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The extraction of timber is expensive, energy intensive, and potentially damaging to the forest soil. Machine development aims to mitigate risks for environmental impact and decrease energy consumption while maintaining or increasing cost efficiency. The development of rubber-tracked forwarders has gained renewed interest, not least due to climate change leading to unreliable weather in combination with low tolerance for soil damage. The increased cost of rubber tracks compared to wheels is believed to be compensated by higher driving speed enabled by semi-active suspension. Thus, the aim of this study was to theoretically investigate how the productivity and cost efficiency of rubber-tracked forwarders are affected by variations in driving speed and machine costs. The calculations were made with fixed stand parameters, to evaluate performance in well-defined working conditions, and with parameters from 2500 final felling stands in central Sweden, to evaluate performance in varied working conditions. Scenarios were compared to a baseline corresponding to mid-sized wheeled forwarders. The results show higher productivity with the increased driving speed enabled by rubber tracks and suspension at all extraction distances, with larger differences at long extraction distances. Assuming a 15% higher machine price for the rubber-tracked forwarder and a variable cost increase proportional to speed increase, extraction costs break even with the baseline at 400 m and 700 m extraction distance for moderate and fast driving speed, respectively. Furthermore, a rubber-tracked forwarder is likely to enable access to a larger part of the harvest area during longer seasons. For the studied set of stands, the year-round accessible volumes are estimated to increase from 9% to 92% with a rubber-tracked forwarder. With rubber tracks instead of wheels, good accessibility has the potential to be combined with low soil impact and cost efficiency in a favourable way for both industry and ecosystem. [ABSTRACT FROM AUTHOR]

We explore multi-log grasping using reinforcement learning and virtual visual servoing for automated forwarding in a simulated environment. Automation of forest processes is a major challenge, and many techniques regarding robot control pose different challenges due to the unstructured and harsh outdoor environment. Grasping multiple logs involves various problems of dynamics and path planning, where understanding the interaction between the grapple, logs, terrain, and obstacles requires visual information. To address these challenges, we separate image segmentation from crane control and utilise a virtual camera to provide an image stream from reconstructed 3D data. We use Cartesian control to simplify domain transfer to real-world applications. Because log piles are static, visual servoing using a 3D reconstruction of the pile and its surroundings is equivalent to using real camera data until the point of grasping. This relaxes the limits on computational resources and time for the challenge of image segmentation, and allows for data collection in situations where the log piles are not occluded. The disadvantage is the lack of information during grasping. We demonstrate that this problem is manageable and present an agent that is 95% successful in picking one or several logs from challenging piles of 2–5 logs. [ABSTRACT FROM AUTHOR]

The reinforcement learning control of an underground loader was investigated in a simulated environment by using a multi-agent deep neural network approach. At the start of each loading cycle, one agent selects the dig position from a depth camera image of a pile of fragmented rock. A second agent is responsible for continuous control of the vehicle, with the goal of filling the bucket at the selected loading point while avoiding collisions, getting stuck, or losing ground traction. This relies on motion and force sensors, as well as on a camera and lidar. Using a soft actor–critic algorithm, the agents learn policies for efficient bucket filling over many subsequent loading cycles, with a clear ability to adapt to the changing environment. The best results—on average, 75% of the max capacity—were obtained when including a penalty for energy usage in the reward. [ABSTRACT FROM AUTHOR]

Systems for transport and processing of granular media are challenging to analyse, operate and optimise. In the mining and mineral processing industries, these systems are chains of processes with a complex interplay among the equipment, control and processed material. The material properties have natural variations that are usually only known at certain locations. Therefore, we explored a material-oriented approach to digital twins with a particle representation of the granular media. In digital form, the material is treated as pseudo-particles, each representing a large collection of real particles of various sizes, shapes and mineral properties. Movements and changes in the state of the material are determined by the combined data from control systems, sensors, vehicle telematics and simulation models at locations where no real sensors could see. The particle-based representation enables material tracking along the chain of processes. Each digital particle can act as a carrier of observational data generated by the equipment as it interacts with the real material. This make it possible to better learn the material properties from process observations and to predict the effect on downstream processes. We tested the technique on a mining simulator and demonstrated the analysis that can be performed using data from cross-system material tracking. [ABSTRACT FROM AUTHOR]