UAV Applications and Optimization
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Abstract
Abstract
Abstract Precision agriculture increasingly relies on coordinated unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) to perform field operations such as crop monitoring, pest surveillance, and variable-rate application. However, existing scheduling methods often adopt fixed task priorities and insufficiently account for the heterogeneous energy dynamics of aerial and ground platforms, which may lead to delayed responses when emergency tasks compete with routine operations for limited battery resources. To address these challenges, this study proposes an Energy-Constrained Multi-Stage Task Scheduling (ECMSTS) framework that integrates three tightly coupled mechanisms. First, a three-stage dynamic scheduling strategy divides the operational process into emergency, steady-state, and completion phases, with transitions determined by real-time energy states and task conditions rather than predefined schedules. Second, an LSTM-based predictor trained on 45 days of telemetry data is used to correct the physics-based estimate of remaining energy and provide updated energy information for scheduling decisions. Third, an enhanced NSGA-II optimizer and an energy-aware auction mechanism are jointly employed to balance energy consumption, task completion, and completion time while dynamically allocating urgent tasks to suitable platforms. By relying on a distributed edge computing layer for scheduling and control, the system's end-to-end scheduling delay can be consistently kept under 15 milliseconds. The actual operation results show that the framework can ensure that all emergency tasks are carried out strictly as planned. Compared with Standard NSGA-II, the total energy consumption was reduced by 21.7%. These results demonstrate that coupling energy-aware phase transitions with LSTM-corrected remaining-energy prediction provides a practical path toward sustainable, large-scale precision agriculture
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@article{Huang2026Energy,
title = {Energy-Constrained Multi-Stage Task Scheduling for Cooperative UAV-UGV Operations in Precision Agriculture},
author = {Anqi Huang and Qiaoman Zhang and Tingan Jin},
journal = {Engineering Research Express},
year = {2026},
doi = {10.1088/2631-8695/aeb7b7},
url = {https://doi.org/10.1088/2631-8695/aeb7b7}
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