Exploring the feasibility of autonomous forestry operations: Results from the first experimental unmanned machine

Abstract

This article presents a study on the world's first unmanned machine designed for autonomous forestry operations. In response to the challenges associated with traditional forestry operations, we developed a platform equipped with essential hardware components necessary for performing autonomous forwarding tasks. Through the use of computer vision, autonomous navigation, and manipulator control algorithms, the machine is able to pick up logs from the ground and manoeuvre through a range of forest terrains without the need for human intervention. Our initial results demonstrate the potential for safe and efficient autonomous extraction of logs in the cut‐to‐length harvesting process. We achieved a high level of accuracy in our computer vision system, and our autonomous navigation system proved to be highly efficient. This research represents a significant milestone in the field of autonomous outdoor robotics, with far‐reaching implications for the future of forestry operations. By reducing the need for human labor, autonomous machines have the potential to increase productivity and reduce labor costs, while also minimizing the environmental impact of timber harvesting. The success of our study highlights the potential for further development and optimization of autonomous machines in the forestry industry.

Real-time target point identification and automated log grasping by a forwarder, using a single stereo camera for both object detection and boom-tip control

“…The suggested method is simpler compared to the approaches used in previous studies, where the camera is used to position the log in the coordinate system of the boom (Gietler et al 2022;Ayoub et al 2023;Li and Lideskog 2023;La Hera et al 2024). Since our study served as an initial test of a new method, we have not assessed the accuracy of YOLOv5 itself in determining the correct coordinates from image data.…”

“…Previous studies have demonstrated the potential of autonomous full-scale, real-time, offroad driving with forwarders (Gelin et al 2021;La Hera et al 2024). In simulations, reinforcement learning methods have been tested for loading one or more logs (Andersson et al 2021;Wallin et al 2024).…”

“…Recently, studies on full-scale, real-time experiments of autonomous log grasping have been published. In 2021, La Hera et al (2024) demonstrated autonomous forwarding in a simplified outdoor environment in Sweden. In a full-scale, log-loading test-bed in Canada, Ayoub et al (2023) used a method where logs are identified and then replicated in a virtual environment, with grasp planning carried out using a convolutional neural network and a virtual depth camera, followed by loading in the real world.…”

Automatic work-element detection: the missing piece in developing intelligent coaching systems for cut-to-length logging machinery

“…An additional dimension to this discussion arises from the shift in resources towards the development of the next generation of logging systems, including remote-controlled and autonomous forest machines (Lundbäck et al 2022(Lundbäck et al , 2023La Hera et al 2024). If these new systems gradually replace the existing machinery, the lifespan of ICS comes into question.…”