Coordinated motion of a hydraulic forestry crane and a vehicle using nonlinear model predictive control

Forests are a challenging environment for autonomous operations. The automatic driving and control of hydraulic forestry cranes have been studied earlier as separate problems. However, this study focuses on control where the boom and the forest machine are employed in a coordinated manner. Such control could be beneficial in certain tasks, such as forest cleaning operations where small trees are removed. To accomplish the coordinated actions, nonlinear model predictive control (NMPC) is utilized. NMPC is a control strategy based on numerical optimization which minimizes a given objective function. The coordinated control was tested with real hardware consisting of a tractor and a forestry crane. In the tests, the tractor has a target path and the tip of the boom has a target trajectory. These tests correspond to a real world situation where the forest machine has its own driving lines and the tool is used to accomplish a given task. Two different trajectories for the boom tip were tested with the target velocity of the boom tip being 0.5 m/s or 1.0 m/s. At these velocities, the average tracking error of the tractor ranged from 6.7 cm to 36.2 cm while the average error of the boom tip varied between 3.7 cm and 13.6 cm. In a separate test where only the tractor was controlled, its tracking error was 15.4 cm.