Themes

WildBotics is based on three main pillars / themes which focus on open challenges in technologies to go beyond state-of-the-art and support conservation ecology, in particular with robotics-based sampling and AI-based interpretation:

Theme 1 - Robot system design for nature conservation (WP1) 

T1 will innovate on embodiment and control of robotics to enable complex environmental monitoring and sampling tasks: combining aerial and legged robotics to increase flexibility and spatial coverage of physical sampling from drones (PhD1); control of aerial robots for long-range sampling operations in complex environments, such as forests or open-sea (PhD2); development of mechanisms and base station for robotic collection and storage of multiple eDNA samples (PhD3); and the development of mechanisms for safe and robust perching of aerial robots on natural targets (PhD4). These PhDs will work together to develop a new generation of environmental robotics, adapted to the needs of ecology and nature conservation (Theme 3). The technological development will encompass highly versatile designs suitable for advanced, fully autonomous operations.

Theme 2 - Autonomy, perception and AI for complex natural environments (WP2) 

T2 focuses on advancing perception, vision and AI to support robotics autonomous operations and data processing for complex environmental monitoring and sampling tasks. This theme encompasses cutting-edge topics, namely robust AI-driven data fusion for UAV navigation in challenging scenes by combining diverse sensors in real-time (PhD5), deep learning to automate species detection and bio-event tracking within extensive audio datasets (PhD6), semantic SLAM for precise navigation to targeted sampling sites (PhD7) and decentralised, secure and resilient robotic swarms for real-time environmental monitoring (PhD8). Jointly, and in parallel to Theme 1, these PhDs will drive advancements in autonomous perception, navigation and collaborative operations, significantly contributing to environmental monitoring, conservation and ecosystem management.

Theme 3 - Analysis of large,  sample-based datasets for wildlife ecology & biodiversity conservation (WP3) 

T3 will focus on innovative applications of robotics-based sample collection and AI analytics to ecology and nature conservation. This includes improving our understanding of disease transmission in ungulate communities through robot-based faecal sampling in Kenya (PhD9), performing population health assessments for bowhead whales in West Greenland (PhD10), examining the impacts of anthropogenic noise on marine mammals in the North and the Baltic Sea (PhD11) and scaling up eDNA sampling in lakes and forestry areas for more comprehensive environmental monitoring (PhD12). PhDs in Theme 3 will be supported by innovative solutions coming from Theme 1 and 2 and will produce ecological knowledge that is currently too costly or time-consuming to produce at more-than-local scales. They will start their research using standard methods for sample collection and will progressively integrate the advanced software and hardware developed by PhDs in Themes 1 and 2 into their methodology. Additionally, they will generate datasets necessary for training technologies developed in other Themes, assessing the performance of new solutions in comparison to existing ones.