Robotics
Work description
Multi-Robot Autonomous Navigation in GNSS Denied Enviroments). Autonomous navigation in GNSS-denied environments remains a major challenge for robotic systems due to localisation drift, perceptual degradation, limited situational awareness, and communication constraints. These challenges become particularly relevant in ocean environments, where reliable GNSS coverage may be unavailable or degraded, environmental conditions are highly dynamic, and autonomous platforms must operate for extended periods with limited external infrastructure. This PhD scholarship will focus on developing cooperative autonomous navigation methods for teams of robotic platforms operating in GNSS-denied environments, with particular emphasis on applications in ocean and maritime settings. The research will investigate how multiple autonomous agents can cooperate by sharing complementary sensing and localisation information to improve navigation accuracy, robustness, and resilience. The research will explore multimodal perception and localisation architectures that combine sensors such as cameras, inertial measurement units, acoustic sensing, radar, and inter-agent ranging technologies. Depending on the operational scenario, additional sensing modalities relevant to maritime environments may also be considered. Sensor fusion techniques will be developed to provide reliable localisation, mapping, obstacle detection, and situational awareness under challenging conditions, including changing illumination, adverse weather, wave-induced motion, limited visibility, and sparse environmental features. A central research topic will be the development of distributed and cooperative localisation and SLAM frameworks. These methods will enable multiple autonomous platforms to exchange local observations, relative measurements, and map information in order to maintain consistent estimates of their positions and surroundings without relying on GNSS or fixed external infrastructure. The PhD will also investigate cooperative navigation strategies involving heterogeneous robotic systems, potentially including unmanned aerial vehicles (UAVs), autonomous surface vehicles (ASVs), and autonomous underwater vehicles (AUVs). Cooperation between aerial, surface, and underwater platforms can provide complementary sensing capabilities and enable persistent monitoring, inspection, mapping, and exploration of complex ocean environments. From a scientific and technological perspective, the proposed methods will be evaluated not only in terms of localisation and navigation accuracy, but also with respect to computational efficiency, scalability, communication requirements, robustness, and real-time feasibility. Algorithms will therefore be designed with deployment on embedded and edge computing platforms in mind, considering the energy and computational constraints of autonomous robotic systems. Learning-based and self-supervised approaches may also be investigated to improve perception, sensor fusion, and adaptation to changing environmental conditions. Such methods can enable autonomous systems to adjust their navigation strategies to different sensing conditions and operational scenarios while reducing their dependence on extensive manually labelled datasets. The proposed approaches will be validated through simulation and experimental trials involving realistic GNSS-denied scenarios, with a particular focus on ocean and maritime applications. Potential applications include marine environmental monitoring, offshore infrastructure inspection, search and rescue, oceanographic data collection, cooperative exploration, and persistent surveillance.
Academic Qualifications
Master's degree in Electrical and Computer Engineering or a related scientific field.
Minimum profile required
• Hold a Master’s degree in (Electrical/Electronic and Computer Engineering) or a related field, with valorization in (Robotics and/or Autonomous Systems) or a related field, and have a scientific and professional CV that demonstrates a suitable profile for the activities to be carried out.• Not have previously been awarded a doctoral grant or a doctoral grant in companies directly funded by FCT, regardless of its duration.• Reside permanently and habitually in Portugal at the start date of the period of the work plan to be carried out abroad, should the proposed work plan include a period at foreign institutions (mixed grants). This requirement applies to both Portuguese and foreign citizens.• Not hold a doctoral degree.
Preference factors
The candidate's experience will be especially valued in one of the following research lines: Experience in Computer Vision, Cooperative Navigation, Robotics & Artificial Intelligence.
Application Period
Since 01 Oct 2026 to 15 Oct 2026
Centre
Robotics and Autonomous Systems
Scientific Advisor
Hugo Miguel Silva