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 labour, autonomous machines have the potential to increase productivity and reduce labour 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.

Assessing the Potential of Onboard LiDAR-Based Application to Detect the Quality of Tree Stems in Cut-to-Length (CTL) Harvesting Operations

“…The challenges of using MLS in forests include dealing with complex environments that affect signal quality, handling large data volumes, maintaining accuracy in adverse conditions, detecting minor or internal defects, integrating with harvesting equipment, and the costs and maintenance of advanced systems. Despite these challenges, the advantages of MLS, such as enhancing tree inventory management, timber value assessment, harvesting planning, safety, environmental impact assessment, and providing data for sustainable management, demonstrate its potential to improve efficiency, accuracy, and sustainability in forest operations [11][12][13]22,29,30].…”

“…The current state of automation in the forestry sector, as noted, is nascent with substantial room for growth. The partial automation of forest machinery, as explored in research [29,30], suggests a promising direction for reducing operator workload and enhancing operational efficiency. This gradual integration of automation, coupled with the strategic use of technologies like LiDAR, as highlighted in [31], has the potential to revolutionize forest management by minimizing waste, optimizing resource use, and improving financial outcomes for the forest industries.…”

“…The acceptable limit for crookedness is defined as a maximum of 1 cm deviation per meter [32,33]. For curved growth, characterized by localized bending on part of the stem, the cutting strategy involves removing sections with a curvature exceeding 1 cm over a 1-m length [28,29]. The assessment of crookedness involves measuring the maximum deviation of the log's centerline from a straight line connecting the top and bottom centers.…”

Autoplant—Autonomous Site Preparation and Tree Planting for a Sustainable Bioeconomy

“…The primary tool for exploring forestry within Autoplant is a heavy-duty unmanned forest machine platform [32,33] (Figure 5), developed by The Machine Design Group at Luleå University of Technology, affiliated with the Arctic Off-Road Robotics Laboratory (AORO). The objective behind creating this unmanned forest machine is to test various robotic software solutions aimed at achieving fully automated forest operations.…”

Multi-Log Grasping Using Reinforcement Learning and Virtual Visual Servoing

“…There are, however, promising experiments in which segmentation has been used to identify grasp poses. La Hera et al [18] shows sparks of early autonomous forwarding in practice, picking single logs along a path on flat ground in concept machine experiments. Ayoub et al [19] developed a grasp planning algorithm which was successfully tested on a physical crane to grasp single or multiple logs on flat ground.…”