Increase INESC TEC’s capabilities to improve and develop new approaches for autonomous underwater manipulation allowing its application in INESC TEC unmaned underwater vehicles (UUVs) for field robotics missions underwater (e.g: mining, oil and gas, security…).
Increase INESC TEC’s capabilities for robust and resilient long-term navigation and autonomy through robot perception, capable of enabling numerous field applications using autonomous/semi-autonomous robots.
Increase INESC TEC's competences in ground field scene reconstruction and ground field context awareness exploiting semantic information extracted via deep learning techniques. Semantic mapping is indeed the result of synergic interactions between deep learning semantic scene interpretation and mapping. To overall objective is to show how semantics can provide the proper priors for a better and more accurate reconstruction of the environment and, at the same time, the reconstruction itself can be used to boost semantic interpretation, e.g., via deep learning on 3D structures such as graphs.
To develop and deploy novel multi-robot cooperative target tracking methods for a team of aerial vehicles addressing coastal survey, search and rescue applications.
To develop cooperation strategies for heterogeneous teams of robots involving aerial and water surface vehicles.
Applications include autonomous landing on floating platforms for self-recharging, cooperative detection of people in dangerous situations (e.g., drowning...)