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.
Katikaridis et al. (2026) studied this question.