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Applied Sciences
Volume 16
Issue 2
10.3390/app16021143
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Proactive Path Planning Using Centralized UAV-UGV Coordination in Semi-Structured Agricultural Environments

by
Dimitris Katikaridis
1,2,3,
Lefteris Benos
1,*,
Dimitrios Kateris
1,
Elpiniki Papageorgiou
4,
George Karras
2,
Ioannis Menexes
3,
Remigio Berruto
5,
Claus Grøn Sørensen
6 and
Dionysis Bochtis
1,3
1
Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd., 57001 Thessaloniki, Greece
2
Department of Informatics and Telecommunications, University of Thessaly, 35131 Lamia, Greece
3
farmB Digital Agriculture S.A., Laertou 22, 55535 Thessaloniki, Greece
4
Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece
5
Interuniversity Department of Regional and Urban Studies and Planning (DIST), University of Turin, Viale Pier Andrea Mattioli 39, 10125 Torino, Italy
6
Department of Electrical and Computer Engineering, Aarhus University, 8000 Aarhus, Denmark
*
Author to whom correspondence should be addressed.
Appl. Sci. 2026, 16(2), 1143; https://doi.org/10.3390/app16021143 (registering DOI)
Submission received: 15 December 2025 / Revised: 14 January 2026 / Accepted: 21 January 2026 / Published: 22 January 2026
(This article belongs to the Special Issue The Use of Evolutionary Algorithms in Robotics)
Versions Notes

Abstract

Unmanned ground vehicles (UGVs) in agriculture face challenges in navigating complex environments due to the presence of dynamic obstacles. This causes several practical problems including mission delays, higher energy consumption, and potential safety risks. This study addresses the challenge by shifting path planning from reactive local avoidance to proactive global optimization. To that end, it integrates aerial imagery from an unmanned aerial vehicle (UAV) to identify dynamic obstacles using a low-latency YOLOv8 detection pipeline. These are translated into georeferenced exclusion zones for the UGV. The UGV follows the optimized path while relying on a LiDAR-based reactive protocol to autonomously detect and respond to any missed obstacles. A farm management information system is used as the central coordinator. The system was tested in 30 real-field trials in a walnut orchard for two distinct scenarios with varying worker and vehicle loads. The system achieved high mission success, with the UGV completing all tasks safely, with four partial successes caused by worker detection failures under afternoon shadows. UAV energy consumption remained stable, while UGV energy and mission time increased during reactive maneuvers. Communication latency was low and consistent. This enabled timely execution of both proactive and reactive navigation protocols. In conclusion, the present UAV–UGV system ensured efficient and safe navigation, demonstrating practical applicability in real orchard conditions.
Keywords: agricultural robotics; multi-robot collaboration; farm management information system; computer vision; situational awareness; safety redundancy; operational cost agricultural robotics; multi-robot collaboration; farm management information system; computer vision; situational awareness; safety redundancy; operational cost

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MDPI and ACS Style

Katikaridis, D.; Benos, L.; Kateris, D.; Papageorgiou, E.; Karras, G.; Menexes, I.; Berruto, R.; Sørensen, C.G.; Bochtis, D. Proactive Path Planning Using Centralized UAV-UGV Coordination in Semi-Structured Agricultural Environments. Appl. Sci. 2026, 16, 1143. https://doi.org/10.3390/app16021143

AMA Style

Katikaridis D, Benos L, Kateris D, Papageorgiou E, Karras G, Menexes I, Berruto R, Sørensen CG, Bochtis D. Proactive Path Planning Using Centralized UAV-UGV Coordination in Semi-Structured Agricultural Environments. Applied Sciences. 2026; 16(2):1143. https://doi.org/10.3390/app16021143

Chicago/Turabian Style

Katikaridis, Dimitris, Lefteris Benos, Dimitrios Kateris, Elpiniki Papageorgiou, George Karras, Ioannis Menexes, Remigio Berruto, Claus Grøn Sørensen, and Dionysis Bochtis. 2026. "Proactive Path Planning Using Centralized UAV-UGV Coordination in Semi-Structured Agricultural Environments" Applied Sciences 16, no. 2: 1143. https://doi.org/10.3390/app16021143

APA Style

Katikaridis, D., Benos, L., Kateris, D., Papageorgiou, E., Karras, G., Menexes, I., Berruto, R., Sørensen, C. G., & Bochtis, D. (2026). Proactive Path Planning Using Centralized UAV-UGV Coordination in Semi-Structured Agricultural Environments. Applied Sciences, 16(2), 1143. https://doi.org/10.3390/app16021143

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