Collaborative Trajectory Planning for Stereoscopic Agricultural Multi-UAVs Driven by the Aquila Optimizer

Stereoscopic agriculture, as an advanced method of agricultural production, poses new challenges for multi-task trajectory planning of unmanned aerial vehicles (UAVs). To address the need for UAVs to perform multi-task trajectory planning in stereoscopic agriculture, a multi-task trajectory planning model and algorithm (IEP-AO) that synthesizes flight safety and flight efficiency is proposed. Based on the requirements of stereoscopic agricultural geomorphological features and operational characteristics, the multi-task trajectory planning model is ensured by constructing targeted constraints at five aspects, including the path, slope, altitude, corner, energy and obstacle threat, to improve the effectiveness of the trajectory planning model. And combined with the path optimization algorithm, an Aquila optimizer (IEP-AO) based on the interference-enhanced combination model is proposed, which can help UAVs to improve the trajectory search capability in complex operation space and large-scale operation tasks, and jump out of the locally optimal trajectory path region timely, to generate the optimal trajectory planning plan that can adapt to the diversity of the tasks and the flight efficiency. Meanwhile, four simulated flights with different operation scales and different scene constraints were conducted under the constructed real 3Dimension scene, and the experimental results can show that the proposed multi-task trajectory planning method can meet the multi-task requirements in stereoscopic agriculture and improve the mission execution efficiency and agricultural production effect of UAV.