Human-robot skills transfer interface for UAV-based precision pesticide in dynamic environments

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.

Automated summary

The global agricultural system heavily relies on synthetic pesticides to control diseases, weeds, and insects. Unmanned aerial vehicles (UAVs) play a major role in integrated pest management in precision agriculture, accurately distributing pesticides to combat crop diseases and pests. Through sensor fusion, dynamic movement primitives, and a novel control approach, UAVs are able to effectively navigate and spray pesticides in uncertain environments.