Enabling sustainable and cost-efficient semi-autonomous forest machine chain - Modeling, estimation and control for autonomous driving in terrain

Abstract

Traditionally, two humans operate the existing cut-to-length (CTL) forest machine chain, which includes a harvester and a forwarder. The harvester fells and cuts the trees into logs, whereas the forwarder carries the CTL logs to transportation sites. A fully loaded forwarder risks damaging the soft forest terrain. In addition, the rollover of forwarders is a real risk.

The motivation for the Autologger project was to introduce a novel forest machine chain concept to raise its productivity while minimizing terrain damage. This thesis aimed to study and develop smart harvester and autonomous forwarder functions. The purpose of the smart harvester is to build an initial three-dimensional (3D) model of the driving path. The autonomous forwarder, in turn, tracks the shown path, utilizing a 3D terrain model while avoiding vehicle rollover.

Two articles focus on estimating the 3D form of the solid path. The ground height was estimated without relying on a camera or LiDAR. The four papers focus on building vehicle models incorporating a 3D terrain model for autonomous driving in terrain. The vehicle model was suitable for exact non-linear vehicle simulations, state estimation, and nonlinear model predictive control (NMPC)-based 3D motion control with rollover avoidance.The solution to the smart harvester problem was to measure the wheel heights. The height-odometry algorithm measures the height profile of the path using wheel height measurements, the vehicle's attitude data, and its geometry. The aided height-odometry method filters the biases and errors from the height-odometry output using a priori 3D terrain map.

The solution to the autonomous forwarder problem was to utilize a six-degrees-of-freedom (6-DOF) vehicle model to simulate the dynamics of the off-road vehicles, as it has all the necessary components, i.e., forces and moments. A linear tire force model was adapted in the 6-DOF vehicle simulations, assuming the vehicle operates in the primary handling regime. The constituent force models were modified to include the 3D map information. The 6-DOF dynamical model for car-like vehicles was extended to center-articulated vehicles with 1-DOF articulation using a combined center of gravity (CG) approach. The vehicle simulator contributed to devising system calibration procedures, identifying actuator dynamics, and quantifying sensor delays. The simulations facilitated the development of a continuous-discrete extended Kalman filter (CDEKF) for state estimation, designing NMPC for 3D motion control, and studying rollover avoidance.

Polaris (a terrain car) was used as a case study to validate the (aided) height-odometry method(s) and augmented 6-DOF vehicle model through various experiments. The estimated wheel heights followed the ground truth within a few centimeters. Stable state estimates were obtained even with erroneous satellite navigation data in the forest. The real-time NMPC-based 3D motion control was ultimately demonstrated on the university's campus.