEEE (2019) . (View) (Download) (Researchgate)
 Li Qingqing, Fu Yuhong, Jorge Pena Queralta, Tuan Nguyen Gia, Hannu Tenhunen, Zhou Zou and Tomi Westerlund, "Edge Computing for Mobile Robots:Multi-Robot Feature-Based Lidar Odometry with FPGAs", The 12th International Conference on Mobile Computing and Ubiquitous Networking (2019) . (View) (Download) (Researchgate)