Architecture for Multi-Unmanned Aerial Vehicles based Autonomous Precision Agriculture Systems

Ebasa Temesgen; Nathnael Minyelshowa; Lebsework Negash

cs.ROMar 22, 2026

Architecture for Multi-Unmanned Aerial Vehicles based Autonomous Precision Agriculture Systems

Authors: Ebasa Temesgen, Nathnael Minyelshowa, Lebsework Negash

Organizations: University of Minnesota · Addis Ababa Institute of Technology (AAiT)

Abstract

The use of unmanned aerial vehicles (UAVs) in precision agriculture has seen a huge increase recently. As such, systems that aim to apply various algorithms on the field need a structured framework of abstractions. This paper defines the various tasks of the UAVs in precision agriculture and model them into an architectural framework. The presented architecture is built on the context that there will be minimal physical intervention to do the tasks defined with multiple coordinated and cooperative UAVs. Various tasks such as image processing, path planning, communication, data acquisition, and field mapping are employed in the architecture to provide an efficient system. Besides, different limitation for applying Multi-UAVs in precision agriculture has been considered in designing the architecture. The architecture provides an autonomous end-to-end solution, starting from mission planning, data acquisition and image processing framework that is highly efficient and can enable farmers to comprehensively deploy UAVs onto their lands. Simulation and field tests shows that the architecture offers a number of advantages that include fault-tolerance, robustness, developer and user-friendliness.

Figures & tables

Fig. 1: Multi-UAVs architecture, containing the communication scheme, different units and functionality starting from ground station control unit to the UAVs.

Fig. 2: Simulation environment created in Gazebo

Fig. 3: Simulation architecture used to test and verify the algorithms and codes.

Fig. 4: The test set-up used, starting with the dashboard to load a mission, the two quad-copters used and the sample image they collected

Fig. 5: The CNN model used to classify the collected image datas.

Fig. 6: The performance of the CNN network, showing the accuracy on fig. (a) increasing with each epoch and in fig. (b) the loss decreasing with the epochs.

Precision Agriculture

Precision agriculture uses spatial and temporal data, sensors, AI, and robotics to tailor crop monitoring and farm inputs to local conditions.

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Jul 7, 2026J/K move · Enter open · S save

Towards Reliable Aerial Ground Vehicle Collaboration: An Integrated Planning and Autonomy Framework for Field Deployment

Md Safwan Mondal, Luca Russo, James D. Humann, James M. Dotterweich+1

Department of Mechanical and Industrial Engineering, University of Illinois Chicago, IL, 60607 USA · DEVCOM Army Research Laboratory, Minneapolis, Minnesota, USA · DEVCOM Army Research Laboratory, Aberdeen Proving Grounds, Aberdeen, MD 21005 USA

Vehicle Routing Multi-Robot Coordination Dynamic Replanning

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Limited flight endurance significantly restricts the operational range of unmanned aerial vehicles (UAVs) in long duration missions such as surveillance and inspection, where multiple spatially distributed Areas of Interest (AOIs) must be visited. These tasks require efficient routing determining the sequence of visits which directly impacts mission time, energy consumption, and overall feasibility. Pairing UAVs with unmanned ground vehicles (UGVs) for mobile recharging offers a promising solution, but introduces a tightly coupled cooperative routing problem involving UAV route planning, UGV road constrained movement, energy management, and rendezvous scheduling under uncertainty. In this work, we present an integrated planning and autonomy framework for reliable field deployment. We formulate the problem as an energy constrained cooperative routing task and solve it using a Deep Reinforcement Learning (DRL) based planner that jointly optimizes the UAV visitation sequence and rendezvous locations with the UGV, outperforming baseline heuristics in minimizing total mission time. To bridge the gap between planning and execution, we introduce a standardized two layer YAML based mission API that captures environment states and structures lightweight, synchronized action sequences. This framework is supported by a complete autonomy stack using PX4/MAVSDK for UAV control and ROS 2/Nav2 for UGV navigation. Furthermore, we propose a lightweight Rendezvous Aware Replanner (RARP) that operates online to handle environmental uncertainties, reducing energy margin violations from 83.33% to 20.00%. The full system is validated through outdoor field experiments, demonstrating robust cooperative navigation and adaptability in dynamic tasks, including a search and rescue scenario with vision language model (VLM) based hazard detection

Abstract

Precision Agriculture

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