Unmanned Ground Vehicle for the Identification of Trees Infested by Bark Beetles

This research presents an Unmanned Ground Vehicle for the identification of infested trees by bark beetles in mountain forests. The ground vehicle uses sensors for autonomous navigation and obstacle avoiding. Identification of infested trees is carried out by classifying the resin stains on the bark of unhealthy trees with a computer vision algorithm. This approach proposes tracking bark beetle spread in forest trees with image data of the infested trees considering resin sprouts as early indicators of the infestation in contrast to aerial monitoring which only detects trees in advanced stages. Terrain autonomous vehicle direction is controlled by changing the velocities left- and right-side wheel. A rotating LiDAR sensor is used to detect trees and avoidance objects. The dynamic model of the vehicle is presented and a control algorithm is proposed to control the path following and the stability proof is provided by a Lyapunov function. In order to show the performance of the algorithms of control and classification experimental results are presented in outdoor forest environment.