Framework for Guidance, Navigation, and Control of Cooperative Multi-robot Systems in Agriculture Environments


Challenges in achieving food security are exacerbated – among others – by climate change. Conventional smart and precision-farming applications, mainly enabled by combining Remote Sensing, Machine Learning (ML) and Internet of Things (IoT) technologies, may no longer suffice and therefore, the agricultural sector requires innovative solutions to improve the efficiency of agricultural operations. In this context, agricultural robots will undoubtedly become essential for farming in the future. These robots will be able to undertake tasks like harvesting and picking, pruning and seeding, etc., thus reducing the workload and hazard for farmers, while at the same time increasing yield production and quality, and reducing waste and labour costs.

Cooperative Multi-robot Systems (MRSs) are particularly relevant. Robot-to-robot cooperation minimizes human error, enabling a fully autonomous system. However, coordinating multiple robots to work collaboratively in agricultural settings presents challenges in communication, coordination and task allocation, which will be addressed by the framework of this project.


Before such a system can be deployed in fields, several technological challenges must be overcome. The literature review reveals that robot-to-robot cooperation for executing diverse tasks through an intelligent management system remains underexplored. The AgriROS project aims to introduce several innovative aspects, setting it apart in the field..

One focus is Collaborative Decision-Making: Advanced algorithms will be developed for collaborative decision-making among multiple robotic entities. These algorithms will enable the robots to work together efficiently, allowing them to share information and adapt their actions based on the evolving agricultural environment.

Adaptive Navigation is another key aspect: The framework will incorporate adaptive navigation algorithms, enabling robotic systems to navigate through complex and dynamic agricultural landscapes. This adaptability is crucial for addressing challenges such as uneven terrains, changing crop layouts and unexpected obstacles.

Moreover, the framework will develop Interpolate Controllers: These are controller blocks  applicable to various types of mobile robot motion models (aerial and ground).

Scalability and Modularity are also priorities: The framework aims to be scalable and modular, allowing the integration of different types of robots with varying capabilities. This innovation will enable robotic fleets to be customized based on specific agricultural needs and resource availability.

Finally, AgriROS will develop an ROS2-based Digital Twin (DT) tool, which can reproduce the cooperative MRSs on a virtual farm. This DT will provide a digital representation of the entire farm, including IoT devices in the field.


AgriROS has the ambition to advance the development of cooperative MRSs for agriculture. The main outcomes will be the development of methods for intelligent guidance and control systems, as well as methods for highly accurate navigation.

The latter will be implemented as ROS2-SW packages, and thus, are expected to have a significant impact on the robotics community. By developing a Digital Twin of the cooperative MRSs and providing an IoT integration tool, AgriROS will push towards the full digitalization of fields.

This innovative project will tackle the technical challenges of the target system, ranging from fundamental research (design of methods, development of SW) to prototyping and implementation in the field. As a result, the project outcomes are expected to yield socio-economic benefits for the agricultural sector, both within and beyond Luxembourg. These benefits range from increasing efficiency and productivity to optimizing resource utilization.



Research domains
  • Environment

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