Unmanned Ground Vehicle for Identifying Trees Infested with Bark Beetles

This research presents an unmanned ground vehicle for identifying infested trees by bark beetles in mountain forests. The ground vehicle uses sensors for autonomous navigation and obstacle avoidance. The 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 of left- and right-side wheels. A rotating LiDAR sensor is used to detect trees and avoid objects. The dynamic model of the vehicle is presented, and a control algorithm is proposed for path-following. Moreover, the stability of the system is proven using a Lyapunov function. In order to demonstrate the performance of the control and classification algorithms, experimental results from an outdoor forest environment are presented.