Purpose

Nowadays, the global agricultural system is highly dependent on the widespread use of synthetic pesticides to control diseases, weeds and insects. The unmanned aerial vehicle (UAV) is deployed as a major part of integrated pest management in a precision agriculture system for accurately and cost-effectively distributing pesticides to resist crop diseases and insect pests.

Design/methodology/approach

With multimodal sensor fusion applying adaptive cubature Kalman filter, the position and velocity are enhanced for the correction and accuracy. A dynamic movement primitive is combined with the Gaussian mixture model to obtain numerous trajectories through the teaching of a demonstration. Further, to enhance the trajectory tracking accuracy under an uncertain environment of the spraying, a novel model reference adaptive sliding mode control approach is proposed for motion control.

Findings

Experimental studies have been carried out to test the ability of the proposed interface for the pesticides in the crop fields. The effectiveness of the proposed interface has been demonstrated by the experimental results.

Originality/value

To solve the path planning problem of a complex unstructured environment, a human-robot skills transfer interface is introduced for the UAV that is instructed to follow a trajectory demonstrated by a human teacher.

中文翻译：

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动态环境中基于无人机的精密农药的人机交互技能转移界面

目的

如今，全球农业系统高度依赖于合成农药的广泛使用来控制疾病，杂草和昆虫。无人驾驶飞机（UAV）被部署为精密农业系统中虫害综合管理的重要组成部分，用于准确而经济地分配农药以抵抗农作物病虫害。

设计/方法/方法

通过采用自适应库尔曼卡尔曼滤波器的多模式传感器融合，可以提高位置和速度，从而提高校正和准确性。通过演示的教学，将动态运动原语与高斯混合模型结合在一起，以获得许多轨迹。此外，为提高在不确定环境下喷涂的轨迹跟踪精度，提出了一种新型的模型参考自适应滑模控制方法。

发现

已经进行了实验研究以测试所提出的界面对农作物中的农药的能力。实验结果证明了所提出接口的有效性。

创意/价值

为了解决复杂的非结构化环境中的路径规划问题，为无人机引入了人机交互技能转移界面，指示该界面遵循人类老师演示的轨迹。