Autonomous Decision Making in Very Long Traverses (ADE), H2020 Space Robotics Technologies

Objective is develop and test in a representative analogue a rover system suitable to increase data collection, perform autonomous long traverse surface exploration, guarantee fast reaction, mission reliability, and optimal exploitation of resources. ADE specific objectives:

  • Autonomous long range navigation with high reliability.
  • Consistent data detection / avoiding un-detection of interesting data
  • Autonomous decision making capabilities in presence of conflicts
  • Demonstration in a representative environment.

Dissemination video for the H2020 project Autonomous Decision Making in very long traverses Autonomous Decision Making in very long traverses (ADE) , Video courtesy GMV; Music “Battle of the kings” composed by Per Kiilstofte (Machinimasound)


Agricultural Interoperability and Analysis System (ATLAS), H2020 DT-ICT-08-2019 – Agricultural digital integration platforms

The goal of ATLAS is to achieve a new level of interoperability of agricultural machines, sensors and data services and enable farmers to have full control over their data and decide which data is shared with whom in which place. The technology developed in ATLAS will be tested and evaluated within pilot studies on a multitude of real agricultural operations across Europe along 4 relevant use cases:

  • precision agriculture tasks along with implementation of robotic technologies.
  • sensor-driven irrigation management
  • data-based soil management
  • behavioural analysis of livestock.


Simultaneous Safety and Surveying for Collaborative Agricultural Vehicles (S3-CAV), ERA-NET ICT-AGRI2 action

Precision farming relies on the ability to accurately locate the crops or leaves with problems and to accurately apply a local remedy without wasting resources or contaminating the environment. This project develops a unifying framework allowing incorporation of many different types of sensor data, methods for creating 3D maps and maximising map accuracy to facilitate operations on a narrow scale with a smaller environment footprint, methods for combining this data to make relevant information easily visible to the farmer, and methods for incorporating real-time sensor data into historical data both to increase precision during applications and to provide fast automated safety responses.

Ambient Awareness for Autonomous Agricultural Vehicles (QUAD-AV) , ERA-NET ICT-AGRI action


Autonomous vehicles are being increasingly adopted in agriculture to improve productivity and efficiency. For an autonomous agricultural vehicle to operate safely, environment perception and interpretation capabilities are fundamental requirements. The present project will focus on the development of sensors and sensor processing methods to provide an autonomous agricultural vehicle with such ambient awareness. The “obstacle detection” problem will be specifically addressed.
The obstacles that might be encountered in the field can be separated into four overall categories that should be detected and handled in different ways: positive obstacles, negative obstacles, moving people/animals/obstacles, and difficult terrain. Further, obstacles may vary greatly from situation to situation, depending on type of crop, fruit, vegetable or plant grown, curvature of landscape as well as other factors. Owing to the variety of situations and problems that may be encountered, no sensor exists that can guarantee reliable results in every case. Any candidate sensor has its strengths and drawbacks. Therefore, a complementary sensor suite should be used to gain the best performance.
The idea of this project is that of using different sensor modalities and multi-algorithm approaches to detect the various kinds of obstacles and to build an obstacle database that can be used for vehicle control. For instance, bearing and distance to the nearest collision can be estimated and used by the path planner to change route or to lower the speed if an obstacle is in close proximity to the vehicle’s planned path.