Nowadays, many precision farming applications rely on the use of GNSS-RTK. However, when it comes to autonomous agricultural vehicles, GNSS cannot be used as a stand-alone system for positioning. To ensure high availability and robustness of the positioning solution, GNSS-RTK must be fused with additional sensors. This paper presents a novel sensor fusion algorithm tailored to tracked agricultural vehicles. GNSS-RTK, an IMU and wheel speed sensors are fused in an error-state Kalman filter to estimate position and attitude of the vehicle. An odometry model for tracked vehicles is introduced which is used to propagate the filter state. By using both IMU and wheel speed sensors, specific motion characteristics of tracked vehicles such as slippage can be included in the dynamic model. The presented sensor fusion algorithm is tested at a composting site using a tracked compost turner. The sensor measurements are recorded using the Robot Operating System (ROS). To analyze the achievable accuracies for position and attitude of the vehicle, a precise reference trajectory is measured using two robotic total stations. The resulting trajectory of the error-state filter is then compared to the reference trajectory. To analyze how well the proposed error-state filter is suited to bridge GNSS outages, GNSS outages of 30 s are simulated in post-processing. During these outages, the vehicle’s state is propagated using the wheel speed sensors, IMU, and the dynamic model for tracked vehicles. The results show that after 30 s of GNSS outage, the estimated horizontal position of the vehicle still has a sub-decimetre accuracy.

This paper develops an algorithm to compute optimal routes for an autonomous compost turner. In commercial composting, the material to be composted is piled up in large heaps called windrows and turned regularly by compost turners. The environment at the composting site is constantly changing, as the locations of the windrows change with each turning procedure. Therefore, we propose a novel method that automatically computes routes on a composting plant from LiDAR data. The LiDAR is mounted on the compost turner together with a dual-antenna GNSS receiver, an IMU, and rotary encoders. An extended Kalman filter is used to obtain the vehicle’s pose. Through direct georeferencing, a global point cloud is obtained. The routing algorithm crops, segments, and filters the point cloud until the points along the ridge of each windrow remain. These points are used to compute the optimal routes along each windrow. Furthermore, a user can select the windrows which need to be turned and the algorithm then computes the most efficient path for the compost turner, which also includes the passages between the windrows. The method was tested within a simulation environment using a 3D model of the composting site. The results show that the algorithm detects the windrows and computes the routes with sufficient accuracy for autonomous compost turning.

In a sustainable circular economy, the composting of organic waste plays an essential role. This paper presents the design and technical development of a smart and self-driving compost turner. The architecture of the hardware, including the sensor setup, navigation module, and control module, is presented. Furthermore, the methodological development using model-based systems engineering of the architecture of concepts, models, and their subsequent software integration in ROS is discussed. The validation and verification of the overall system are carried out in an industrial environment using three scenarios. The capabilities of the compost turner are demonstrated by requiring it to autonomously follow pre-defined trajectories at the composting plant and perform required composting tasks. The results prove that the autonomous compost turner can perform the required activities. In addition to autonomous driving, the compost turner is capable of intelligent processing of the compost data and of transferring, visualizing, and storing them in a cloud server. The overall system of the intelligent, autonomous compost turner can provide essential leverage for improving sustainability efforts, thus contributing substantially to an environmentally friendly and sustainable future.

Mechanical compost turning produces short-lived greenhouse-gas concentration peaks, yet the physical mechanism linking particle agitation to near-field gas transport remains poorly understood. A two-stage numerical framework combining the discrete element method (DEM) and computational fluid dynamics (CFD) was developed to investigate this process during industrial windrow turning. In Stage 1, a coupled CFD–DEM model resolved rotor-driven particle motion and the resulting mechanically generated airflow. The time-averaged airflow field was then transferred to a Volume-of-Fluid multiphase model to simulate CO₂ and CH₄ transport. The framework was evaluated against field measurements obtained from a commercially operated eWender windrow turner. Although turning produced a visually prominent plume, measured and simulated near-field air velocities remained predominantly below 1 m/s, indicating a low-velocity transport regime rather than strong atmospheric convection. CO₂ and CH₄ concentrations exhibited short transient bursts during active turning, while measured CO₂ concentrations remained predominantly within approximately 1500–4000 ppm. Simulated air velocities and CO₂ concentrations fell within the corresponding measured ranges. The results indicate that mechanically generated airflow contributes mainly to local gas redistribution and near-field concentration enhancement rather than sustained or far-reaching transport. Accordingly, the visually prominent plume and short-duration concentration peaks observed during turning should not be interpreted alone as evidence of proportionally increased total greenhouse-gas emissions.