AURORAS - Project

Advanced autonomy for robots applied to the Primary Sector (AURORAS)

The project aims to address critical challenges in biodiversity, climate change, and sustainability by developing autonomous robotic systems applied to agriculture and livestock management. It focuses on advanced technologies for perception, mapping, and navigation in outdoor environments, promoting a more efficient and sustainable management of natural resources. The work aligns with Topic 8 of the call (food, bioeconomy, natural resources, agriculture, climate, and environment) and seeks to provide practical solutions that integrate emerging technologies with the real needs of the primary sector. Livestock management constitutes an essential component of the project, incorporating robots designed to support tasks such as monitoring animal behavior and efficiently tracking herds. Complementarily, systems will be developed to optimize soil monitoring, enabling more precise use of resources such as water and nutrients, and fostering the digitization and sustainability of agricultural ecosystems. To validate these solutions, two testbeds will be implemented: the first will focus on soil monitoring, evaluating how advanced perception and mapping systems enable the analysis of key factors such as soil moisture, nutrient levels, and land quality. The second will be dedicated to livestock control and management, utilizing multiple robots for tasks such as grazing management, movement control, and safe interaction with animals. Both testbeds will operate in unstructured environments under complex conditions, reflecting the everyday challenges faced by the sector. Given the complexity of the issues addressed, the project will be developed in coordination between two universities, each leading an interconnected subproblem within the primary sector. The first subproblem will evaluate the impact of advanced perception and mapping systems on the management of soil, crops, and livestock, optimizing their adoption and effectiveness in extensive livestock farming and open agricultural areas. The second subproblem will analyze how an advanced task planning and navigation system improves these areas and fosters practical integration into the proposed environments. Both universities bring complementary expertise: one focused on perception and mapping in agricultural environments, and the other on navigation and planning for robot control and monitoring. This coordinated approach ensures effective technological integration and a comprehensive analysis of its impact on the sector. The project has the potential to transform agriculture and livestock management by reducing environmental impact, increasing productivity through precision agriculture, and optimizing the management of natural resources. The use of heterogeneous robot fleets will address specific challenges in soil and livestock management, demonstrating the feasibility of these technologies in real-world scenarios. Furthermore, the project aims to revitalize rural areas by improving the quality of life for farmers and ranchers, encouraging the active participation of women in the sector, and promoting sustainable practices. This coordinated effort will not only drive technical and scientific advancements but also highlight the transformative impact of robotic innovation in the primary sector.

Perception and Mapping Systems for Transforming the Primary Sector (PERMAP)

The PERMAP project ("Perception and Mapping Systems for Transforming the Primary Sector") aims to develop and validate advanced robotic systems for perception and mapping that provide critical information on soil, crops, and livestock in agricultural and livestock environments. Within the framework of precision agriculture and livestock farming, this project represents an advancement in data monitoring to optimize natural resources, promote sustainability, and support biodiversity conservation. As a result, the project seeks to establish a guide of best practices based on experimentation, aimed at farmers and robot marketers, to facilitate the selection, deployment, and use of these technologies in real-world scenarios.

The project focuses on designing and validating perception and mapping systems capable of delivering accurate data on key parameters such as soil moisture, nutrient levels, plant growth, and livestock behavior. This information would be highly beneficial for the sector, particularly when combined with existing digital tools, enabling real-time decision-making, improved resource utilization, and reduced environmental impact.

To validate the proposed solutions, two testbeds will be implemented under real operational conditions. The first testbed will focus on soil monitoring, using robots equipped with advanced sensors to analyze features such as moisture, compaction, and nutrient distribution. The collected data will help optimize the use of water and fertilizers, minimizing waste and enhancing the sustainability of agricultural practices. The second testbed will focus on livestock management, where robots will perform tasks such as animal tracking and behavior monitoring in open environments. This testbed will demonstrate how robotic systems can be effectively integrated into livestock activities to enhance both productivity and animal welfare.

A key component of the project is the development of practical guidelines based on experimental results, providing clear recommendations for the use of robots in the primary sector. These guidelines will help farmers understand how to deploy and use technologies efficiently and serve as a reference for marketers interested in adapting their products to the specific needs of the sector. The project will identify the most suitable types of robots for specific activities, such as quadrupeds or wheeled robots, by analyzing results and interactions with animals. An essential requirement for the developed applications is their accessibility and ease of use by non-technical users, to increase their acceptance.

Advanced Task Planning and Navigation for Robotics in the Primary Sector (PLANNAV)

The PLANNAV project addresses two major challenges in robotics: navigation in outdoor environments with rugged terrain and changing weather conditions, and task planning for groups of autonomous robots. Regarding navigation, the project aims to develop advanced maps that include detailed information about the terrain, such as slopes, accessible areas, and features like moisture or composition, which will be dynamically updated during robot operations. Additionally, it seeks to improve localization beyond traditional GPS-based solutions, enhancing precision in complex environments, and to explore navigation under adverse conditions, such as low visibility or unstable terrain, a field that remains underexplored but is crucial for ensuring robots' functionality in everyday tasks. In the area of task planning, the project proposes applying symbolic planning techniques using modern planning and execution frameworks, many of which have been developed by our team, and extending their application to the problem of fleet coordination for robots, optimizing their collaboration in these challenging environments. The goal is to transfer advanced technologies, typically used in warehouse logistics, to the agricultural and livestock sectors, increasing the efficiency of farm management through the automation of tasks such as soil measurement, intelligently distributing them among the available robots. Our international experience in these areas positions us to lead this initiative, which represents a significant step toward integrating robots into traditional sectors, democratizing access to robotics, and benefiting users with limited technical expertise in the field.

List of publications:

Journal:

2025:

Conferences:

2025:

• EasyNav Simple Stack: a minimum set of plugins for a reliable navigation.Francisco Martín Rico, Francisco Miguel Moreno Olivo, Juan Carlos Manzanares Serrano, José Miguel Guerrero Hernández, Juan Sebastián Cely Gutiérrez, Esther Aguado González and Francisco José Romero Ramírez. (2025). In: Proceedings International Workshop on Physical Agents (WAF). September 4-5, 2025, Cartagena (Spain)
• HERDS: A ROS 2-based animal detection and herding system. Luis Prieto López, Jean Chrysostome Mayoko Biong, Sergio Sánchez de la Fuente, Miguel A. González-Santamarta, Francisco J. Rodríguez-Lera, and Lidia Sánchez-González. (2025). In: Proceedings 8th Iberian Robotics Conference ROBOT. 12-14 November, 2025, Porto (Portugal)

  Demos/Experiments:

  Public Repositories:

  • GitHub: Easy Navigation
  • GitHub: GPS Navigation for Ignition Fortress and Gazebo Classic
  • GitHub: Sheep Following System
  • GitHub: SHEPHERD: Real-Time Detection of Sheep and Predators for Autonomous Herding Robots